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Multiscale characteristics of the emerging global cryptocurrency market
Abstract
Modern financial markets are characterized by a rapid flow of information, a vast number of participants having diversified investment horizons, and multiple feedback mechanisms, which collectively lead to the emergence of complex phenomena, for example speculative bubbles or crashes. As such, they are considered as one of the most complex systems known. Numerous studies have illuminated stylized facts, also called complexity characteristics, which are observed across the vast majority of financial markets. These include the so-called “fat tails” of the returns distribution, volatility clustering, the “long memory”, strong stochasticity alongside non-linear correlations, persistence, and the effects resembling fractality and even multifractality.
The striking development of the cryptocurrency market over the last few years – from being entirely peripheral to capitalizing at the level of an intermediate-size stock exchange – provides a unique opportunity to observe its evolution in a short period. The availability of high-frequency data allows conducting advanced statistical analysis of fluctuations on cryptocurrency exchanges right from their birth up to the present day. This opens a window that allows quantifying the evolutionary changes in the complexity characteristics which accompany market emergence and maturation. The purpose of the present review, then, is to examine the properties of the cryptocurrency market and the associated phenomena. The aim is to clarify to what extent, after such an impetuous development, the characteristics of the complexity of exchange rates on the cryptocurrency market have become similar to traditional and mature markets, such as stocks, bonds, commodities or currencies.
The review introduces the history of cryptocurrencies, offering a description of the blockchain technology behind them. Differences between cryptocurrencies and the exchanges on which they are traded have been consistently shown. The central part of the review surveys the analysis of cryptocurrency price changes on various platforms. The statistical properties of the fluctuations in the cryptocurrency market have been compared to the traditional markets. With the help of the latest statistical physics methods, namely, the multifractal cross-correlation analysis and the q-dependent detrended cross-correlation coefficient, the non-linear correlations and multiscale characteristics of the cryptocurrency market are analyzed. In the last part of this paper, through applying matrix and network formalisms, the co-evolution of the correlation structure among the 100 cryptocurrencies having the largest capitalization is retraced. The detailed topology of cryptocurrency network on the Binance platform from bitcoin perspective is also considered. Finally, an interesting observation on the Covid-19 pandemic impact on the cryptocurrency market is presented and discussed: recently we have witnessed a “phase transition” of the cryptocurrencies from being a hedge opportunity for the investors fleeing the traditional markets to become a part of the global market that is substantially coupled to the traditional financial instruments like the currencies, stocks, and commodities.
The main contribution is an extensive demonstration that, fueled by the increased transaction frequency, turnover, and the number of participants, structural self-organization in the cryptocurrency markets has caused the same to attain complexity characteristics that are nearly indistinguishable from the Forex market at the level of individual time-series. However, the cross-correlations between the exchange rates on cryptocurrency platforms differ from it. The cryptocurrency market is less synchronized and the information flows more slowly, which results in more frequent arbitrage opportunities. The methodology used in the review allows the latter to be detected, and lead–lag relationships to be discovered. Hypothetically, the methods for describing correlations and hierarchical relationships between exchange rates presented in this review could be used to construct investment portfolios and reduce exposure to risk. A new investment asset class appears to be dawning, wherein the bitcoin assumes the role of the natural base currency to trade.
... One can notice that the time series for V ∆t and N ∆t are more persistent (a steeper ascent of F XY q=2 (s)) than the time series for r ∆t (a milder ascent). Encouraged by the values included in Fig. 2 and our previous results on the price returns in the pre-Covid-19 era [45], which proved to be fractally cross-correlated, we now study the detrended cross-correlations between the time series representing BTC/USDT and ETH/USDT cross-rates. Fig. 4 (top) shows the bivariate fluctuation functions obtained for three particular time series arrangements. ...
... This result is somehow expected, because contemporary markets operate at scales that are much shorter than 1 minute. However, due to the long-range temporal autocorrelation in volatility lasting up to a few trading days [45] even if we shift one time series with respect to the other by 1 min, the fractal structure of the detrended cross-correlations can still be observed for these time series. We apply the same formalism to the volume traded V ∆t (t) and plot the corresponding quantities in Fig. 5. ...
... Recently, the military escalation in Ukraine has also been exerting a heavy impact on the markets, including the cryptocurrency market, leading them to suffer from extra draw-downs that added momentum to the bear market dominating the scene since December 2021. From the cryptocurrency perspective, another interesting structural phenomenon is an emergent, strong permanent coupling of the cryptocurrency market and the stock market [9,[44][45][46][47]] -a phenomenon that prior to the pandemics used to be observed only occasionally [8,[48][49][50][51][52][53]. ...
Unlike price fluctuations, the temporal structure of cryptocurrency trading has seldom been a subject of systematic study. In order to fill this gap, we analyse detrended correlations of the price returns, the average number of trades in time unit, and the traded volume based on high-frequency data representing two major cryptocurrencies: bitcoin and ether. We apply the multifractal detrended cross-correlation analysis, which is considered the most reliable method for identifying nonlinear correlations in time series. We find that all the quantities considered in our study show an unambiguous multifractal structure from both the univariate (auto-correlation) and bivariate (cross-correlation) perspectives. We looked at the bitcoin--ether cross-correlations in simultaneously recorded signals, as well as in time-lagged signals, in which a time series for one of the cryptocurrencies is shifted with respect to the other. Such a shift suppresses the cross-correlations partially for short time scales, but does not remove them completely. We did not observe any qualitative asymmetry in the results for the two choices of a leading asset. The cross-correlations for the simultaneous and lagged time series became the same in magnitude for the sufficiently long scales.
... In particular, the microstructure of the market [22], the use of machine learning in trading [23], the role of Binance exchange in volatility transmission [24], and the impact of BTC future contracts on the market [25] were studied. Exchange rates on CEX were analyzed from many perspectives [26], including noise occurrence [27], optimal trading strategies [28], and portfolio construction [29,30]. A multifractal detrended fluctuation analysis (MFDFA) was also applied to study BTC [31][32][33][34] and ETH [35][36][37][38] price changes on CEX exchanges. ...
... Such a left-sided asymmetry in the singularity spectrum is commonly seen in financial time series, where small fluctuations often represent noise, while medium and large ones convey meaningful information [77,95,124]. For log returns ( Figure 6, left side), the most developed multifractal spectrum is observed for time series from Binance, which confirms previous findings [26,99]. In the case of Uniswap v3 log returns, only the left arm of the spectrum is present, and it is shorter than that of Binance, indicating that only large fluctuations exhibit multifractality. ...
... The smaller differences between Binance and Uniswap in the multifractal characteristics of volume may stem from the fact that, as shown in the basic statistics in Table 1, Uniswap has a higher transaction volume per trade, despite its slower trading if compared to Binance. To assess the statistical significance of the observed multifractal effects in the analyzed time series, two types of surrogate time series were generated: the Fourier surrogates [126] For log returns ( Figure 6, left side), the most developed multifractal spectrum is observed for time series from Binance, which confirms previous findings [26,98]. In the case of Uniswap v3 log returns, only the left arm of the spectrum is present, and it is shorter than that of Binance, indicating that only large fluctuations exhibit multifractality. ...
Multifractality is a concept that helps compactly grasp the most essential features of financial dynamics. In its fully developed form, this concept applies to essentially all mature financial markets and even to more liquid cryptocurrencies traded on centralized exchanges. A new element that adds complexity to cryptocurrency markets is the possibility of decentralized trading. Based on the extracted tick-by-tick transaction data from the Universal Router contract of the Uniswap decentralized exchange, from 6 June 2023 to 30 June 2024, the present study using multifractal detrended fluctuation analysis (MFDFA) shows that even though liquidity on these new exchanges is still much lower compared to centralized exchanges, convincing traces of multifractality are already emerging in this new trading as well. The resulting multifractal spectra are, however, strongly left-side asymmetric, which indicates that this multifractality comes primarily from large fluctuations, and small ones are more of the uncorrelated noise type. What is particularly interesting here is the fact that multifractality is more developed for time series representing transaction volumes than rates of return. On the level of these larger events, a trace of multifractal cross-correlations between the two characteristics is also observed.
... Bitcoin's primary goal was to establish a system for conducting internet transactions globally without a central authority, a breakthrough that was first actualized with the establishment of Mt. Gox in February 2011, the first BTC-to-fiat currency exchange platform (Wątorek et al., 2021). Blockchain technology application, the underlying infrastructure of Bitcoin, has since been recognized for its potential to enhance security and privacy across various domains, including the Internet of Things (IoT) ecosystem (Miraz and Ali, 2020). ...
... Blockchains are data files containing records of past transactions and the creation of new blocks, forming a continuous chain where each block builds upon the previous one (Wątorek et al., 2021). This structure underpins Distributed Ledger Technology (DLT), which combines cryptographic techniques with consensus protocols like Proof of Work (PoW) or Proof of Stake (PoS) to maintain an immutable ledger (Lisi et al., 2021). ...
... Mining, the process that sustains blockchain functionality, involves solving complex puzzles to validate transactions, for which miners are rewarded, typically in cryptocurrencies. This mechanism ensures transparency and provides proof of work, fostering confidence among participants in the blockchain network (Wątorek et al., 2021). Yau and Wong (2021) illustrated the transformative potential of integrating gamification with blockchain technology in enhancing financial literacy education. ...
This study explores the mediating effect of financial literacy on the relationship between blockchain technology application and financial risk among financial institution professionals in Ghana. Utilizing a correlational research design, data were collected from a sample of 336 professionals through a self-constructed Likert-scale questionnaire. The analysis was conducted using path analysis and mediation techniques. The findings reveal that financial literacy had a significant partial mediating effect between blockchain technology application and financial risk, accounting for 26.0% of the total effect. The direct negative effect of blockchain technology applications on financial risk also remained significant, highlighting the independent contribution of blockchain technology applications to reducing financial risk. These results underscore the importance of financial literacy in enhancing the effectiveness of blockchain technology applications in mitigating financial risks. The study emphasizes the need for comprehensive financial literacy programs to maximize the benefits of technological advancements in the financial sector. Empirical evidence from related literature supports the findings, indicating that higher financial literacy enables better utilization of blockchain technology applications, thereby reducing financial risk. The research provides insights for policymakers aiming to improve financial literacy and leverage blockchain technology applications for financial risk management.
... For example, Halaburda and Gandal 9 and Goldstein 18 examined triangular arbitrage opportunities between cryptocurrencies and simple arbitrage between crypto asset trading platforms, mainly on BTC LTC-LiteCoin, PPC-PeerCoin, Namecoin (NMC), Feathercoin (FTC), Novacoin (NVC), and Terracoin (TRC). Triangular arbitrage opportunities on Binance and Kraken were reported 19 . However, these works evaluate their proposed methods using simulations based on token price history but not actual trades. ...
... The results from the cross-exchange arbitrage are shown in Figure 6. We invested using the CRV token and earned up to 0.054% PNL during August [14][15][16][17][18][19]2022. The average number of hops ranged from approximately 3 to 4 hops, which was similar to the average number of hops on DEX (see Figure 3), depending on the f ee gas . ...
The emergence of decentralized finance (DeFi) allows arbitrageurs to obtain risk-free income from price gaps of cryptocurrency tokens in many global markets. Several automated arbitrage techniques have been invented to profit from single or multiple platforms, including Centralized and Decentralized Exchange (CEX and DEX), triangular, and DEX-Fait. This paper proposes the arbitrage strategy of cross-cryptocurrency exchanges (ASCEX), a novel automated arbitrage strategy for CEX-DEX platforms, to maximize profit and loss (PNL) using a token route searching algorithm. Based on feature comparison, ASCEX outperforms the existing trading strategies available. Our actual trade experiment shows that ASCEX can generate up to 0.95% monthly risk-free profit compared to 0.34% trading on DEX alone.
... For log-returns (Fig. 6, left side), the most developed multifractal spectrum is observed for time series from Binance, which confirms previous findings [26,98]. In the case of Uniswap v3 log-returns, only the left arm of the spectrum is present and it is shorter than that of Binance, indicating that only large fluctuations exhibit multifractality. ...
... For q = 2 and s = 10, the coefficient is significantly lower for Uniswap (ρ(q, s) < 0.1) compared to Binance (ρ(q, s) = 0.52 in the case of ETH/USDT and ρ(q, s) = 0.37 in the case of ETH/USDC), particularly at small time scales. In both exchanges, ρ(q, s) increases with time scale s, which is in agreement with the related phenomenon observed in the traditional financial markets [26,134,135]. However, this increase occurs more rapidly in the time series from Uniswap as ρ(q, s) for Binance plateau around s = 400, reaching above 0.8. ...
Multifractality is a concept that helps compactly grasping the most essential features of the financial dynamics. In its fully developed form, this concept applies to essentially all mature financial markets and even to more liquid cryptocurrencies traded on the centralized exchanges. A new element that adds complexity to cryptocurrency markets is the possibility of decentralized trading. Based on the extracted tick-by-tick transaction data from the Universal Router contract of the Uniswap decentralized exchange, from June 6, 2023, to June 30, 2024, the present study using Multifractal Detrended Fluctuation Analysis (MFDFA) shows that even though liquidity on these new exchanges is still much lower compared to centralized exchanges convincing traces of multifractality are already emerging on this new trading as well. The resulting multifractal spectra are however strongly left-side asymmetric which indicates that this multifractality comes primarily from large fluctuations and small ones are more of the uncorrelated noise type. What is particularly interesting here is the fact that multifractality is more developed for time series representing transaction volumes than rates of return. On the level of these larger events a trace of multifractal cross-correlations between the two characteristics is also observed.
... Raza et al. [70] also discovered a positive relationship between cryptocurrencies and forex markets, particularly for Bitcoin, for all taken quantiles. Moreover, recent studies during the COVID-19 pandemic have identified the relationship between cryptocurrencies and traditional financial markets [71,72], raising concerns about potential compromise of its decentralized nature. Therefore, it is worthwhile examining if the cryptocurrency market resembles traditional currency markets by studying the jumps that occur during extreme events. ...
... Furthermore, Dashcoin is the only currency that focuses on privacy through masternodes to ensure the high anonymity of a trader. This, however, attracts illicit activities, as highlighted by Wątorek et al. [72]. Therefore, the frequent and rapid price and volume fluctuations and special privacy features might contribute to the higher multifractal patterns in the jumps of Dashcoin. ...
Jump dynamics in financial markets exhibit significant complexity, often resulting in increased probabilities of subsequent jumps, akin to earthquake aftershocks. This study aims to understand these complexities within a multifractal framework. To do this, we employed the high-frequency intraday data from six major cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dashcoin, EOS, and Ripple) and six major forex markets (Euro, British pound, Canadian dollar, Australian dollar, Swiss franc, and Japanese yen) between 4 August 2019 and 4 October 2023, at 5 min intervals. We began by extracting daily jumps from realized volatility using a MinRV-based approach and then applying Multifractal Detrended Fluctuation Analysis (MFDFA) to those jumps to explore their multifractal characteristics. The results of the MFDFA—especially the fluctuation function, the varying Hurst exponent, and the Renyi exponent—confirm that all of these jump series exhibit significant multifractal properties. However, the range of the Hurst exponent values indicates that Dashcoin has the highest and Litecoin has the lowest multifractal strength. Moreover, all of the jump series show significant persistent behavior and a positive autocorrelation, indicating a higher probability of a positive/negative jump being followed by another positive/negative jump. Additionally, the findings of rolling-window MFDFA with a window length of 250 days reveal persistent behavior most of the time. These findings are useful for market participants, investors, and policymakers in developing portfolio diversification strategies and making important investment decisions, and they could enhance market efficiency and stability.
... The cryptocurrency market has experienced remarkable and rapid expansion, largely attributed to the decentralized nature of blockchain-based cryptocurrencies [1][2][3]. However, this unprecedented growth has unfortunately been accompanied by significant challenges, most prominently a substantial surge in scam activities. ...
The surge in cryptocurrencies has been accompanied by a significant rise in scams, underscoring the critical need for precise scam detection. Cryptocurrency markets and transaction networks are dynamic, leading to evolving scam tactics and transaction topologies that challenge detection efforts. Compounding this, scammers cleverly mimic benign transactions, blurring the lines between legitimate and illicit activities. To address these intertwined challenges, we introduce the Triplet-style Dynamic Graph Convolutional Network (TD-GCN). TD-GCN is specifically engineered to model the dynamic nature of cryptocurrency transaction networks and to disentangle subtle distinctions between scam and benign transaction patterns. It leverages a Transformer Encoder for dynamic weight updates, effectively capturing network evolution, and employs Triplet Learning to disentangle representations of similar-appearing scam and benign transactions. Evaluations on a real-world Bitcoin transaction network demonstrate TD-GCN's superior scam detection performance. Notably, TD-GCN achieves a leading F1-macro score of 0.8855 and a 10.36%p precision increase over competing models, crucial for minimizing false positives. Ablation studies highlight the Triplet Dynamic Disentangle component's key role, reducing false positives by 81.1%. TD-GCN’s performance gains stem from its dynamic updates for evolving networks and Triplet Learning for disentangling subtly different patterns. These features enable TD-GCN to significantly bolster cryptocurrency security by effectively detecting scams and minimizing false positives in dynamic transaction networks.
... These unidirectional effects suggest that cryptocurrencies, while still exhibiting low long-term correlation with traditional indices (Alfieri et al., 2019), integrate more tightly during crises (Matkovskyy & Jalan, 2019). This trend is reinforced by Wątorek et al. (2021), who document a growing synchronization between crypto assets and global financial instruments, challenging the narrative of digital assets as hedging vehicles. ...
This study explores the macroeconomic factors driving cryptocurrency price fluctuations, focusing on Bitcoin and Ethereum using the Random Forest machine learning model. It analyzes daily data from 2015 to 2025, incorporating key economic and financial indicators such as the S&P 500, NASDAQ, Treasury yield spreads, Federal Funds Rate, long-term Treasury rates, inflation, Brent oil prices, major exchange rates, and the Volatility Index. Separate predictive models for Bitcoin and Ethereum achieved high accuracy (R² = 0.999 and R² = 0.995, respectively), demonstrating the model's strong forecasting capability. The findings reveal that cryptocurrencies are increasingly influenced by traditional financial markets, particularly US stock indices, highlighting their integration into the global economic system. Bitcoin emerged as relatively stable and less sensitive to monetary policy shifts, functioning as a speculative hedge. In contrast, Ethereum showed greater sensitivity to liquidity and interest rate variables due to its linkage with decentralized finance applications. The study concludes that machine learning methods, combined with traditional macroeconomic indicators, can effectively explain and predict cryptocurrency market behavior, offering a foundation for developing new hybrid economic models tailored to the unique nature of digital assets.
... These applications have showcased the potential of blockchain to disrupt traditional financial systems, influencing how blockchain solutions are considered for non-cryptocurrency domains, including enterprise salary management. A detailed analysis of the cryptocurrency market, its volatility, and its underlying blockchain mechanisms is presented in [13], highlighting blockchain technology's broader economic and technological significance. ...
... Economic and financial research over the past decade has demonstrated the effectiveness and efficiency of wavelet analysis. The research proves that there are multi-scale features in the stock markets [46,47], the cryptocurrency markets [48], and the interactions between stock markets and commodity markets [49][50][51]. The wavelet method is always combined with the traditional GARCH model to study the multi-scale spillovers between time series. ...
To examine the spillover of tail-risk information across global stock markets, we select nine major stock markets for the period spanning from June 2014 to May 2024 as the sample data. First, we employ effective Rényi transfer entropy to measure the tail-risk information spillover. Second, we construct a Diebold–Yilmaz connectedness table to explore the overall characteristics of tail-risk information spillover across the global stock markets. Third, we integrate wavelet analysis with effective Rényi transfer entropy to assess the multi-scale characteristics of the information spillover. Our findings lead to several key conclusions: (1) US and European stock markets are the primary sources of tail-risk information spillover, while Asian stock markets predominantly act as net information receivers; (2) the intensity of tail-risk information spillover is most pronounced between markets at the medium-high trading frequency, and as trading frequency decreases, information spillover becomes more complex; (3) across all trading frequencies, the US stock market emerges as the most influential, while the Japanese stock market is the most vulnerable. China’s stock market, in contrast, demonstrates relative independence.
... The advent of cryptocurrencies has revolutionized financial markets, introducing a new asset class characterized by high volatility and round-the-clock trading [1]. This has created unprecedented opportunities for traders but also poses significant challenges due to rapid price fluctuations and market complexities [2]. To tackle these challenges, automated trading systems have become indispensable tools. ...
Trading has seen a profound evolution with the advent of high-frequency trading (HFT) and algorithmic trading, fundamentally changing how trades are executed in global financial markets. These advanced techniques allow traders to execute orders with extraordinary speed and accuracy, exploiting market inefficiencies. This shift is particularly pronounced in cryptocurrency markets, known for their high volatility, 24/7 trading cycles, and frequent liquidity fluctuations. Algorithmic trading within this space offers substantial opportunities but also presents unique challenges for traders and investors. This paper introduces the architecture of an algorithmic trading system specifically designed for cryptocurrency markets. The system systematically analyzes market conditions, selecting cryptocurrency pairs based on volatility, liquidity, and other key metrics, while dynamically updating a blacklist to avoid pairs with unfavorable characteristics. Leveraging multiple server instances, the system trains deep learning models on historical price and volume data for each chosen pair, automatically selecting the most effective model for price prediction. Advanced risk management techniques, including bet sizing algorithms, determine optimal position sizes, stop-loss, and take-profit levels. The system also continuously monitors trading performance and adjusts strategies in real time, enhancing overall trading efficiency and profitability. This framework showcases the potential of algorithmic trading to maximize returns in cryptocurrency markets while managing risk effectively.
... Several studies have examined the volatility of cryptocurrencies compared to traditional assets, with findings indicating that Bitcoin and other digital currencies exhibit higher price fluctuations, making them risky yet potentially lucrative investment vehicles [19], [20]. The emergence of stablecoins, which are pegged to traditional currencies or commodities, has been proposed as a solution to mitigate volatility while maintaining the benefits of digital assets [21]. Another area of research focuses on the role of cryptocurrency as a hedge or safehaven asset during financial crises. ...
This study presents a comprehensive bibliometric analysis of cryptocurrency-related literature indexed in the Scopus database, aiming to map the intellectual landscape, identify key research trends, and highlight influential contributors in the field. Using VOSviewer as the primary analytical tool, the study examines keyword co-occurrence, temporal distribution, author collaboration networks, and international research partnerships. The findings indicate that the central focus of the literature remains on fundamental topics such as cryptocurrency, blockchain, and electronic money, while emerging trends show a growing integration of machine learning, forecasting, and sentiment analysis. Temporal mapping reveals a shift from infrastructure-oriented research to data-driven and interdisciplinary applications. Key authors and countries, including the United States, China, India, and the United Kingdom, play dominant roles in shaping the scholarly conversation. Despite the expanding scope of cryptocurrency research, gaps remain in areas such as sustainability, inclusivity, and ethical implications of AI applications. This study offers valuable insights for researchers, policymakers, and practitioners seeking to understand the evolution and future directions of cryptocurrency research.
... The global cryptocurrency market is valued today at $2.49 Trillion 1 and has slowly pushed towards its maturity since 2014 [2]. It attracts many investors, whose behavior is influenced by social influence or public sentiment [3]. ...
Cryptocurrencies have transformed financial markets with their innovative blockchain technology and volatile price movements, presenting both challenges and opportunities for predictive analytics. Ethereum, being one of the leading cryptocurrencies, has experienced significant market fluctuations, making its price prediction an attractive yet complex problem. This paper presents a comprehensive study on the effectiveness of Large Language Models (LLMs) in predicting Ethereum prices for short-term and few-shot forecasting scenarios. The main challenge in training models for time series analysis is the lack of data. We address this by leveraging a novel approach that adapts existing pre-trained LLMs on natural language or images from billions of tokens to the unique characteristics of Ethereum price time series data. Through thorough experimentation and comparison with traditional and contemporary models, our results demonstrate that selectively freezing certain layers of pre-trained LLMs achieves state-of-the-art performance in this domain. This approach consistently surpasses benchmarks across multiple metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), demonstrating its effectiveness and robustness. Our research not only contributes to the existing body of knowledge on LLMs but also provides practical insights in the cryptocurrency prediction domain. The adaptability of pre-trained LLMs to handle the nature of Ethereum prices suggests a promising direction for future research, potentially including the integration of sentiment analysis to further refine forecasting accuracy.
... This has led to the perception of cryptocurrencies as a potential safe haven and a reliable hedge during extreme events such as the periods of economic and financial crises (Corbet et al., 2018;Manavi et al., 2020;Shahzad et al., 2022). However, some argue that cryptocurrencies are increasingly exhibiting properties similar to traditional markets such as forex, commodities, and equities, particularly in the post-COVID-19 era (Drozdz et al., 2020;Wątorek et al., 2021). This raises concerns about the decentralized and unique nature of cryptocurrencies. ...
Jumps in financial markets play a crucial role in disrupting financial systems, influencing trading behavior, and increasing market complexity. This study aims to quantify how jumps in decentralized markets have cross-correlation properties with centralized financial markets, particularly in multifractal context. We employ high frequency data from six major cryptocurrencies and six major forex markets at 5-minute intervals for the period of Aug 04, 2019-Jul 27, 2024. We first extract daily jumps by using the MinRV-based jump method and employ multifractal detrended cross-correlation analysis (MF-DXA, also known as MF-DCCA) on jumps to understand their interconnected dynamics. The findings indicate all 36 pairs of jumps between cryptocurrencies and forex markets to have significant multifractality in cross-correlation. However, the strength of multifractal cross-correlation varies significantly across all combinations. Notably, all cryptocurrencies exhibit stronger cross-correlations with Japanese Yen, and weaker with Australian Dollar and British Pound. In addition, all the pairs of jumps have persistence behavior in the cross-correlation indicating the likelihood of positive/negative jumps in one market often propagate positive/negative jumps in another market. These findings offer valuable insights for market participants, investors and policymakers in making informed investment decisions, developing risk adjusted trading strategies, and enhancing efficiency in financial markets.
... The cryptocurrency market sees blockchain technology as a revolutionary innovation that enables fast and secure transactions without a central institution, and has great potential for applications beyond finance, as seen on the Ethereum platform (Wątorek et al., 2020). The cryptocurrency market responds efficiently to regulatory changes, with prices generally rising in response to advancements in regulatory frameworks and legal recognition, while bans and restrictions lead to price declines; however, stricter regulations, particularly those related to anti-money laundering and issuance, often result in lower cryptocurrency prices, suggesting that the negative impact on prices may outweigh the benefits of regulation, and there is heterogeneity in price responses to different types of regulations (Shanaev et al., 2020). ...
In the rapidly evolving digital era, technological transformation has become a critical factor in accelerating and streamlining global trade. Blockchain and cryptocurrency have emerged as innovations with significant potential to revolutionize cross-border transactions by offering fast, intermediary-free methods of transaction. Blockchain ensures transparency, security, and efficiency in managing global supply chains and business transactions, while cryptocurrency enables faster and cheaper cross-border payments, eliminating delays and high fees associated with traditional banking systems. The purpose of this study is to explore the role of blockchain and cryptocurrency in reshaping global trade, analyze patterns and trends in the global trade digital transformation literature, and identify the challenges and opportunities faced by these technologies in the context of international trade. This study uses a bibliometric approach to analyze relevant literature, resulting in four main clusters: Green (Global Trade and Supply Chain), Blue (Ethereum Blockchain and Challenge), Red (Market and Currency), and Yellow (Role and Example). The findings suggest that blockchain and cryptocurrency can enhance the efficiency, security, and transparency of international trade; however, challenges such as cryptocurrency price volatility, security issues, and limited technology adoption still need to be addressed to fully realize the potential of these technologies.
... Methodologically, our work is principally inspired by a rich literature of applying statistical and physically-inspired models to capture the dynamics of real-world phenomena. In financial markets, these techniques have been applied to a broad range of asset classes including equities [41][42][43], foreign exchange [44], cryptocurrencies [45][46][47][48][49][50][51], and debt-related instruments [52]. These applied mathematical methods have also been used in a variety of other disciplines including epidemiology [53][54][55][56][57][58][59][60][61][62][63], environmental sciences [64][65][66][67][68][69][70][71], crime [72][73][74], the arts [75,76], and other fields [77][78][79]. ...
This paper studies the time-varying structure of the equity market with respect to market capitalization. First, we analyze the distribution of the 100 largest companies' market capitalizations over time, in terms of inequality, concentration at the top, and overall discrepancies in the distribution between different times. In the next section, we introduce a mathematical framework of linear and nonlinear functionals of time-varying portfolios. We apply this to study the market capitalization exposure and spread of optimal portfolios chosen by a Sharpe optimization procedure. These methods could be more widely used to study various measures of optimal portfolios and measure different aspects of market exposure while holding portfolios selected by an optimization routine that changes over time.
... Concerning the cryptocurrency market dynamics, relevant contributions have enlightened us on various topics in the financial world, like risk, pricing, speculation, volatility, returns, and forecasting. Strictly speaking, some publications analyse the causal relation between trading volume and Bitcoin returns and volatility [12], their behaviour due to uncertainties in the market [13][14][15], the connectivity between cryptocurrencies from the different market caps over time [16,17], patterns of volatility in several cryptocurrency markets [18], errors in cryptocurrency research [19], the impact of speculative behaviour on the pricing [20], and price forecasting [21,22]. Beyond these multiple approaches, the effects of the global crisis are in the scope of recent works, both for the COVID-19 pandemic and the Russia-Ukraine armed conflict [23][24][25][26][27][28][29]. ...
This work analyses the chaos intensities of the ten cryptocurrencies with larger Volumes in American Dollars from time series in reconstructed phase spaces. In the first step, the routine reveals the chaotic dynamics for the prices in Dollars and Euros and the stochastic behaviour of the financial returns by the quantifiers of chaos and diagrams. The collection of the chaos intensities covers periods before, during and after the COVID-19 health public global crisis and the Russia–Ukraine military conflict. It permits the evaluation of changes in the statistics of chaos measured in these critical times. The statistical methods used to analyse the association between cryptocurrencies are the Spearman Rank Correlation and the Two-Sample T-Test for quotations in dollars and euros. The findings include the statistical evidence against the absence of a correlation between several cryptocurrencies, the maximum chaos intensity during the COVID-19 pandemic and the exception, the insignificance of the Russia-Ukraine conflict in the chaos intensities for all prices studied, and p values in favour of equality of the means of chaos measures in Dollar and Euro.
... [1][2][3] From a practical perspective, it is especially important to have reliable tools for quantifying such effects on data sets containing values of the measured observables that are uniformly sampled in time or space. In the former case, several approaches have been developed over the years to extract fractal properties of one-dimensional time series, 4-11 the most widely applied of which is multifractal detrended fluctuation analysis (MFDFA) 7,[12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] due to its relatively good reliability. 30,31 In the latter case, which requires considering at least two-dimensional data arrays, the spectrum of available tools is poorer. ...
An efficient method of exploring the effects of anisotropy in the fractal properties of 2D surfaces and images is proposed. It can be viewed as a direction-sensitive generalization of the multifractal detrended fluctuation analysis into 2D. It is tested on synthetic structures to ensure its effectiveness, with results indicating consistency. The interdisciplinary potential of this method in describing real surfaces and images is demonstrated, revealing previously unknown directional multifractality in data sets from the Martian surface and the Crab Nebula. The multifractal characteristics of Jackson Pollock’s paintings are also analyzed. The results point to their evolution over the time of creation of these works.
... According to Wątorek et al. (2020), there were more than 3,600 cryptocurrencies traded in the cryptocurrency market and the entire market capitalization was 350 billion USD in 2020. But in today (2024) 1 Bitcoin Logo (CoinMarketCap, 2024) Bitcoin is the mother coin employs a consensus network to enable a new payment system and digital money offering features like low processing fees, global accessibility, and fraud control (Bitcoin, 2009;CoinMarketCap, 2024). ...
In the cryptocurrency market prices are highly volatile due to ongoing market issues such as economic crises, technological changes, and high inflation. This study deep dives into the fascinating world of cryptocurrencies by exploring the behaviour and forecasting future trends of top ten cryptocurrencies including Bitcoin, Ethereum, Tether, BNB, Solana, USDC, XRP, Dogecoin, Ton coin, and Cardano. The descriptive analysis provides the past behaviours analysis of the selected cryptocurrencies, which sheds light on the factors that drive often price volatile such as technological break-throughs, regulatory shifts, and macroeconomic fluctuations. For the predic-tive analysis using time series employs ARIMA and ARMAX models to fore-cast future prices, which provides valuable insights into the behaviour of the cryptocurrency price. Our results suggest that the simpler ARIMA model performed better which focuses purely on historical data and often provides more accurate predictions compared to ARMAX model which incorporates external factors like trading volume and market capitalization. This finding emphasizes the cryptocurrency market's complexity and suggests that both historical behaviour and influencing factors must be considered in order to make better predictions. The study lays the groundwork for such efforts, providing valuable insights and pointing the way forward in the quest to comprehend the ever-changing world of cryptocurrencies. Finally, the analysis contributes to various stakeholders, including investors and policymakers, to offer a detailed analysis of the cryptocurrency market, underlying the factors influencing its dynamics.
... From a practical perspective, it is especially important to have reliable tools of quantifying such effects for data sets containing values of the measured observables that are uniformly sampled in time or in space. In the former case, several approaches have been developed over the years to extract fractal properties of 1-dimensional time series [4,5,6,7,8,9,10,11], the most widely applied of which is multifractal detrended fluctuation analysis (MFDFA) [7,12,13,14,15,17,18,19,20,21,22,23,16,24,25,26,27,28,29] due to its relatively good reliability [30,31]. In the latter case, which requires considering at least 2dimensional data arrays, the spectrum of available tools is poorer [32,33,34]. ...
An efficient method of exploring the effects of anisotropy in the fractal properties of 2D surfaces and images is proposed. It can be viewed as a direction-sensitive generalization of the multifractal detrended fluctuation analysis (MFDFA) into 2D. It is tested on synthetic structures to ensure its effectiveness, with results indicating consistency. The interdisciplinary potential of this method in describing real surfaces and images is demonstrated, revealing previously unknown directional multifractality in data sets from the Martian surface and the Crab Nebula. The multifractal characteristics of Jackson Pollock's paintings are also analyzed. The results point to their evolution over the time of creation of these works.
... The drive for this is due to the increasing efforts by researchers and financial institutions to minimize financial risks. Despite this, the predictive accuracy of current models still requires improvement, as shown by various studies on cryptocurrency price modeling that aim to improve forecasting methods for profitable investment decisions ( [1,[50][51][52]). Then, there is a need for more robust evaluation methods that consider the volatility of the Bitcoin market and the importance of real-time predictions. ...
Bitcoin, the first decentralized cryptocurrency, has attracted significant attention from investors and researchers alike due to its volatile and unpredictable price movements. However, predicting the price of Bitcoin remains a challenging task. This paper presents a detailed literature review on previous studies that have attempted to predict the price of Bitcoin. It discusses the main drivers of Bitcoin prices, including its attractiveness, macroeconomic and financial factors with a particular focus on the use of Blockchain information. We apply time series to daily data for the period from 28/04/2013 to 28/01/2023. We used Python and TensorFlow library version 2.11.0 and propose a deep multimodal reinforcement learning policy combining Convolutional Neural Network (CNN) and Long ShortTerm Memory (LSTM) neural network for cryptocurrencies’ prices prediction. Also, this study attempts to predict the price of Bitcoin using a special type of deep neural networks, a Deep Autoencoders. Two results are worth noting: Autoencoders turns out to be the best method of predicting Bitcoin prices, and Bitcoin-specific Blockchain information is the most important variable in predicting Bitcoin prices. This study highlights the potential utility of incorporating Blockchain factors in price prediction models. Also, our findings show that sentiment indicator, Ethereum, XRP and Doge Coin prices, global currency ratio, macroeconomic factors, and Blockchain information of Ethereum did not contribute significantly toward predicting Bitcoin prices. These conclusions provide decision support for investors and a reference for the governments to design better regulatory policies.
Background: Despite the financial technology (Fintech) industry being marked as a strategic development direction in many countries, cryptocurrency products show low adoption rates. Purpose: This study investigated factors affecting consumer trust in cryptocurrency products, particularly exchanges and crypto wallets. Methods: A three-stage multi-method approach was adopted: two non-probability convenience surveys and a systematic literature review. The initial survey (N=45) was followed by literature review (N=16) and a follow-up survey (N=95). Qualitative and quantitative analysis techniques were used. Findings: Trust must be understood as a versatile concept, with consumers perceiving different factors differently when choosing cryptocurrency products. Two key findings emerged: convenience, rather than trust, is the biggest factor attributed to cryptocurrency product popularity and adoption. Second, an inverse relationship exists between trustworthiness and popularity of information sources about cryptocurrency products, with less popular sources being more trusted. Consumers rely on convenience-based attributes like the ease of use and accessibility, which indirectly influence trust perception. Conclusions: Trust degree is not bound to specific products or services but depends on consumer intentions and knowledge, among other factors. Research implications: The authors suggest policy and innovation development directions to increase consumer trust in cryptocurrency products.
This chapter introduces the application scenarios based on CoG-MIN, including industrial internet of things (IIoT), internet of vehicles (IoV), space-terrestrial integrated networks (STIN), digital asset management and trading, and a community with a shared future in cyberspace.
Cryptocurrencies, such as Bitcoin and Ethereum, are not just a new form of money. They represent a potential revolution in the financial system, offering decentralized, secure, and borderless alternatives to traditional currencies. Operating on blockchain technology, they provide a transparent, tamper-proof transaction ledger, eliminating the need for intermediaries and allowing for trustless, global transactions that are immutable and resistant to censorship. As digital money evolves, stablecoins have emerged, offering the stability of fiat currencies while retaining the benefits of cryptocurrencies. Interoperability between blockchain networks is becoming crucial, allowing assets and data to move seamlessly across platforms. However, there are growing concerns about the environmental impact of energy-intensive proof-of-work consensus mechanisms used by some cryptocurrencies, leading to the exploration of more sustainable alternatives like proof-of-stake. Decentralized Finance is another emerging trend, leveraging blockchain to provide financial services without intermediaries, though it faces challenges like regulatory uncertainty and intelligent contract vulnerabilities. The allure of cryptocurrencies lies in their potential to reshape the financial system, reflecting a shift towards a more digital, decentralized, and efficient economy.
Cryptocurrencies have changed the financial industry and have the potential to make a considerable social impact. Since cryptocurrencies are a new phenomenon in the financial market, research on the social impact of cryptocurrencies remains in the initial stage. This chapter aims to present the current research stage on the social impact of cryptocurrencies. The findings from the review of the literature are structured to positive (financial inclusion, provision of access to credit, remittances, empowerment of individuals and communities, and philanthropic initiatives) and negative (high risk, illicit activities, and environmental harm). The chapter shows most of the findings are inconsistent and the conclusions vary. It is argued in the chapter that the negative impact of cryptocurrencies on the social fabric prevails due to the very meaning (nature) of cryptocurrencies.
Contrary to expectations some years ago, the crypto market has matured and gives the impression of an established financial eco-system. Certainly, some deviations from robustness, typically reflected in event related volatility bursts and spikes, are observed, but a liquid derivatives market has been established, at least for the dominant digital assets. It is, therefore, not only necessary for pricing contingent claims to understand the stochastic dynamics via a solid data analysis but also to provide instruments identifying volatility patterns and their dynamic evolvement. Using the Bitcoin as a representative instrument, we ventured to model this particular crypto coin dynamics via a combination of roughness in volatility and jumps in the underlying crypto currency. Findings on the roughness, e.g. the size of the Hurst exponent for the volatility dynamics, revealed remarkable differences when compared to corresponding estimates for equities and fixed income funds. Through a parametric bootstrap, we give evidence that both roughness and jumps are crucial for predicting the range of next-day returns in terms of a simulated confidence interval. By scaling up the jump sizes, we obtained a nicely working combination of volatility roughness and jumps (of the underlying) resulting in precise coverage levels. All calculations may be redone on quantlet.com and courselets are in .
Cryptocurrency has emerged as a revolutionary force in both the financial and technological
spheres, fundamentally changing how we perceive and interact with money. Virtual currency like
cryptocurrency has carved itself a distinct position in the worldwide financial markets,
particularly after its rapid growth and expansion. The implementation of blockchain technology
in the utilisation of cryptocurrency has garnered attention from various entities such as the
banking industry, stakeholders, government, and individual investors. Studies on
cryptocurrencies are still in their infancy and are limited. These digital currencies are expected to
challenge existing financial and regulatory paradigms, offering an alternative means for
economic players to engage in transactions. This chapter offers an extensive exploration of
cryptocurrency, covering its origins, foundational technology, various types, benefits, risks, and
future potential in considerable detail. By the end of this chapter, readers will have a thorough
understanding of the key concepts and intricacies surrounding cryptocurrency.
The many problems inherent in existing blockchains and Consensus-Mechanisms were documented in Nwogugu (2025a;b;c) and other research articles, and they justify the development of new Consensus-Mechanisms. This document introduces a new Consensus Framework for developing different types of efficient blockchains. The approach in this document is based on Game Theory and Complex Systems Theory wherein one Modular Framework for building new Consensus-Mechanisms for AI (and especially for blockchains and DEXes) is developed as a Repeated Large Dynamic Game that consists of a group of modular sub-games that are “Game Strategies” (“Modular-Strategies”). Each Modular-Strategy can be implemented in 1-5 different ways (Modular-Options). The “Intersection” and the “Union” of the Modular-Options are the Optimal Strategy Space and the Overall Strategy Space respectively. Because there are at least ten Modular- Strategies, each of which has at least three different Modular-Options, the Modular Framework can generate at least fifty different types of blockchains, each of which is different from the others by at least two Modular-Options.
The efficiency of Trust-At-Scale remains a contentious issue around the world, and underpins large and growing global markets (industry analysts have forecasted the shift of more than forty trillion worth of stocks, bonds and Real-World-Assets to blockchains during 2025-2040 – eg. trading, documentation, transaction-processing, custody; etc.). As of 2025, PoW (Proof-of-Work), PoS/DPoS (Proof-of-Stake and Delegated Proof-of-Stake), RPCA (Ripple Protocol Consensus Algorithm) and PoR (Proof-of-Reputation) were the most popular and dominant Consensus systems in terms of completed transaction-volumes, pending-orders and TVL. This article: i) critiques PoW, PoS, RPCA and PoR and explains the economic, technological/operational, HCI (Human Computer Interaction), Antitrust/Competition Psycho-Technological problems inherent in these Consensus Mechanisms; ii) analyzes these Consensus Mechanisms as “Coordination Mechanisms” and “Belief Networks”; iii) explains common and popular misconceptions and misinformation about these Consensus-Mechanisms; iv) critiques several relevant academic journal articles that analyzed PoW, PoR and PoS/DPoS, and developed mathematical models; v) explains the inherent and “Emergent” Financial Stability risks and Systemic Risks of these Consensus Mechanisms.
The debate about the relative-values, volatility and Contagion of Altcoins (versus Bitcoin, Ether, stocks and bonds)
continues to intensify, and is made more complex by changing regulatory/enforcement approaches of the Obama,
Biden and Trump administrations in the US, geopolitical trends, the imposition/removal of Economic Sanctions on
nations and Trade Wars. This article:
i) explains how/why International Contagion and financial management of Altcoins is related to, and can affect
HCI in blockchains/DEXes in terms of user experience, users’ risk-perception, users’ valuations, systems
design, security, “Big-Trust” (Trust-At-Scale), Consensus systems; etc..
ii) explains how the undervaluation of Altcoins is related to, and can affect HCI in blockchains/DEXes in terms
of user experience, users’ risk-perception, users’ valuations, systems design, security, “Big-Trust” (Trust-At
Scale), Consensus systems; etc..
iii) introduces simple tests/methods (including index-based tests) for measuring whether Altcoins are
undervalued relative to bitcoin, stocks, commodities, and bonds; and explains the evidence that indicates or
may indicate that many Altcoins are significantly undervalued relative to bitcoin, stocks and bonds.
iv) explains the problems inherent in using Indices and Price/Book ratios for Relative Value Analysis.
v) introduces the “Berkshire Hathaway Effect”, the “Apple Effect” and the “Modified Berkshire/Apple Effect”
and the “Low BTC NAV Discount” phenomenon, and presents evidence.
vi) Explains some of the possible effects of the trend of Hybrid Real-World Crypto Companies and Crypto
Investment Companies and implications for International Financial Contagion.
vii) Explains how some CryptoTokens can interact with Corporate Finance and Corporate Strategy.
Herding behavior has become a familiar phenomenon to investors, with potential dangers of both undervaluing and overvaluing assets, while also threatening market stability. This study contributes to the literature on herding behavior by using a recent dataset, covering the most impactful events of recent years. To our knowledge, this is the first study examining herding behavior across three different types of investment vehicle and also the first study observing herding at a community (subset) level. Specifically, we first explore this phenomenon in each separate type of investment vehicle, namely stocks, US ETFs and cryptocurrencies, using the Cross-Sectional Absolute Deviation model. We find mostly similar herding patterns for stocks and US ETFs. Subsequently, the same experiment is implemented on a combination of all three investment vehicles. For a deeper investigation, we adopt graph-based techniques including the Minimum Spanning Tree and Louvain community detection to partition the combination into smaller subsets to detect herding behavior for each subset. We find that herding behavior exists at all times across all types of investment vehicle at a subset level, although perhaps not at the superset level, and that this herding behavior tends to stem from specific events that solely impact that subset of assets. Lastly, we explore herding by examining the financial contagion effects between these types of investment vehicle. Results show that US ETFs not only have a tendency to propagate similar trading behaviors in stocks and especially cryptocurrencies but also show self-reinforcing herding behavior, acting as drivers of their own trends.
Cryptocurrency, emerging post-recession, has the potential to reshape the financial landscape. Since Bitcoin's debut in 2009, cryptocurrencies have evolved into advanced assets using blockchain technology. These decentralized digital currencies stand out from traditional money by expanding banking access, cutting transaction costs, and enhancing security. Beyond technology, they shift trust and control in finance away from centralized entities like banks and governments, leveraging blockchain and distributed systems to boost efficiency and promote financial inclusion, especially in developing countries.
Purpose
The study aimed to determine the static return connectedness between Brazil, Russia, India, China and South Africa (BRICS) equity markets and crypto assets.
Design/methodology/approach
The study employs the time-varying parameter vector autoregression (TVP-VAR) method to examine the static and dynamic connectedness between crypto assets and the BRICS stock market. The study sample size was segmented into full sample, pre-COVID-19 and post-COVID-19 for in-depth analysis.
Findings
Empirical findings pointed out the significant rise in the total connectedness between both markets in the pre-COVID-19 period. Our result also exhibits a lower level of connectedness during the post-COVID-19 period. During the full sample period, it was found that cryptocurrencies and Indian, Chinese and South African stock markets remained key return transmitters, while Russian and Brazilian stock markets were seen as recipients. Moreover, during the pre-COVID period, cryptocurrencies played the role of return transmitter while the stock markets in BRICS remained recipients of return spillover.
Practical implications
This study contains practical insights for investors and portfolio managers in diversifying their portfolios considering the aforementioned connectivity of both markets, especially during periods of instability.
Originality/value
The study highlighted the importance of the TVP-VAR method in analyzing the static and dynamic connectedness of returns between cryptocurrencies and BRICS stock markets in different periods, including pre- and post-COVID-19. It further pragmatized the dynamic roles of cryptocurrencies as transmitters of returns and the BRICS stock markets as receivers where investors and policymakers can navigate market uncertainties.
The aim of this research is to raise awareness regarding cryptocurrency fraud. In this context, the study focuses on cryptocurrency investment frauds and provides an evaluation from the perspective of new media literacy. Throughout the research process, a total of 969 complaints were analyzed under the categories of "Cryptocurrency Investment Fraud" and "Cryptocurrency and Victim Complaints" on the Şikayetvar platform. Adopting an exploratory approach, the complaints were coded under various themes using content and thematic analysis methods. The analysis process was conducted using MAXQDA 24, a qualitative data analysis software. The findings reveal that the theme with the highest frequency among types of fraud is "Fake Coin/Token" (337), illustrating the strategies employed by cryptocurrency fraudsters to deceive investors through fraudulent projects and assets. Additionally, the theme "Withdrawal and Transaction Request Rejection" (159) reflects the difficulties faced by users in conducting transactions and withdrawing their funds on legitimate platforms, showcasing how fraudulent platforms delay their victims. Furthermore, scams conducted through Telegram channels (173) have garnered attention, highlighting the significant role social media platforms play in fraudulent activities. Cryptocurrency frauds underscore the deficiencies in users' new media literacy and emphasize the importance of financial literacy and new media literacy education in an environment where fraud is prevalent.
Modern çağın en önemli yeniliklerinden biri paranın dijitalleşmesidir. Günümüzde birçok dijital para birimi bulunmaktadır ve işlem hacmi ile piyasa değeri açısından Bitcoin öne çıkmaktadır. Bu çalışmada, kripto para birimleri içinde önemli bir yere sahip olan Bitcoin'in çevre ile ilişkisi incelenmektedir. 2010 Ağustos – 2024 Mart dönemine ait aylık veriler kullanılarak, Bitcoin ile karbon emisyonu arasındaki ilişki ampirik olarak test edilmiştir. Ampirik analizde öncelikle durağanlık mertebesini belirlemek amacıyla geleneksel ve güncel ampirik metotlardan yararlanılmaktadır. Diğer yandan geleneksel eşbütünleşme yaklaşımlarının yanında doğrusal olmayan eşbütünleşme testleri içerisinde güncel testler arasında yer alan Hepsağ (2021) eşbütünleşme testi kullanılmaktadır. Son olarak değişkenler arasındaki nedensellik ilişkisi incelenmiştir. Ampirik bulgular, Bitcoin ile karbon emisyonları arasında uzun dönemli bir ilişki olduğunu göstermektedir. Ayrıca, Bitcoin'den karbon emisyonlarına doğru Granger nedensellik olduğu sonucuna ulaşılmıştır. Elde edilen sonuçlar, Bitcoin üretiminde çevresel kaliteyi artırıcı önlemler alınmasının gerekliliğini ortaya koymaktadır.
Purpose
This study delves into Bitcoin’s return dynamics to address its pronounced volatility, particularly in extreme market conditions. We analyze a broad range of explanatory variables, including traditional financial indicators, innovative cryptocurrency-specific metrics and market sentiment gauges. We uniquely introduce the Conference Board Leading Economic Indicator (LEI) to the cryptocurrency research landscape.
Design/methodology/approach
We employ quantile regression to examine Bitcoin’s daily and monthly returns. This approach captures timescale dependencies and evaluates the consistency of our findings across different market conditions. By conducting a thorough analysis of the entire return distribution, we aim to reveal how various factors influence Bitcoin’s behavior at different risk levels. The research incorporates a comprehensive set of explanatory variables to provide a holistic view of Bitcoin’s market dynamics. Additionally, by segmenting the study period, we assess the consistency of the results across diverse market regimes.
Findings
Our results reveal that factors driving Bitcoin returns vary significantly across market conditions. For instance, during downturns, an increase in transaction volume is linked to lower Bitcoin returns, potentially indicating panic selling. When the market stabilizes, a positive correlation emerges, suggesting healthier ecosystem activity. Active addresses emerge as a key predictor of returns, especially during bearish phases, and sentiment indicators such as Wikipedia views reveal shifting investor optimism, depending on market trends. Monthly return analysis suggests Bitcoin might act as a hedge against traditional markets due to its negative correlation with the S&P 500 during normal conditions.
Practical implications
The study’s findings have significant implications for investors and policymakers. Understanding how different factors influence Bitcoin returns in varying market conditions can guide investment strategies and regulatory approaches.
Originality/value
A novel contribution of this study is the identification of Bitcoin’s sensitivity to broader economic downturns as demonstrated by the negative correlation between LEI and returns. These insights not only deepen our understanding of Bitcoin market behaviour but also offer practical implications for investors, risk managers and policymakers navigating the evolving cryptocurrency landscape.
This article explores the transformative impact of cryptocurrency on finance and society, analyzing its underlying technological principles, applications, and the regulatory challenges it faces. Cryptocurrency, underpinned by blockchain technology, introduces a decentralized model of financial transactions that bypasses traditional banking institutions, offering enhanced security, transparency, and accessibility. Through decentralized finance (DeFi), smart contracts, and tokenization, cryptocurrency extends beyond simple monetary exchange, enabling a diverse ecosystem of financial products and digital ownership frameworks. However, its widespread adoption is constrained by regulatory ambiguities, market volatility, and environmental concerns related to energy-intensive consensus mechanisms. The future of cryptocurrency depends on advancements in blockchain technology, particularly in scalability and sustainability, and the establishment of cohesive regulatory frameworks. As it integrates further into digital economies and the vision of Web 3.0, cryptocurrency has the potential to redefine financial and societal norms, positioning itself as a foundational component of the next-generation digital infrastructure.
We explore patterns, regularities, and correlations in the evolving landscape of Ethereum-based tokens, both ERC-20 (fungible) and ERC-721 (non-fungible) to understand the factors contributing to the rise in certain tokens over others. By applying network science methodologies, minimum spanning trees, econometric autoregressive–moving-average (ARMA) models, and the study of accumulation processes, we are able to highlight a rising centralisation process. Not only do “rich” tokens get richer, but past transactions also emerge as more reliable predictors of new transactions. Our findings are validated across different samples of tokens.
Abstract
The financial and economic stability of the business world is greatly damaged by a number of crises over the years. These crises also change and affect stock markets around the globe. This study examines the nonlinear behavior of well-known stock markets in the US, Europe, Canada, and Asia (S&P 500, Nikkei 225, CAC40, DAX30, Bombay Stock Index, Hang Seng Index, and Canadian Stock Index). We also investigate the commodity indexes (Gold, Bitcoin, Brent Crude Oil, and West Texas Crude Oil). The transmission of returns and the volatility propagation pattern across stock-commodity markets during the COVID-19 era is analyzed using the VAR DCC MEGARCH model. The results demonstrate that correlations intensify and become more complicated during the COVID-19 period, especially between financial market indices and oil commodity indices. However, the volatility spillover statistics during the pandemic period show a definite rise in the uncertainty of financial market indices due to index instability. The analysis also shows that financial markets experience a substantial leverage effect during the pandemic crisis. According to the research, Bitcoin is crucial for reducing portfolio risk during the health crisis, and gold proves to be the best option for investors to diversify their financial risk.
The cryptocurrency market offers attractive but risky investment opportunities, characterized by rapid growth, extreme volatility, and uncertainty. Traditional risk management models, which rely on probabilistic assumptions and historical data, often fail to capture the market’s unique dynamics and unpredictability. In response to these challenges, this paper introduces a novel portfolio optimization model tailored for the cryptocurrency market, leveraging a credibilistic CVaR framework. CVaR was chosen as the primary risk measure because it is a downside risk measure that focuses on extreme losses, making it particularly effective in managing the heightened risk of significant downturns in volatile markets like cryptocurrencies. The model employs credibility theory and trapezoidal fuzzy variables to more accurately capture the high levels of uncertainty and volatility that characterize digital assets. Unlike traditional probabilistic approaches, this model provides a more adaptive and precise risk management strategy. The proposed approach also incorporates practical constraints, including cardinality and floor and ceiling constraints, ensuring that the portfolio remains diversified, balanced, and aligned with real-world considerations such as transaction costs and regulatory requirements. Empirical analysis demonstrates the model’s effectiveness in constructing well-diversified portfolios that balance risk and return, offering significant advantages for investors in the rapidly evolving cryptocurrency market. This research contributes to the field of investment management by advancing the application of sophisticated portfolio optimization techniques to digital assets, providing a robust framework for managing risk in an increasingly complex financial landscape.
Social systems are characterized by an enormous network of connections and factors that can influence the structure and dynamics of these systems. Among them the whole economical sphere of human activity seems to be the most interrelated and complex. All financial markets, including the youngest one, the cryptocurrency market, belong to this sphere. The complexity of the cryptocurrency market can be studied from different perspectives. First, the dynamics of the cryptocurrency exchange rates to other cryptocurrencies and fiat currencies can be studied and quantified by means of multifractal formalism. Second, coupling and decoupling of the cryptocurrencies and the conventional assets can be investigated with the advanced cross-correlation analyses based on fractal analysis. Third, an internal structure of the cryptocurrency market can also be a subject of analysis that exploits, for example, a network representation of the market. In this work, we approach the subject from all three perspectives based on data from a recent time interval between January 2019 and June 2020. This period includes the peculiar time of the Covid-19 pandemic; therefore, we pay particular attention to this event and investigate how strong its impact on the structure and dynamics of the market was. Besides, the studied data covers a few other significant events like double bull and bear phases in 2019. We show that, throughout the considered interval, the exchange rate returns were multifractal with intermittent signatures of bifractality that can be associated with the most volatile periods of the market dynamics like a bull market onset in April 2019 and the Covid-19 outburst in March 2020. The topology of a minimal spanning tree representation of the market also used to alter during these events from a distributed type without any dominant node to a highly centralized type with a dominating hub of USDT. However, the MST topology during the pandemic differs in some details from other volatile periods.
The COVID-19 pandemic provided the first widespread bear market conditions since the inception of cryptocurrencies. We test the widely mooted safe haven properties of Bitcoin, Ethereum and Tether from the perspective of international equity index investors. Bitcoin and Ethereum are not a safe haven for the majority of international equity markets examined, with their inclusion adding to portfolio downside risk. Only investors in the Chinese CSI 300 index realized modest downside risk benefits (contingent on very limited allocations to Bitcoin or Ethereum). As Tether successfully maintained its peg to the US dollar during the COVID-19 turmoil, it acted as a safe haven investment for all of the international indices examined. We caveat the latter findings with a warning that Tether's dollar peg has not always been maintained, with evidence of impaired downside risk hedging properties earlier in our sample.
In this paper, the price synchronization in the cryptocurrencies market is investigated, using descriptive metrics and a novel methodology both from the area of Complex Networks. The study is conducted for three consecutive years, 2016-2018. In doing so, the cross-correlation-based networks of the cryptocurrencies under examination are created and subsequently their properties for each year are analyzed and compared. The novel Threshold Weighted – Minimum Dominating Set (TW–MDS) methodology is employed in order to identify the so-called dominant cryptocurrencies for each year. In this framework, a node is identified as dominant when it can adequately describe the collective behavior of its relevant neighborhood. The empirical results provide strong evidence of an increased price synchronization. One interesting finding is that, contrary to what may be expected, the cryptocurrencies that are identified as dominant are not always the most popular that are usually the ones with the highest capitalization (such as Bitcoin or Ethereum).
Multifractal detrended cross-correlation methodology is described and applied to Foreign exchange (Forex) market time series. Fluctuations of high-frequency exchange rates of eight major world currencies over 2010–2018 period are used to study cross-correlations. The study is motivated by fundamental questions in complex systems’ response to significant environmental changes and by potential applications in investment strategies, including detecting triangular arbitrage opportunities. Dominant multiscale cross-correlations between the exchange rates are found to typically occur at smaller fluctuation levels. However, hierarchical organization of ties expressed in terms of dendrograms, with a novel application of the multiscale cross-correlation coefficient, is more pronounced at large fluctuations. The cross-correlations are quantified to be stronger on average between those exchange rate pairs that are bound within triangular relations. Some pairs from outside triangular relations are, however, identified to be exceptionally strongly correlated as compared to the average strength of triangular correlations. This in particular applies to those exchange rates that involve Australian and New Zealand dollars and reflects their economic relations. Significant events with impact on the Forex are shown to induce triangular arbitrage opportunities which at the same time reduce cross-correlations on the smallest timescales and act destructively on the multiscale organization of correlations. In 2010–2018, such instances took place in connection with the Swiss National Bank intervention and the weakening of British pound sterling accompanying the initiation of Brexit procedure. The methodology could be applicable to temporal and multiscale pattern detection in any time series.
Multifractality is ubiquitously observed in complex natural and socioeconomic systems. Multifractal analysis provides powerful tools to understand the complex nonlinear nature of time series in diverse fields. Inspired by its striking analogy with hydrodynamic turbulence, from which the idea of multifractality originated, multifractal analysis of financial markets has bloomed, forming one of the main directions of econophysics. We review the multifractal analysis methods and multifractal models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. We survey the cumulating evidence for the presence of multifractality in financial time series in different markets and at different time periods and discuss the sources of multifractality. The usefulness of multifractal analysis in quantifying market inefficiency, in supporting risk management and in developing other applications is presented. We finally discuss open problems and further directions of multifractal analysis.
We present a detailed bubble analysis of the Bitcoin to US Dollar price dynamics from January 2012 to February 2018. We introduce a robust automatic peak detection method that classifies price time series into periods of uninterrupted market growth (drawups) and regimes of uninterrupted market decrease (drawdowns). In combination with the Lagrange Regularization Method for detecting the beginning of a new market regime, we identify three major peaks and 10 additional smaller peaks, that have punctuated the dynamics of Bitcoin price during the analysed time period. We explain this classification of long and short bubbles by a number of quantitative metrics and graphs to understand the main socio-economic drivers behind the ascent of Bitcoin over this period. Then, a detailed analysis of the growing risks associated with the three long bubbles using the Log-Periodic Power-Law Singularity (LPPLS) model is based on the LPPLS Confidence Indicators, defined as the fraction of qualified fits of the LPPLS model over multiple time windows. Furthermore, for various fictitious 'present' times t2 before the crashes, we employ a clustering method to group the predicted critical times t
c
of the LPPLS fits over different time scales, where t
c
is the most probable time for the ending of the bubble. Each cluster is proposed as a plausible scenario for the subsequent Bitcoin price evolution. We present these predictions for the three long bubbles and the four short bubbles that our time scale of analysis was able to resolve. Overall, our predictive scheme provides useful information to warn of an imminent crash risk.
Based on the high-frequency recordings from Kraken, a cryptocurrency exchange and professional trading platform that aims to bring Bitcoin and other cryptocurrencies into the mainstream, the multiscale cross-correlations involving the Bitcoin (BTC), Ethereum (ETH), Euro (EUR) and US dollar (USD) are studied over the period between 1 July 2016 and 31 December 2018. It is shown that the multiscaling characteristics of the exchange rate fluctuations related to the cryptocurrency market approach those of the Forex. This, in particular, applies to the BTC/ETH exchange rate, whose Hurst exponent by the end of 2018 started approaching the value of 0.5, which is characteristic of the mature world markets. Furthermore, the BTC/ETH direct exchange rate has already developed multifractality, which manifests itself via broad singularity spectra. A particularly significant result is that the measures applied for detecting cross-correlations between the dynamics of the BTC/ETH and EUR/USD exchange rates do not show any noticeable relationships. This could be taken as an indication that the cryptocurrency market has begun decoupling itself from the Forex.
I study the dependency and causality structure of the cryptocurrency market investigating collective movements of both prices and social sentiment related to almost two thousand cryptocurrencies traded during the first six months of 2018. This is the first study of the whole cryptocurrency market structure. It introduces several rigorous innovative methodologies applicable to this and to several other complex systems where a large number of variables interact in a non-linear way, which is a distinctive feature of the digital economy. The analysis of the dependency structure reveals that prices are significantly correlated with sentiment. The major, most capitalised cryptocurrencies, such as bitcoin, have a central role in the price correlation network but only a marginal role in the sentiment network and in the network describing the interactions between the two. The study of the causality structure reveals a causality network that is consistently related with the correlation structures and shows that both prices cause sentiment and sentiment cause prices across currencies with the latter being stronger in size but smaller in number of significative interactions. Overall this study uncovers a complex and rich structure of interrelations where prices and sentiment influence each other both instantaneously and with lead–lag causal relations. A major finding is that minor currencies, with small capitalisation, play a crucial role in shaping the overall dependency and causality structure. Despite the high level of noise and the short time-series I verified that these networks are significant with all links statistically validated and with a structural organisation consistently reproduced across all networks.
The paper analyzes the relationship between the most popular cryptocurrencies and a range of selected fiat currencies, in order to identify any pattern and/or causality between the series. Cryptocurrencies are a hot topic in Finance due to their strict relationship with the Blockchain system they originate from and therefore are normally considered as part of the ongoing, world-wide financial revolution. This innovative study investigates this relationship for the first time by thoroughly investigating the data, their features, and the way they are interconnected. Results show very interesting results in terms of how concentrated the causality effect on some specific cryptocurrencies and fiat currencies is. The outcome is a clear and possibly explainable relationship between cryptocurrencies and Asian markets, while envisioning some kind of Asian effect.
The concept of multifractality offers a powerful formal tool to filter out a multitude of the most relevant characteristics of complex time series. The related studies thus far presented in the scientific literature typically limit themselves to evaluation of whether a time series is multifractal, and width of the resulting singularity spectrum is considered a measure of the degree of complexity involved. However, the character of the complexity of time series generated by the natural processes usually appears much more intricate than such a bare statement can reflect. As an example, based on the long-term records of the S&P500 and NASDAQ—the two world-leading stock market indices—the present study shows that they indeed develop the multifractal features, but these features evolve through a variety of shapes, most often strongly asymmetric, whose changes typically are correlated with the historically most significant events experienced by the world economy. Relating at the same time the index multifractal singularity spectra to those of the component stocks that form this index reflects the varying degree of correlations involved among the stocks.
We empirically verify that the market capitalizations of coins and tokens in the cryptocurrency universe follow power-law distributions with significantly different values for the tail exponent falling between 0.5 and 0.7 for coins, and between 1.0 and 1.3 for tokens. We provide a rationale for this, based on a simple proportional growth with birth and death model previously employed to describe the size distribution of firms, cities, webpages, etc. We empirically validate the model and its main predictions, in terms of proportional growth (Gibrat's Law) of the coins and tokens. Estimating the main parameters of the model, the theoretical predictions for the power-law exponents of coin and token distributions are in remarkable agreement with the empirical estimations, given the simplicity of the model. Our results clearly characterize coins as being 'entrenched incumbents' and tokens as an 'explosive immature ecosystem', largely due to massive and exuberant Initial Coin Offering activity in the token space. The theory predicts that the exponent for tokens should converge to 1 in the future, reflecting a more reasonable rate of new entrants associated with genuine technological innovations.
Based on 1-min price changes recorded since year 2012, the fluctuation properties of the rapidly emerging Bitcoin market are assessed over chosen sub-periods, in terms of return distributions, volatility autocorrelation, Hurst exponents, and multiscaling effects. The findings are compared to the stylized facts of mature world markets. While early trading was affected by system-specific irregularities, it is found that over the months preceding April 2018 all these statistical indicators approach the features hallmarking maturity. This can be taken as an indication that the Bitcoin market, and possibly other cryptocurrencies, carry concrete potential of imminently becoming a regular market, alternative to the foreign exchange. Since high-frequency price data are available since the beginning of trading, the Bitcoin offers a unique window into the statistical characteristics of a market maturation trajectory.
Digital assets termed cryptocurrencies are correlated. We analyze cross correlations between price changes of different cryptocurrencies using methods of random matrix theory and minimum spanning trees. We find that the cross correlation matrix exhibits non-trivial hierarchical structures and groupings of cryptocurrency pairs, which are not present in the partial cross correlations. In sharp contrast to the predictions for other financial markets, we discover that most of the eigenvalues in the spectrum of the cross correlation matrix do not agree with the universal predictions of random matrix theory, but the few of the largest eigenvalues deviate as expected. The minimum spanning tree of cryptocurrency cross correlations reveals distinct community structures that are surprisingly stable. Collective behaviors that are present in the cryptocurrency market can be useful for the construction of portfolio of cryptocurrencies as well as for future research on the subject.
We use a data-driven methodology, namely the directed acyclic graph, to uncover the contemporaneous and lagged relations between Bitcoin and other asset classes. The adopted methodology allows us to identify causal networks based on the measurements of observed correlations and partial correlations, without relying on a priori assumptions. Results from the contemporaneous analysis indicate that the Bitcoin market is quite isolated, and no specific asset plays a dominant role in influencing the Bitcoin market. However, we find evidence of lagged relationships between Bitcoin and some assets, especially during the bear market state of Bitcoin. This finding suggests that the integration between the Bitcoin and other financial assets is a continuous process that varies over time. We conduct forecast error variance decompositions and find that the influence of each of the other assets on Bitcoin over a 20-day horizon does not account for more than 11% of all innovations.
We analyze quantitatively the effect of spurious multifractality induced by the presence of fat-tailed symmetric and asymmetric probability distributions of fluctuations in time series. In the presented approach different kinds of symmetric and asymmetric broad probability distributions of synthetic data are examined starting from Levy regime up to those with finite variance. We use nonextensive Tsallis statistics to construct all considered data in order to have good analytical description of frequencies of fluctuations in the whole range of their magnitude and simultaneously the full control over exponent of power-law decay for tails of probability distribution. The semi-analytical compact formulas are then provided to express the level of spurious multifractality generated by the presence of fat tails in terms of Tsallis parameter and the scaling exponent of the asymptotic decay of cumulated probability density function (CDF).The results are presented in Hurst and H\"{o}lder languages - more often used in study of multifractal phenomena. According to the provided semi-analytical relations, it is argued how one can make a clear quantitative distinction for any real data between true multifractality caused by the presence of nonlinear correlations, spurious multifractality generated by fat-tailed shape of distributions - eventually with their asymmetry, and the correction due to linear autocorrelations in analyzed time series of finite length. In particular, the spurious multifractal effect of fat tails is found basic for proper quantitative estimation of all spurious multifractal effects. Examples from stock market data are presented to support these findings.
Detection of power-law behavior and studies of scaling exponents uncover the characteristics of complexity in many real world phenomena. The complexity of financial markets has always presented challenging issues and provided interesting findings, such as the inverse cubic law in the tails of stock price fluctuation distributions. Motivated by the rise of novel digital assets based on blockchain technology, we study the distributions of cryptocurrency price fluctuations. We consider Bitcoin returns over various time intervals and from multiple digital exchanges, in order to investigate the existence of universal scaling behavior in the tails, and ascertain whether the scaling exponent supports the presence of a finite second moment. We provide empirical evidence on slowly decaying tails in the distributions of returns over multiple time intervals and different exchanges, corresponding to a power-law. We estimate the scaling exponent and find an asymptotic power-law behavior with 2 < {\alpha} < 2.5 suggesting that Bitcoin returns, in addition to being more volatile, also exhibit heavier tails than stocks, which are known to be around 3. Our results also imply the existence of a finite second moment, thus providing a fundamental basis for the usage of standard financial theories and covariance-based techniques in risk management and portfolio optimization scenarios.
Music is thought to engage its listeners by driving feelings of surprise, tension, and relief through a dynamic mixture of predictable and unpredictable patterns, a property summarized here as “expressiveness”. Birdsong shares with music the goal to attract its listeners’ attention and might use similar strategies to achieve this. We here tested a thrush nightingale’s (Luscinia luscinia) rhythm, as represented by song amplitude envelope (containing information on note timing, duration, and intensity), for evidence of expressiveness. We used multifractal analysis, which is designed to detect in a signal dynamic fluctuations between predictable and unpredictable states on multiple timescales (e.g. notes, subphrases, songs). Results show that rhythm is strongly multifractal, indicating fluctuations between predictable and unpredictable patterns. Moreover, comparing original songs with re-synthesized songs that lack all subtle deviations from the “standard” note envelopes, we find that deviations in note intensity and duration significantly contributed to multifractality. This suggests that birdsong is more dynamic due to subtle note timing patterns, often similar to musical operations like accelerando or crescendo. While different sources of these dynamics are conceivable, this study shows that multi-timescale rhythm fluctuations can be detected in birdsong, paving the path to studying mechanisms and function behind such patterns.
The properties of q-dependent cross-correlation matrices of stock market have been analyzed by using the random matrix theory and complex network. The correlation structure of the fluctuations at different magnitudes have unique properties. The cross-correlations among small fluctuations are much more stronger than those among large fluctuations. The large and small fluctuations are dominated by different group of stocks. The network representation of the financial market constructed from those q-dependent matrices uncovered some new identities of financial market. By utilizing those q-dependent correlation-based networks, we are able to construct some portfolio by those most independent stocks which consistently perform the best. The best multifractal order for portfolio optimization is around . Those results have deepened our understanding about the collective behaviors of the complex financial system.
Self-similarity across length scales is pervasively observed in natural systems. Here, we investigate topological self-similarity in complex networks representing diverse forms of connectivity in the brain and some related dynamical systems, by considering the correlation between edges directly connecting any two nodes in a network and indirect connection between the same via all triangles spanning the rest of the network. We note that this aspect of self-similarity, which is distinct from hierarchically nested connectivity (coarse-grain similarity), is closely related to idempotence of the matrix representing the graph. We introduce two measures, t(1) and t(∞), which represent the element-wise correlation coefficients between the initial matrix and the ones obtained after squaring it once or infinitely many times, and term the matrices which yield large values of these parameters "quasi-idempotent". These measures delineate qualitatively different forms of "shallow" and "deep" quasi-idempotence, which are influenced by nodal strength heterogeneity. A high degree of quasi-idempotence was observed for partially synchronized mean-field Kuramoto oscillators with noise, electronic chaotic oscillators, and cultures of dissociated neurons, wherein the expression of quasi-idempotence correlated strongly with network maturity. Quasi-idempotence was also detected for macro-scale brain networks representing axonal connectivity, synchronization of slow activity fluctuations during idleness, and co-activation across experimental tasks, and preliminary data indicated that quasi-idempotence of structural connectivity may decrease with ageing. This initial study highlights that the form of network self-similarity indexed by quasi-idempotence is detectable in diverse dynamical systems, and draws attention to it as a possible basis for measures representing network "collectivity" and pattern formation.
We closely examine and compare two promising techniques helpful in estimating the moment an asset bubble bursts. Namely, the Log-Periodic Power Law model and Generalized Hurst Exponent approaches are considered. Sequential LPPL fitting to empirical financial time series exhibiting evident bubble behavior is presented. Estimating the critical crash-time works satisfactorily well also in the case of GHE, when substantial „decorrelation“ prior to the event is visible. An extensive simulation study carried out on empirical data: stock indices and commodities, confirms very good performance of the two approaches.
We present a set of stylized empirical facts emerging from the statistical analysis of price variations in various types of financial markets. We first discuss some general issues common to all statistical studies of financial time series. Various statistical properties of asset returns are then described: distributional properties, tail properties and extreme fluctuations, pathwise regularity, linear and nonlinear dependence of returns in time and across stocks. Our description emphasizes properties common to a wide variety of markets and instruments. We then show how these statistical properties invalidate many of the common statistical approaches used to study financial data sets and examine some of the statistical problems encountered in each case.
Complex systems are composed of mutually interacting components and the output values of these components are usually long-range cross-correlated. We propose a method to characterize the joint multifractal nature of such long-range cross correlations based on wavelet analysis, termed multifractal cross wavelet analysis (MFXWT). We assess the performance of the MFXWT method by performing extensive numerical experiments on the dual binomial measures with multifractal cross correlations and the bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. For binomial multifractal measures, the empirical joint multifractality of MFXWT is found to be in approximate agreement with the theoretical formula. For bFBMs, MFXWT may provide spurious multifractality because of the wide spanning range of the multifractal spectrum. We also apply the MFXWT method to stock market indexes and uncover intriguing joint multifractal nature in pairs of index returns and volatilities.
Based on a recently proposed q-dependent detrended cross-correlation coefficient (J.~Kwapie\'n, P.~O\'swi\k{e}cimka, S.~Dro\.zd\.z, Phys. Rev.~E 92, 052815 (2015)), we introduce a family of q-dependent minimum spanning trees (qMST) that are selective to cross-correlations between different fluctuation amplitudes and different time scales of multivariate data. They inherit this ability directly from the coefficients that are processed here to construct a distance matrix being the input to the MST-constructing Kruskal's algorithm. In order to illustrate their performance, we apply the qMSTs to sample empirical data from the American stock market and discuss the results. We show that the qMST graphs can complement in detection of "hidden" correlations that cannot be observed by the MST graphs based on and, therefore, they can be useful in many areas where the multivariate cross-correlations are of interest (e.g., in portfolio analysis).
Since December 2019 the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has produced an outbreak of pulmonary disease which has soon became a global pandemic, known as COronaVIrus Disease-19 (COVID-19). The new coronavirus shares about 82% of its genome with the one which produced the 2003 outbreak (SARS CoV-1). Both coronaviruses also share the same cellular receptor, which is the angiotensin-converting enzyme 2 (ACE2) one. In spite of these similarities, the new coronavirus has expanded more widely, more faster and more lethally than the previous one. Many researchers across the disciplines have used diverse modeling tools to analyze the impact of this pandemic at global and local scales. This includes a wide range of approaches–deterministic, data-driven, stochastic, agent-based, and their combinations–to forecast the progression of the epidemic as well as the effects of non-pharmaceutical interventions to stop or mitigate its impact on the world population. The physical complexities of modern society need to be captured by these models. This includes the many ways of social contacts–(multiplex) social contact networks, (multilayers) transport systems, metapopulations, etc.–that may act as a framework for the virus propagation. But modeling not only plays a fundamental role in analyzing and forecasting epidemiological variables. It plays an important role in helping to find cures for the disease and in preventing contagion by means of new vaccines. The necessity for answering swiftly and effectively to the questions: Could existing drugs work against SARS CoV-2? and Can new vaccines be developed in time?demands the use of physical modeling of proteins, protein-inhibitors interactions, virtual screening of drugs against virus targets, predicting immunogenicity of small peptides, modeling vaccinomics and vaccine design, to mention just a few. Here, we review these three main areas of modeling research against SARS CoV-2 and COVID-19: (1) epidemiology; (2) drug repurposing; and (3) vaccine design. Therefore, we compile the most relevant existing literature about modeling strategies against the virus to help modelers to navigate this fast-growing literature. We also keep an eye on future outbreaks, where the modelers can find the most relevant strategies used in an emergence situation as the current one to help in fighting future pandemics.
Empirical time series of interevent or waiting times are investigated using a modified Multifractal Detrended Fluctuation Analysis operating on fluctuations of mean detrended dynamics. The core of the extended multifractal analysis is the nonmonotonic behavior of the generalized Hurst exponent h(q)—the fundamental exponent in the study of multifractals. The consequence of this behavior is the nonmonotonic behavior of the coarse Hölder exponent α(q) leading to multibranchedness of the spectrum of dimensions. The Legendre-Fenchel transform is used instead of the routinely used canonical Legendre (single-branched) contact transform. Thermodynamic consequences of the multibranched multifractality are revealed. These are directly expressed in the language of phase transitions between thermally stable, metastable, and unstable phases. These phase transitions are of the first and second orders according to Mandelbrot's modified Ehrenfest classification. The discovery of multibranchedness is tantamount in significance to extending multifractal analysis.
This study investigated the multifractality of streamflow data of 192 stations located in 13 river basins in India using the multifractal detrended fluctuation analysis (MF-DFA). The streamflow datasets of different river basins displayed multifractality and long-term persistence with a mean exponent of 0.585. The streamflow records of Krishna basin displayed least persistence and that of Godavari basin displayed strongest multifractality and complexity. Subsequently, the streamflow-sediment links of five major river basins were evaluated using the novel multifractal cross-correlation analysis (MFCCA) method of cross-correlation studies. The results showed that the joint persistence of streamflow and total suspended sediments (TSS) is approximately the mean of the persistence of individual series. The streamflow displayed higher persistence than TSS in 60% of the stations while in majority of stations of Godavari basin the trend was opposite. The annual cross-correlation is higher than seasonal cross-correlation in majority of stations but at these time scales strength of their association differs with river basin.
Cross correlations in fluctuations of the daily exchange rates within the basket of the 100 highest-capitalization cryptocurrencies over the period October 1, 2015–March 31, 2019 are studied. The corresponding dynamics predominantly involve one leading eigenvalue of the correlation matrix, while the others largely coincide with those of Wishart random matrices. However, the magnitude of the principal eigenvalue, and thus the degree of collectivity, strongly depends on which cryptocurrency is used as a base. It is largest when the base is the most peripheral cryptocurrency; when more significant ones are taken into consideration, its magnitude systematically decreases, nevertheless preserving a sizable gap with respect to the random bulk, which in turn indicates that the organization of correlations becomes more heterogeneous. This finding provides a criterion for recognizing which currencies or cryptocurrencies play a dominant role in the global cryptomarket. The present study shows that over the period under consideration, the Bitcoin (BTC) predominates, hallmarking exchange rate dynamics at least as influential as the U.S. dollar (USD). Even more, the BTC started dominating around the year 2017, while other cryptocurrencies, such as the Ethereum and even Ripple, assumed similar trends. At the same time, the USD, an original value determinant for the cryptocurrency market, became increasingly disconnected, and its related characteristics eventually started approaching those of a fictitious currency. These results are strong indicators of incipient independence of the global cryptocurrency market, delineating a self-contained trade resembling the Forex.
We examine the serial correlation structure of six liquid cryptocurrencies with a long data record – Bitcoin, DASH, Stellar, Litecoin, Monero, and Ripple – with a use of the detrended cross-correlation (DCCA) and detrending moving-average cross-correlation (DMCA) correlation coefficients. We find that these cryptocurrencies behave differently from the stock markets which are much closer to the random walk (efficient) dynamics. We further discuss issues connected to strong statements about cryptocurrency markets practical inefficiency.
Modularity matrix has long been used for inferring modular structure of stochastic networks of different scale-free nature. In this paper we show efficiency of the modularity to detect the core–periphery organization on the example of the cryptocurrency correlation-based network. The cryptocurrencies exemplify assets with dual macroeconomical background sharing properties of currency and stock markets with a non-obvious topological organization. We demonstrate that the modularity operator applied to a daily correlation-based network rules out community structure of the cryptocurrency market, simultaneously revealing stratification into a core and a periphery. Classification of tokens into two groups is shown to be day-dependent, however, stable tokens with statistically significant participation ratio can be easily identified. To approve the core–periphery organization of the stable assets, we compute the centrality measure of the two groups and show that it is considerably less for the periphery than for the core. Embedding of a subgraph of the stable tokens into the Euclidean space demonstrates clear spatial core–shell segregation. Furthermore, we show that the degree distribution of the minimal spanning tree has a distinctive power-law tail with exponent γ≈−2.6 which makes the cryptomarket an archetypal example of the scale-free network. Economical reasoning suggests that the revealed topological motif is in the full agreement with the outliers hypothesis. The core is driven by traditionally liquid and highly capitalized tokens, resembling blockchain and payment systems, while the periphery is marked by the stable tokens with little exposure to the market. We report that the very center of the core is populated by tokens with strong financial usage, while main drivers of the market (such as ETH or XRP) turn out to locate in the middle layers. This is an clear evidence of speculative processes underlying formation and evolution of the market.
Data quality is a bottleneck for efficient machine-to-machine communication without human intervention in Industrial Internet of Things (IIoT). Conventional centralised data quality management (DQM) approaches are not tamper-proof. They require trustworthy and highly skilled intermediation, and can only access and use data from limited data sources. This does not only impacts the integrity and availability of the IIoT data, but also makes the DQM process time and resource consuming. To address this problem, a blockchain based DQM platform is proposed in this paper, which aims to enable tamper-proof data transactions in a decentralised and trustless environment. To fit for different quality requirements, our platform supports customisable smart contracts for quality assurance. And to improve our platform’s performance, we discuss and analyze different distributed ledger technologies.
We compare gold and Bitcoin for the G7 stock markets, finding that gold and Bitcoin have distinct safe haven and hedging characteristics. Gold is an undisputable safe haven and hedge for several G7 stock indices, whereas Bitcoin takes these two functions in Canada. The out-of-sample hedging effectiveness of gold is much superior to that of Bitcoin. Furthermore, we find that the conditional diversification benefits offered by gold to equity investments in the G7 markets are comparatively much higher and more stable than those of Bitcoin, especially in the lower return quantiles, i.e., when both the stock and gold markets are in a bearish state. Implications are further discussed.
Cryptocurrency markets exhibit periods of large, recurrent arbitrage opportunities across exchanges. These price deviations are much larger across than within countries, and smaller between cryptocurrencies, highlighting the importance of capital controls for the movement of arbitrage capital. Price deviations across countries co-move and open up in times of large bitcoin appreciation. Countries with higher bitcoin premia over the US bitcoin price see widening arbitrage deviations when bitcoin appreciates. Finally, we decompose signed volume on each exchange into a common and an idiosyncratic component. The common component explains 80% of bitcoin returns. The idiosyncratic components help explain arbitrage spreads between exchanges.
Statistical and multiscaling characteristics of WTI Crude Oil futures prices expressed in US dollar in relation to the most traded currencies as well as to gold futures and to the E-mini S&P500 futures prices on 5 min intra-day recordings in the period January 2012–December 2017 are studied. It is shown that in most of the cases the tails of return distributions of the considered financial instruments follow the inverse cubic power law. The only exception is the Russian ruble for which the distribution tail is heavier and scales with the exponent close to 2. From the perspective of multiscaling the analysed time series reveal the multifractal organization with the left-sided asymmetry of the corresponding singularity spectra. Even more, all the considered financial instruments appear to be multifractally cross-correlated with oil, especially on the level of medium-size fluctuations, as the multifractal cross-correlation analysis carried out by means of the multifractal cross-correlation analysis (MFCCA) and detrended cross-correlation coefficient ρq show. The degree of such cross-correlations is however varying among the financial instruments. The strongest ties to the oil characterize currencies of the oil extracting countries. Strength of this multifractal coupling appears to depend also on the oil market trend. In the analysed time period the level of cross-correlations systematically increases during the bear phase on the oil market and it saturates after the trend reversal in 1st half of 2016. The same methodology is also applied to identify possible causal relations between considered observables. Searching for some related asymmetry in the information flow mediating cross-correlations indicates that it was the oil price that led the Russian ruble over the time period here considered rather than vice versa.
We study the cryptocurrency market with respect to the efficient market hypothesis. Specifically, we are interested in testing whether the examined coins and tokens are efficient or not but we also compare the levels of efficiency within the cryptomarket. To do so, we utilize the Efficiency Index comprising the long-range dependence, fractal dimension and entropy components. Focusing on a set of historical currencies – Bitcoin, DASH, Litecoin, Monero, Ripple, and Stellar – as well as popular currencies and tokens of the last year (with market capitalization above $0.5 billion), we uncover some surprising results. First, the historical currencies are unanimously inefficient over the analyzed period. Second, efficiency itself and ranking as well are dependent on the denomination (the US dollar or Bitcoin). Third, most of the coins and tokens were efficient between July 2017 and June 2018. And fourth, the least efficient coins turn out to be Ethereum and Litecoin whereas DASH is the winner as the most efficient cryptocurrency.
The growing cryptocurrency market has attracted the attention from many investors worldwide, mainly due to the ease of entering the market and its extremely volatile character. The main aim of this paper is to examine interdependencies between log-returns of cryptocurrencies, with the special focus on Bitcoin. Based on implications from the literature, we use methods dedicated for studying the stock market and apply the two-step analysis, comparing results between two subsequent periods. Results obtained using Minimum Spanning Tree (MST) method show that cryptocurrencies form hierarchical clusters, consistently over two separate periods, indicating potential topological properties of the cryptocurrency market. Then, using Vector Autoregression (VAR) model, we study the transmission of demand shocks within clusters. Results show that changes in Bitcoin price do not affect and are not affected by changes in prices of other cryptocurrencies. However, results indicate that findings obtained for Bitcoin shall not be generalized to the entire cryptocurrency market.
The bitcoin price has surged in recent years and it has also exhibited phases of rapid decay. In this paper we address the question to what extent this novel cryptocurrency market can be viewed as a classic or semi-efficient market. Novel and robust tools for estimation of multi-fractal properties are used to show that the bitcoin price exhibits a very interesting multi-scale correlation structure. This structure can be described by a power-law behavior of the variances of the returns as functions of time increments and it can be characterized by two parameters, the volatility and the Hurst exponent. These power-law parameters, however, vary in time. A new notion of generalized Hurst exponent is introduced which allows us to check if the multi-fractal character of the underlying signal is well captured. It is moreover shown how the monitoring of the power-law parameters can be used to identify regime shifts for the bitcoin price. A novel technique for identifying the regimes switches based on a goodness of fit of the local power-law parameters is presented. It automatically detects dates that can be associated with some known events in the bitcoin market place. A very surprising result is moreover that, despite the wild ride of the bitcoin price in recent years and its multi-fractal and non-stationary character, this price has both local power-law behaviors and a very orderly correlation structure when it is observed on its entire period of existence.
The price behavior of cryptocurrencies relative to conventional currencies remains relatively understudied, despite some attempts that consider the Bitcoin market. This study examines long-range cross-correlations and asymmetric multifractality between leading conventional currencies (Swiss Franc, Euro, British Pound, Yen, and Australian dollar) and main cryptocurrencies (Bitcoin, Litecoin, Ripple, Monero, and Dash) from June 2, 2014 to February 28, 2018. Empirical results show evidence of a significant asymmetric characteristic from the cross-correlation, that is found to be persistent and multifractal in most of the cases. Bitcoin and Litecoin are the cryptocurrencies that exhibit the most multifractal behavior, whereas Monero and Ripple generally exhibit lower multifractal behavior. All cryptocurrencies exhibit a slightly lower asymmetry for longer terms.
Current literature on the hedge or safe haven property of cryptocurrency focuses exclusively on a few number of the cryptocurrency. This paper gives a detailed observation by including 973 forms of cryptocurrency and 30 international indices from a dynamic perspective. The empirical results mainly show that: (1)generally, cryptocurrency is a safe haven but not a hedge for most of the international indices; (2)the safe haven property is more pronounced in subgroups with larger market capitalization and higher liquidity; and (3)the safe haven property is more pronounced in developed markets.
Bitcoin has attracted a wealth of attention in the media and by investors alike and this paper investigates whether Bitcoin can act as a hedge or safe haven against world currencies. Contrary to previous studies, we assess the relationship between Bitcoin and currencies at the hourly frequency since Bitcoin experiences quite large volatility throughout the day. We employ a ADCC model and find that Bitcoin can be an intraday hedge for the CHF, EUR and GBP, but acts as a diversifier for the AUD, CAD and JPY. We also implement the non-temporal Hansen (2000) test to examine the safe haven properties of Bitcoin and find that Bitcoin is a safe haven during periods of extreme market turmoil for the CAD, CHF and GBP. Therefore our results indicate that Bitcoin does act as an intraday hedge, diversifier and safe haven for certain currencies, which will be of great interest to currency, cryptocurrency and high frequency investors alike.
This paper addresses the timely question of whether Bitcoin exhibits a safe-haven property for stock market investments during extreme market conditions and whether such a property is similar to or different from that of gold and the general commodity index. We propose a new definition of a weak and strong safe-haven within a bivariate cross-quantilogram approach. This definition considers the lowest tails of both the safe-haven asset and the stock index. Our sample period spans from 19 July 2010 until 22 February 2018 and focuses on several stock market indices, including those of the US, China, and other developed and emerging economies. Our main results show that, at best, each of Bitcoin, gold, and the commodity index can be considered as a weak safe-haven asset in some cases. Rolling-window predictability analyses generally confirm those results and reveal that the safe-haven roles of Bitcoin, gold, and commodities are time-varying and differ across the stock market indices under study.
The relationship between oil prices and stock markets is commonly studied, in order to understand how financial markets are influenced by this important real asset. The evidence of any kind of interdependence is important, because investors could have additional information about the evolution of those kinds of assets. In this paper, we analyse the detrended cross-correlation coefficient between oil price and 20 different stock markets. We split our sample, in order to analyse the behaviour of that correlation before and after the 2008 crisis, allowing us to use the \Delta\rho_{DCCA}, searching for a possible increase in the connection between both variables. Based on the existing literature, which used simulated and real time series to define the critical values for \Delta\rho_{DCCA}, we study the statistical significance of this variable Our results show some evidence that before the crisis the correlations were low, but increased after the crisis, which could be understood as an increase in the relationship between oil and stock markets. This is an interesting result because it shows that stock markets are now more exposed to oil price fluctuation than before the crisis.
In this review article we present some of achievements of econophysics and sociophysics which appear to us the most significant. We briefly explain what their roles are in building of econo- and sociophysics research fields. We point to milestones of econophysics and sociophysics facing to challenges and open problems.
This paper provides a systematic review of the empirical literature based on the major topics that have been associated with the market for cryptocurrencies since their development as a financial asset in 2009. Despite astonishing price appreciation in recent years, cryptocurrencies have been subjected to accusations of pricing bubbles central to the trilemma that exists between regulatory oversight, the potential for illicit use through its anonymity within a young under-developed exchange system, and infrastructural breaches influenced by the growth of cybercriminality. Each influences the perception of the role of cryptocurrencies as a credible investment asset class and legitimate of value.
This study examines the presence of herding behaviour in the cryptocurrency market. The latter is the outcome of mass collaboration and imitation. Results from the static model suggest no significant herding. However, the presence of structural breaks and nonlinearities in the data series suggests applying a static model is not appropriate. Accordingly, we conduct a rolling-window analysis, and those results point to significant herding behaviour, which varies over time. Using a logistic regression, we find that herding tends to occur as uncertainty increases. Our findings induce useful insights related to portfolio and risk management, trading strategies, and market efficiency.
This paper discusses the dynamics of intraday prices of 12 cryptocurrencies during the past months’ boom and bust. The importance of this study lies in the extended coverage of the cryptoworld, accounting for more than 90% of the total daily turnover. By using the complexity-entropy causality plane, we could discriminate three different dynamics in the data set. Whereas most of the cryptocurrencies follow a similar pattern, there are two currencies (ETC and ETH) that exhibit a more persistent stochastic dynamics, and two other currencies (DASH and XEM) whose behavior is closer to a random walk. Consequently, similar financial assets, using blockchain technology, are differentiated by market participants.
We analyse, in the time and frequency domains, the relationships between three popular cryptocurrencies and a variety of other financial assets. We find evidence of the relative isolation of these assets from the financial and economic assets. Our results show that cryptocurrencies may offer diversification benefits for investors with short investment horizons. Time variation in the linkages reflects external economic and financial shocks.
Alzheimer’s disease (AD) is a degenerative disorder of neural system that affects mainly the older population. Recently, many researches show that the EEG of AD patients can be characterized by EEG slowing, enhanced complexity of the EEG signals, and EEG synchrony. In order to examine the neural synchrony at multi scales, and to find a biomarker that help detecting AD in diagnosis, detrended cross-correlation analysis (DCCA) of EEG signals is applied in this paper. Several parameters, namely DCCA coefficients in the whole brain, DCCA coefficients at a specific scale, maximum DCCA coefficient over the span of all time scales and the corresponding scale of such coefficients, were extracted to examine the synchronization, respectively. The results show that DCCA coefficients have a trend of increase as scale increases, and decreases as electrode distance increases. Comparing DCCA coefficients in AD patients with healthy controls, a decrease of synchronization in the whole brain, and a bigger scale corresponding to maximum correlation is discovered in AD patients. The change of max-correlation scale may relate to the slowing of oscillatory activities. Linear combination of max DCCA coefficient and max-correlation scale reaches a classification accuracy of 90%. From the above results, it is reasonable to conclude that DCCA coefficient reveals the change of both oscillation and synchrony in AD, and thus is a powerful tool to differentiate AD patients from healthy elderly individuals.
In recent years a new type of tradable assets appeared, generically known as cryptocurrencies. Among them, the most widespread is Bitcoin. Given its novelty, this paper investigates some statistical properties of the Bitcoin market. This study compares Bitcoin and standard currencies dynamics and focuses on the analysis of returns at different time scales. We test the presence of long memory in return time series from 2011 to 2017, using transaction data from one Bitcoin platform. We compute the Hurst exponent by means of the Detrended Fluctuation Analysis method, using a sliding window in order to measure long range dependence. We detect that Hurst exponents changes significantly during the first years of existence of Bitcoin, tending to stabilize in recent times. Additionally, multiscale analysis shows a similar behavior of the Hurst exponent, implying a self-similar process.
Most empirical literature investigates the relation between oil prices and exchange rate through different models. These models measure this relationship on two time scales (long and short terms), and often fail to observe the co-movement of these variables at different time scales. We apply a detrended cross-correlation approach (DCCA) to investigate the co-movements of the oil price and exchange rate in 12 Asian countries. This model determines the co-movements of oil price and exchange rate at different time scale. The exchange rate and oil price time series indicate unit root problem. Their correlation and cross-correlation are very difficult to measure. The result becomes spurious when periodic trend or unit root problem occurs in these time series. This approach measures the possible cross-correlation at different time scale and controlling the unit root problem. Our empirical results support the co-movements of oil prices and exchange rate. Our results support a weak negative cross-correlation between oil price and exchange rate for most Asian countries included in our sample. The results have important monetary, fiscal, inflationary, and trade policy implications for these countries.
This article aimed at presenting the scattered econophysics literature as a unified and coherent field through a specific lens imported from philosophy science. More precisely, I used the methodology developed by Imre Lakatos to cover the methodological evolution of econophysics over these last two decades. In this perspective, three co-existing approaches have been identified: statistical econophysics, bottom-up agent based econophysics and top-down agent based econophysics. Although the last is presented here as the last step of the methodological evolution of econophysics, it is worth mentioning that this tradition is still very new. A quick look on the econophysics literature shows that the vast majority of works in this field deal with a strictly statistical approach or a classical bottom-up agent-based modelling. In this context of diversification, the objective (and contribution) of this article is to emphasize the conceptual coherence of econophysics as a unique field of research. With this purpose, I used a theoretical framework coming from philosophy of science to characterize how econophysics evolved by combining a methodological enrichment with the preservation of its core conceptual statements.