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Evaluating the sophisticated digital assets and cryptocurrencies capacities of substituting international currencies in inflationary eras
Abstract
This study investigates the dynamic nexus that major international currencies (US dollar, Euro, Japanese yen)
exhibit with cryptocurrencies and highly innovative digital money (DeFi and NFT assets) during inflationary
periods such as the Russia-Ukraine conflict (from 14 December 2021 until 1 March 2024). The Quantile Vector
Autoregressive methodology as in Cunado et al. (2023) and daily data are adopted to investigate the net joint
extended dynamic connectedness and network connectedness at lower and upper quantiles. Conventional international
currencies act as hedgers against shocks while major cryptocurrencies are only modest generators
with Ripple being an influential absorber of effects. DeFi mainly serve for counteracting losses from conventional
investments in bear or bull markets and Maker is the most prominent generator of spillovers while NFTs mostly
rely on a few very strong leaders –Gala being by far the strongest- to have an impact, imitating Bitcoin in the
early cryptocurrency era.
... The global financial markets have witnessed several uncertainties since the Great Depression (1929)(1930)(1931)(1932)(1933)(1934)(1935)(1936)(1937)(1938)(1939), with the pandemic and the Russia-Ukraine conflict adding to the factors driving these uncertainties (Dimitriadis et al., 2024a;Su et al., 2024;Woode et al., 2024b). These growing uncertainties require resilient financial devices, which led to the development of digital assets, such as cryptocurrencies (cryptos) and non-fungible tokens (NFTs). ...
... These growing uncertainties require resilient financial devices, which led to the development of digital assets, such as cryptocurrencies (cryptos) and non-fungible tokens (NFTs). Per prior studies (Dimitriadis et al., 2024a;Mazur & Polyzos, 2024), cryptos and NFTs were developed as innovative hedging instrument in response to the thriving liquidity tests facing the worldwide economic systems amid incessant chaos. Despite the supposed resilience of these assets, recent global events (pandemics, political tensions, conflicts, and policy uncertainty) have exposed their vulnerability, with numerous chaos and crashes being the consequence (Beneki et al., 2019;Biju & Thomas, 2023;Dimitriadis et al., 2024b;Sadorsky & Henriques, 2024;Woode et al., 2024a). ...
... Against this decline and global uncertainties, this study seeks to unravel the factors that drive the NFT market with emphasis on uncertainty measures. With insight from prior NFT-based studies (Ante, 2023;Dimitriadis et al., 2024a;Dowling, 2022;Ghosh et al., 2023;Liu et al., 2023;Sadorsky & Henriques, 2024;Urom et al., 2022;Yousaf & Yarovaya, 2022;Zhang, 2023) that highlighted the challenges (illiquid, speculative, and volatile) possessed by the market, we aim to examine the nexus between the NFTs (BKC, BAYC, CRP, DUD, MAYC, and PUP) and various uncertainty measures (world uncertainty, geopolitical risk, crypto volatility, and crypto uncertainty) established to drive the digital asset market, with emphasis on the uncertain traits of the NFT market as illustrated by Ghosh et al. (2023). This study also accounted for the role of Ethereum (ETH) and Bitcoin (BTC), since the sample NFTs are traded with these cryptos. ...
Responding to the fluctuations in the digital asset markets against the incessant turmoil and the limited studies on the role of varying uncertainty measures in driving these assets, we examine the predictive capacity of uncertainty (world uncertainty, geopolitical risk, crypto volatility, and crypto uncertainty) on the most capitalized and tradable non-fungible tokens (NFTs) (Bored Ape Yacht Club, Bored Ape Kernel Club, Cryptopunks, Doodles, Mutant Ape Yacht Club, and Puddy Penguins) from April 2021 to October 2024. The quantile regression results highlight an adverse (positive) nexus between NFTs and WUI and VCRIX (UCRP and GPR), with WUI being the most robust factor in driving NFTs. The blend of adverse (positive) influence suggests that the NFT market possesses a diverging capacity in terms of hedge and safe haven, with WUI being the most detrimental measure derailing NFT-based investors from such luxury. UCRP positively influences NFTs, while ETH and BTC fail in such capacity. GPR has a limited adverse (positive) influence on BAYC and DUD (MAYC), indicating that only the latter can hedge against GPR. These results support the quantile-on-quantile estimations, highlighting the adaptive and heterogeneous influence of the sample uncertainties on NFTs. The causality-in-quantile results highlighted the predictive capability of all the uncertainty measures, with WUI and UCRP providing the most robust results across most of the quantiles, with GPR and VCRIX possessing limited influence, emphasizing the robust nature of these uncertainty measures on NFTs. These results provide insights into investor heterogeneity and adaptive behavior and the limited potential for diversification within the NFT markets. The results also have implications for NFT-based investors and ventures, speculators, market regulators and policies, and risk management.
... AIC Plateau confirms Model fit improves up to 15 analogues (∆AIC > 2), then stagnates. Spatial Dependence confirms Moran's I drops below 0.1 (p > 0.05) at this scale.Advocates for "contextually scaled" modelsvalidated here[62][63][64] . Used fixed radii; our flexible analogue count better captures irregular landscapes. ...
The results of mass appraisal in many countries are used as a basis for calculating the amount of real estate tax, therefore, regardless of the methods used to calculate it, the resulting value should be as close as possible to the market value of the real estate to maintain a balance of interests between the state and the rights holders. In practice, this condition is not always met, since, firstly, the quality of market data is often very low, and secondly, some markets are characterized by low activity, which is expressed in a deficit of information on asking prices. The aim of the work is ecological valuation of land use: how regression-based mass appraisal can inform ecological conservation, land degradation, and sustainable land management. Four multiple regression models were constructed for AI generated map of land plots for recreational use in St. Petersburg (Russia) with different volumes of market information (32, 30, 20 and 15 units of market information with four price-forming factors). During the analysis of the quality of the models, it was revealed that the best result is shown by the model built on the maximum sample size, then the model based on 15 analogs, which proves that a larger number of analog objects does not always allow us to achieve better results, since the more analog objects there are.
... This research is built on earlier studies such as Koop, Pesaran, and Potter (1996), Shin (1998), Gabauer (2020), Stenfors, Chatziantoniou, and Gabauer (2022), Dimitriadis, Koursaros, and Savva (2024a, 2024b, 2024c, Hong et al. (2024) and Kyriazis et al. (2024). This work is connected with earlier academic papers that investigate how geopolitical risk is linked with the real economy (Balestra, Caruso, and Di Domizio 2024;Kyriazis and Economou 2023;Li et al. 2024;Yang et al. 2023) and financial markets (Baur and Smales 2020;Chiang 2022;Fang et al. 2024;Ngo et al. 2024;Salisu, Lasisi, and Tchankam 2022;Selmi, Bouoiyour, and Wohar 2022;Wang, Wu, and Xu 2019;Zheng et al. 2023). ...
This paper examines the dynamic interplay between the global geopolitical risk and eleven decentralized finance (DeFi) digital currencies during the inflationary burden caused by the Russia-Ukraine war episodes. Daily data spanning from 13 October 2021 to 29 October 2024 and the innovative Quantile-Vector Autoregressive (Q-VAR) methodology are employed for estimating the pairwise, joint and network linkages at the lower, middle and upper quantiles. High levels of geopolitical risk are more connected with bull markets of the DeFi assets and new war episodes strengthen this relation. Geopolitical tensions combined with high inflation lead to the GPR becoming major determinant of DeFi markets so contributing to the transition to the digital decentralized cashless financial system. Maker is the leading DeFi asset in this transition and constitutes a promising successor of fiat currencies that suffer from devaluation generated by conflicts.
This study examines portfolio risk assessment in finance using advanced quantile and superquantile techniques. The portfolio analyzed consists of widely recognized stocks, including Apple (AAPL), Microsoft (MSFT), Alphabet (GOOGL), and Tesla (TSLA). The primary objective is to enhance the accuracy and robustness of financial risk evaluation for such a portfolio. To achieve this, we developed innovative methods for computing quantiles and superquantiles, leveraging various probability distributions. In particular, we explored the Exponential, Gumbel, Frchet, and α-stable distributions to model the returns of the selected equities. The parameters of these distributions were estimated based on historical financial data over a defined time horizon. By doing so, we aimed to better capture the statistical characteristics of asset returns and their tail behavior, which is crucial for effective risk management. The findings reveal that these advanced quantile and superquantile approaches provide deeper insights into the potential risks associated with the portfolio. Compared to traditional risk metrics, such as Value at Risk (VaR) and Expected Shortfall (ES), the proposed methodologies offer a more refined evaluation of extreme losses and downside risks. Additionally, the study highlights how different distributional assumptions impact risk estimates, demonstrating the importance of selecting appropriate models for financial data analysis. Furthermore, we illustrate how these improved risk assessment techniques can assist investors and portfolio managers in making more informed decisions regarding risk exposure. By integrating these methods into risk management frameworks, financial professionals can enhance their ability to anticipate and mitigate adverse market conditions. Ultimately, this research contributes to the ongoing efforts to refine quantitative finance tools, ensuring more reliable and data-driven decision-making in portfolio management.
This paper investigates the impact that the ‛green character’ of stocks generates on their market price of risk. The
study covers a spectrum of international large-cap stocks in influential sectors since 1 January 2013 up to 5 July
2022. Focusing on the asymmetric and shape features of price risk, econometric outcomes reveal that wellestablished
(Industrials) or highly profitable (Technology) sectors are less affected by the skewness/kurtosis
price of risk or the “environmental-friendly” features. On the contrary, sectors vulnerable to alterations in income
or wealth, such as Financials and Real Estate are more sizably influenced by negative asymmetry, kurtosis, and
the higher volatility in bear markets. The ‛green character’ lowers the market price of stocks, increases risk
premia due to higher probability for downwards movements and renders investors more risk-averse and
nevertheless, this impact only reflects the initial period of green innovation with high anti-polluting costs as the
green label could prove to be greatly beneficial in the future.
This paper examines the dynamic connectedness between Gulf countries and BRICS stocks markets with a sample of cryptocurrencies, as well as two newly developed digital assets, namely NFT and DeFi, and Gold. The period under examination spans from January 2019 until September 2022. Our analysis is based on wavelet coherence, which is a suitable methodology considering the nonlinear dynamics present in data. Our empirical results clearly identify nontrivial time-varying connectedness between different assets and the stock markets. Asymmetric patterns in the interconnections of newly developed digital assets, cryptocurrencies, Gold and emerging market indices are well-documented, especially during the advent of the health and political events. Our empirical findings have relevant implications for portfolio managers, investors and researchers about portfolio allocation, investment strategies and potential diversification benefits of NFT and DeFi digital assets.
This paper examines the efficiency and asymmetric multifractal features of NFTs, DeFi, cryptocurrencies, and traditional assets using Asymmetric Multifractal Cross-Correlations Analysis covering the period from November 2017 to February 2022. Considering the full sample with a significant variation among asset classes, the study reveals DeFi-DigiByte is the most efficient while the cryptocurrency-Tether is the least efficient. However, S&P 500 showed high efficiency before COVID-19, and DeFi-Enjin Coin advanced as the most efficient asset during COVID-19. The volatility dynamics of NFTs, DeFi, and cryptocurrencies follow strong nonlinear cross-correlations, but evidence of weaker nonlinearity exists in traditional assets. Additionally, the sensitivity to smaller events in bull markets is high for NFTs and DeFi. The findings have significant implications for portfolio diversification when an investor's portfolio set includes traditional assets and cryptocurrency and relatively new blockchain-based assets like NFTs and DeFi.
We use transaction data on CryptoPunks to dissect the factors affecting the returns of non-fungible tokens (NFTs). Our results show that trading volume in the short period before a trader buys (sells) CryptoPunk relates negatively (positively) to the returns on NFTs, suggesting that when market trading volume is at a high level, NFT owners are better off on the sell side, and investors interested in NFTs should avoid joining the herd. Turnover of a token tends to harm its returns. Finally, both traders' willingness to purchase and trading experience have a positive impact on NFT returns within short-term investment horizons.
The nonfungible token (NFT) marketplace spiked in the recent past. The concept originated initially as a token standard of Ethereum, an open-source blockchain with smart contract functionality, where each token is characterized by distinguishable signs. These types of tokens have unique digital properties that allow their distinct identification. NFTs, with their distinct qualities, can be fluidly traded with customized values according to their ages, rarity, and liquidity. The trading of NFTs has heavily influenced the growth of the decentralized application (dApp) marketplace, as exponential returns (thousand folds from their original value) on its ever-expanding market are being observed, leading to worldwide attention. However, the NFT ecosystem is in its nascence, and the associated technologies are still in their infancy. New researchers might be fascinated with the exponential, yet nebulous evolution of NFTs; however, this novelty has contributed to the paucity of systematic and conclusive published research work on this topic. This review portrays the NFT ecosystem multidimensionally, wherein the paper commences with an overview of state-of-the-art NFT technology and furnishes summary standards and desired properties. Finally, the study concludes with an elaborate discussion of the future outlook for and prime challenges faced by NFTs.
Central banks may soon issue currencies that are entirely digital (CBDCs) and possibly interest-bearing. A strategic analytical framework is used to investigate this innovation in the laboratory, contrasting a traditional “plain” tokens baseline to treatments with “sophisticated” interest-bearing tokens. In the experiment, this theoretically beneficial innovation precluded the emergence of a stable monetary system, reducing trade and welfare. Similar problems emerged when sophisticated tokens complemented or replaced plain tokens. This evidence underscores the advantages of combining theoretical with experimental investigation to provide insights for payments systems innovation and policy design.
This paper investigates the impact of US monetary policy shocks on Bitcoin prices. On the day of the Federal Open Market Committee (FOMC) meeting, a hypothetical unexpected monetary tightening by 1 basis point of two-year Treasury yield is associated with a 0.25% drop in the price of Bitcoin, comparable in magnitude to the effect on the gold price. The cumulative effect is much stronger in the next few days following the FOMC meeting. Impulse response analysis shows that this effect is very persistent. According to quantile regression, monetary policy surprises have a greater impact on Bitcoin prices during a market boom.
Using daily data over the period August 5, 2013–September 27, 2019, this study investigates the dynamic spillovers between international monetary policies across four major economies (i.e. Eurozone, Japan, UK and US) and three key cryptocurrencies (i.e. Bitcoin, Litecoin and Ripple). In doing so, we apply a Time-Varying Parameter Vector Auto-Regression (TVP-VAR) model, a dynamic connectedness approach and network analysis. The empirical results indicate that cryptocurrency returns and monetary policy spillovers were particularly large when shadow policy rates became negative, moderated during the Fed's ‘tapering process’, and sharpened again more recently as cryptocurrency buoyancy returned. Gross directional spillovers suggest that shadow policy rates have more ‘to give than to receive’, while those from and to cryptocurrency returns are naturally volatile. There is also strong interconnectedness between monetary policy in either the US or the Eurozone and the UK, and between Bitcoin and Litecoin. However, the spillovers across monetary policy and cryptocurrencies tend to be muted. Finally, spillovers were only slightly larger during the Fed's ‘unconventional’ policy compared to the ‘standard’ era, but their composition qualitatively changed over time.
This paper examines return and volatility connectedness between Bitcoin, traditional financial assets (Crude. To this end, the Time-Varying Parameter Vector Autoregression (TVP-VAR) model, dynamic connectedness approaches, and network analyses are used. The results indicate that total spillover indices reached unprecedented levels during COVID-19 and have remained high since then. The evidence also confirms the high return and volatility spillovers across markets during the COVID-19 era. Regarding the return spillovers, Gold is the centre of the system and demonstrates the safe heaven properties. Bitcoin is a net transmitter of volatility spillovers to other markets, particularly during the COVID-19 period. Furthermore, the causality-in-variance Lagrange Multiplier (LM) and the Fourier LM tests' results confirm a unidirectional volatility transmission from Bitcoin to Gold, Stocks, Bonds, the VIX and Crude Oil. Interestingly the EPU is the only global factor that causing higher volatility in Bitcoin. Several potential implications of the results are also discussed.
This paper investigates digital financial bubbles amidst the COVID-19 pandemic. Using a sample of 9 DeFi tokens, 3 NFTs, Bitcoin, and Ethereum, we detect several bubbles overlapping the examined cryptoassets. We also uncover DeFi and NFT-specific bubbles in Summer 2020 suggesting distinct driving factors for this class of assets. We document that DeFi and NFTs bubbles are less recurrent but have higher magnitudes than cryptocurrencies’ bubbles. We also find that COVID-19 and trading volume exacerbate bubble occurrences, while Total Value Locked (TVL) is negatively associated with cryptoassets’ bubbles. Our results suggest that TVL can be used as a tool for market monitoring.
This study introduces a comprehensive Google search volume based Bitcoin Sentiment Index (BSI) and investigates its symmetric and asymmetric association with Bitcoin's returns, volume, and volatility, and with United States Dollar (USD) exchange rates. Our results indicate a positive association of BSI with Bitcoin's returns and volume, but a negative relationship with its return volatility. Besides, Bitcoin's optimistic sentiments have an asymmetric relationship with the USD exchange rate in the short-run, and the Bitcoin price has an asymmetric and negative association with USD in the short-run and long-run. We also found a 35.47% speed of adjustment to long-run equilibrium.
DeFi (‘decentralized finance’) has joined FinTech (‘financial technology’), RegTech (‘regulatory technology’), cryptocurrencies, and digital assets as one of the most discussed emerging technological evolutions in global finance. Yet little is really understood about its meaning, legal implications, and policy consequences. In this article we introduce DeFi, put DeFi in the context of the traditional financial economy, connect DeFi to open banking, and end with some policy considerations. We suggest that decentralization has the potential to undermine traditional forms of accountability and erode the effectiveness of traditional financial regulation and enforcement. At the same time, we find that where parts of the financial services value chain are decentralized, there will be a reconcentration in a different (but possibly less regulated, less visible, and less transparent) part of the value chain. DeFi regulation could, and should, focus on this reconcentrated portion of the value chain to ensure effective oversight and risk control. Rather than eliminating the need for regulation, in fact DeFi requires regulation in order to achieve its core objective of decentralization. Furthermore, DeFi potentially offers an opportunity for the development of an entirely new way to design regulation: the idea of ‘embedded regulation’. Regulatory approaches could be built into the design of DeFi, thus potentially decentralizing both finance and its regulation, in the ultimate expression of RegTech.
This paper reviews the empirical literature on the highly popular phenomenon of herding behaviour in the markets of digital currencies. Furthermore, a comparison takes place with outcomes from earlier studies about traditional financial assets. Moreover, we empirically investigate herding behaviour of 240 cryptocurrencies during bull and bear markets. The present survey suggests that empirical findings about whether herding phenomena have made a significant appearance or not in cryptocurrency markets are split. The Cross-sectional absolute deviations (CSAD) and Cross-sectional standard deviations (CSSD) approaches for measuring herding tendencies are found to be the most popular. Different behaviour is detected in bull periods compared to bear markets. Nevertheless, evidence from primary studies indicates that herding is stronger during extreme situations rather than in normal conditions. However, our empirical estimations reveal that herding behaviour is evident only in bull markets. These findings cast light on and provide a roadmap for investment decisions with modern forms of liquidity.
Current financial discourse suggests the imminence of a cashless society, a concept that arose from the global popularization of digital financial services and the development of technologies with the potential for application in financial markets. However, claims about the impending obsolescence of paper money are neither disruptive nor a novelty. Instead, this paper argues that the conversion of money from paper to bits has been a gradual, adaptive process, and that money is already digital. Moreover, in this paper we propose that the statuses of electronic money (e-money) and banknotes have switched in the view of monetary authorities.
Bitcoin is defined as digital money within a decentralized peer-to-peer payment network. It is a hybrid between fiat currency and commodity currency without intrinsic value and independent of any government or monetary authority. This paper analyses the question of whether Bitcoin is a medium of exchange or an asset and more specifically, what is its current usage and what usage will prevail in the future given its characteristics. We analyse the statistical properties of Bitcoin and find that it is uncorrelated with traditional asset classes such as stocks, bonds and commodities both in normal times and in periods of financial turmoil. The analysis of transaction data of Bitcoin accounts shows that Bitcoins are mainly used as a speculative investment and not as an alternative currency and medium of exchange.
The asymptotic power envelope is derived for point-optimal tests of a unit root in the autoregressive representation of a Gaussian time series. The authors propose a family of tests whose asymptotic power functions are tangent to the power envelope at one point and are never far below. When the series has an unknown mean or linear trend, commonly used tests are found to be dominated by members of the family of point-optimal invariant tests. The authors propose a modified version of the Dickey-Fuller t test which has desirable size properties and substantially improved power when an unknown mean or trend is present. Copyright 1996 by The Econometric Society.
This study investigates whether representative sectoral stock indices, gold, oil, Bitcoin, and wheat canmitigate risk and improve portfolio performance during normal times versus crises. The cutting-edgeQuantile Vector Autoregressive model and the Generalized Dynamic Conditional Correlations(Generalized-DCC) framework are adopted covering from 9 January 2017 until 30 August 2022.Econometric findings by the Q-VAR reveal that oil presents the strongest connection with commod-ities and stock indices and that Bitcoin and wheat despite their significant linkages with financialmarkets fail to act as safe havens. Moreover, GDCC-GARCH indicates that the returns of sectoralindices are weakly related but display powerful volatility co-movements. Gold serves efficiently asa hedger and oil follows and both act as better shelters during crises. Nevertheless, Bitcoin partlyabides by conventional markets in stressed periods. Notably, wheat reliably works as a hedger overallbut does not become a safe haven during crises.
The aim of this study is to investigate the volatility spillover connectedness between NFTs attention and financial markets. This paper firstly proposes a new direct proxy for the public’s attention in the NFT market: the non-fungible tokens attention index (NFTsAI), based on 590 m news stories from the LexisNexis News & Business database and applies the historical decomposition to assess the historical variations of the NFTsAI. Then the empirical analysis is performed via a TVP-VAR volatility spillover connectedness model. The empirical results show that NFTsAI indicates NFT markets are dominated by cryptocurrency, DeFi, equity, bond, commodity, F.X. and gold markets. And NFT markets are volatility spillover receivers. In addition, NFT assets could impede financial contagion and have significant diversification benefits. Employing a panel pooled OLS regression model as a supplementary analysis and a GARCH-MIDAS model as a robustness test. This study reveals that NFTsAI has sufficient power to explain the return of NFT assets from a fixed effect perspective, and NFTsAI contains useful forecasting information for both short and long-term volatility of NFT markets, separately. The new NFTsAI and the empirical findings contain useful insights for risk-averse investors, portfolio managers, institutional investors, academics and financial policy regulators on how NFTsAI could act as an indicator in the new digital currency era.
Despite mounting central bank concerns over cryptocurrency adoption, there is little evidence that monetary policy compels national currency holders to consider decentralized virtual alternatives. Exploiting cross-country variation in prescheduled announcement timing, I isolate how monetary policy announcements affect local Bitcoin demand using an event-study design on a panel of 26 emerging economies. I find that monetary policy announcements increase both measures of local Bitcoin attention and local Bitcoin trade volume, but only on days when the public is abnormally attentive to inflation. These announcement-induced Bitcoin trading spikes are only significant in countries that are not normally accustomed to inflation risk.
We propose a “debt view” to explain the dominant international role of the dollar. Within a simple capital-structure model with debt-currency choice, we show that the “dominant currency” is the one that (1) depreciates in global downturns over horizons of typical debt maturity and (2) has the steepest nominal yield curve. Empirically, we show the dollar fits this description better than other major currencies. The debt view can explain dollar-debt-issuance patterns over the past two decades. It also offers insights into the future of the dominance of the dollar in the aftermath of the COVID-19 crisis.
We investigate 1-year interest rate swaps on USD, EUR, JPY and GBP between 2005 and 2020 utilising a quantile connectedness model. This approach allows for a nuanced investigation of connectedness and adds to understanding the monetary policy transmission mechanism within a highly integrated international financial system. Substantial interest rate changes (in either direction) matter for connectedness in financial markets. The results also indicate which currency drives developments depending on the direction of the change in interest rates. The full implementation and replication code — based on R, is available at: https://github.com/GabauerDavid/ConnectednessApproach.
This paper investigates whether bitcoin has a nonlinear relationship with six currencies: euro, pound sterling, Swiss franc, renminbi, yen, and ruble, each denominated in US dollars. It employs the nonparametric causality test proposed by Diks and Panchenko (2006) and applies a multivariate filtering approach using BEKK-GARCH residuals to control the conditional heteroskedasticity on daily log-returns from July 2010 to April 2020. We also split the bitcoin dataset into two samples, one before and one after a structural break. Results reveal a direct impact of the euro on bitcoin. However, in the post-break sample, there is only an effect from renminbi to bitcoin. Findings shed light on the nonlinear relationship dynamics among currencies and whether fiat currencies can help predict bitcoin's behavior.
Utilizing the WHO COVID-19 pandemic statement, we test Bitcoin and Ethereum as safe-havens for stocks. We find that the two largest cryptocurrencies are suitable as short-term safe-havens. The DCC and cDCC results show that their daily returns tend to correlate with S&P500 return negatively during the pandemic. The regression results also robustly support the safe-haven features and uncover that Ethereum is possibly a better safe-haven than Bitcoin. However, we note that both coins exhibit high volatilities. Before (during) the pandemic daily volatilities of Bitcoin, Ethereum, gold, and the S&P500 are 3.44% (9.11%), 4.34% (10.96%), 0.89% (2.19%), and 1.27% (6.07%), respectively.
This paper surveys the academic literature concerning the formation of pricing bubbles in digital currency markets. Studies indicate that several bubble phases have taken place in Bitcoin prices, mostly during the years 2013 and 2017. Other digital currencies of primary importance, such as Ethereum and Litecoin, also exhibit several bubble phases. The Augmented Dickey Fuller (ADF) as well as the Log-Periodic Power Law (LPPL) methodology are the most frequently employed techniques for bubble detection and measurement. Based on much academic research, Bitcoin appears to have been in a bubble-phase since June 2015, while Ethereum, NEM, Stellar, Ripple, Litecoin and Dash have been denoted as possessing bubble-like characteristics since September 2015. However, this latter group possess little academic evidence supporting the presence of bubbles since early 2018. An overall perspective is provided based on a robust bibliography based on large deviations of market quotes from fundamental values that can serve as a guide to policymakers, academics and investors.
This paper studies whether Bitcoin can be classified as a currency, a commodity or an investment asset. Inspired by the movement that tail behavior is the key feature to determine the property of asset and for market participants to perceive the asset, I compare the tail behavior of daily return on Bitcoin to that of daily return on the US dollar, gold and the stock market index. Based on the conditional autoregressive Value at Risk proposed by Engle and Manganelli (2004), I find the similarity of the tail behavior between Bitcoin and the dollar, the stock market index with respect to contemporaneous correlation. Additionally, the tail of stock market return turns out to be associated with the risk premium on Bitcoin’s return while Bitcoin is proved to be the element of the time-varying investment opportunity set on the basis of Merton’s (1973) ICAPM setting. These findings suggest that Bitcoin is traded as an alternative for a medium of exchange and a means of investment, being far from a commodity.
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.
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.
Dyhrberg [2016. Bitcoin, gold and the dollar - A GARCH volatility analysis. Finance Research Letters 16, 85-92] analyzes the relationship between Bitcoin, gold and the US dollar and states that Bitcoin can be classified as something in between gold and the US dollar. This paper uses the same sample and econometric models to replicate the findings and demonstrates that exact replication is not possible and that alternative statistical methods provide more reliable, yet very different results. The findings based on the original sample and an extended sample period show that Bitcoin exhibits distinctively different return, volatility and correlation characteristics compared to other assets including gold and the US dollar.
Recent innovations have made it feasible to transfer private digital currency without the intervention of an organization such as a bank. Any currency must prevent users from spending their balances more than once, which is easier said than done with purely digital currencies. Current digital currencies such as Bitcoin use peer-to-peer networks and open source software to stop double spending and create finality of transactions. This paper explains how the use of these technologies and limitation of the quantity produced can create an equilibrium in which a digital currency has a positive value. This paper also summarizes the rise of 24/7 trading on computerized markets in Bitcoin in which there are no brokers or other agents. The average monthly volatility of returns on Bitcoin is higher than for gold or a set of foreign currencies in dollars, but the lowest monthly volatilities for Bitcoin are less than the highest monthly volatilities for gold and the foreign currencies.
We exploit ideas from high-dimensional data analysis to derive new portmanteau tests that are based on the trace of the square of the mth order autocorrelation matrix. The resulting statistics are weighted sums of the squares of the sample autocorrelation coefficients that, unlike many other tests appearing in the literature, are numerically stable even when the number of lags considered is relatively close to the sample size. The statistics behave asymptotically as a linear combination of chi-squared random variables and their asymptotic distribution can be approximated by a gamma distribution. The proposed tests are modified to check for nonlinearity and to check the adequacy of a fitted nonlinear model. Simulation evidence indicates that the proposed goodness of fit tests tend to have higher power than other tests appearing in the literature, particularly in detecting long-memory nonlinear models. The efficacy of the proposed methods is demonstrated by investigating nonlinear effects in Apple, Inc., and Nikkei-300 daily returns during the 2006–2007 calendar years. The supplementary materials for this article are available online.
D'Agostino & Pearson (1973) gave percentage points of the distribution of b2 for independent observations from a common univariate normal distribution. Their results can be adequately approximated,
when the first three moments of the distribution of b2 have been determined, by fitting a linear function of the reciprocal of a X2 variable and then using the Wilson-Hilferty transformation. Evidence is presented suggesting that the same method of approximation
is satisfactory for the b2 statistic calculated from any set of linear-least-squares residuals, on the hypothesis of normal homoscedastic errors.
This paper presents a unified approach to impulse response analysis which can be used for both linear and nonlinear multivariate models. After discussing the advantages and disadvantages of traditional impulse response functions for nonlinear models, we introduce the concept of a generalized impulse response function which, we argue, is applicable to both linear and nonlinear models. We develop measures of shock persistence and asymmetric effects of shocks derived from the generalized impulse response function. We illustrate the use of these measures for a nonlinear bivariate model of US output and the unemployment rate.
Building on Koop, [Koop et al. (1996) Impulse response analysis in nonlinear multivariate models. Journal of Econometrics 74, 119–147] we propose the `generalized' impulse response analysis for unrestricted vector autoregressive (VAR) and cointegrated VAR models. Unlike the traditional impulse response analysis, our approach does not require orthogonalization of shocks and is invariant to the ordering of the variables in the VAR. The approach is also used in the construction of order-invariant forecast error variance decompositions.
The asymmetric effects of geopolitical conflict induced sentiment on the cryptocurrency, NFTs and DeFi assets
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