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Bitcoin Futures—What use are they?
Abstract
Early analysis of Bitcoin concluded that it did not meet the economic conditions to be classified as a currency. Since this conclusion, interest in Bitcoin has increased substantially. We investigate whether the introduction of futures trading in Bitcoin is able to resolve the issues that stopped Bitcoin from being considered a currency. Our analysis shows that spot volatility has increased following the appearance of futures contracts, that futures contracts are not an effective hedging instrument, and that price discovery is driven by uninformed investors in the spot market. We therefore argue that the conclusion that Bitcoin is a speculative asset rather than a currency is not altered by the introduction of futures trading.
... Since its inception in 2009, Bitcoin has carved out a unique path in the financial world, characterized by wild volatility and large price swings. Early analyzes of Bitcoin therefore concluded that it did not meet the economic conditions to be classified as money [1]. This cryptocurrency built on the basis of blockchain technology not only upended the traditional financial system, but also sparked discussions about its potential to revolutionize every aspect of our lives and contribute to technological progress. ...
... This cryptocurrency built on the basis of blockchain technology not only upended the traditional financial system, but also sparked discussions about its potential to revolutionize every aspect of our lives and contribute to technological progress. The history of Bitcoin has been a roller coaster of price volatility, which piqued the interest of investors and the public, because Bitcoin is a speculative asset rather than a currency [1]. In its first few years, cryptocurrencies existed only on the outskirts, and their value remained relatively low. ...
... Investors rely on analyses to guide decisions, employing methodologies like technical, fundamental, and sentiment analysis. Market sentiment, news, and events contribute to its high volatility, while economic instability and geopolitical factors drive its role as a potential safe-haven asset [1]. Bitcoin's price trends reflect technological advancements and adoption rates, affecting discussions about digital currencies' impact on financial systems. ...
Bitcoin, a pioneering cryptocurrency, has captivated the world with its volatility and price swings. Its price forecasts hold vital importance for investors, policymakers, and technologists. This article delves into the intricate domain of researching and predicting Bitcoin prices, grounded in diverse data exploration and stability assessment. The application of sophisticated predictive models further underscores the analysis, encompassing mathematics, statistics, and AI. Beyond financial gains, these forecasts impact regulatory decisions and technological advancements. This article converges multiple disciplines, bridging finance, technology, and data science to unveil Bitcoin's enigmatic behavior. This paper finds that the ARIMA Model can help predict the price of bitcoin. Its not just about predicting prices; it's about deciphering the potential of blockchain and reshaping our understanding of modern finance in an era of profound technological transformation. So investors should consider bitcoin as a long-term investment. The value of Bitcoin has historically appreciated over time, but short-term price fluctuations are common. Investors should avoid making impulsive decisions based on daily price movements. The second is to use reputable cryptocurrency exchanges and hardware wallets to securely store investors' bitcoins.
... Cryptocurrencies resemble more financial assets rather than currencies (Yermack, 2015;Corbet et al., 2018;Jalal et al., 2021;Duan et al., 2024) due to certain characteristics like the volatility feature Katsiampa, 2017;Ghorbel and Jeribi, 2021;Doumenis et al., 2021;Anandhabalaji et al., 2023;Dhingra et al., 2024), vulnerability to speculation (Cheah and Fry, 2015;Katsiampa, 2020;Grone et al., 2021;Ozdurak et al., 2022;Alaminos et al., 2024), persistence (Caporale et al., 2018;Abakah et al., 2020;Yaya et al., 2021), leverage effects (Phillip et al., 2018; and, heavy tail behaviour Osterrieder and Lorenz, 2017;Gkillas and Katsiampa, 2018;Phillip et al., 2018;Fung et al., 2022). Some studies argue that cryptocurrencies constitute a new investment asset class (Corbet et al., 2018a(Corbet et al., , 2018bRam, 2019;Sterley, 2019;Akbulaev and Abdulhasanov, 2023;Cascavilla, 2024). ...
... Moreover, a great deal of literature has focused on the categories or the performance of cryptocurrencies. However, most of literature focuses on Bitcoin only and pays little attention to the relationship, especially volatility connectedness among different cryptocurrencies (Corbet et al., 2018b;Mensi et al., 2021;Detthamrong et al., 2024). The reason for this could be expected that the prices of Bitcoin and other cryptocurrencies are interdependent, again due to Bitcoin's dominance, the literature on interlinkages and volatility dynamics within cryptocurrency markets still remains underexplored. ...
The cryptocurrency industry has grown erratically and at an alarming pace during its brief life. It has received massive attention from the practitioners, academicians, and especially media since Bitcoin was introduced in 2009. The current paper investigates the volatility dynamics of five major cryptocurrencies, namely Bitcoin, Ethereum, Litecoin, DASH and Ripple’s XRP. It discloses the impact of volatility in the prices of other cryptocurrencies on the Bitcoin’s price volatility. The time series of all the cryptocurrencies prices for the period 2016 to 2022 are considered and preliminary analysis highlights that there exists conditional volatility in all the cryptocurrencies which allows us to use the family of GARCH model. The research findings reveal the significant effect of asymmetric past shocks on the current volatility of Bitcoins. Further, the volatility of remaining virtual currencies are also significantly affecting the Bitcoin’s current volatility. Therefore, the present study unveils the absence of diversification benefits in the market of virtual assets as significant effect of price volatility of other digital coins has been found on the price volatility of Bitcoins. The practical implication of our study is that the findings offer new information for investors and portfolio managers, who are attracted to invest in or hedging strategies in cryptocurrencies.
... As the investigation into the regulation of cryptocurrencies has gained significance, so has the research into cryptocurrencies, specifically the Bitcoin spot price and Bitcoin futures. Most of the existing research has focused on the determinants of price discovery in the Bitcoin markets Alexander and Heck 2020;Entrop et al. 2020), the trading activity in Bitcoin (Wustenfeld and Geldner 2022;Scharnowski 2021;Dyhrberg et al. 2018), the short-term and long-term determinants of the value of Bitcoin (Dubey 2022;Mai et al. 2018;Li and Wang 2017), the market efficiency of Bitcoin (Kochling et al. 2019;Urquhart 2016), the impact of the launch of the Bitcoin futures on the Bitcoin spot (Akanksha et al. 2021;Kim et al. 2020;Liu et al. 2020), the diversification benefits and the linkage of Bitcoin with other financial assets (Wang et al. 2022;Baur and Dimpfl 2021;Qarni and Gulzar 2021;Guesmi et al. 2019), the price discovery leadership between the Bitcoin futures and spot markets (Akyildirim et al. 2020;Baur and Dimpfl 2019;Corbet et al. 2018;Kapar and Olmo 2019), the hedging properties of the Bitcoin futures (Sebastiao and Godinho 2020;Chan et al. 2019), the large contract size associated with the Bitcoin futures and its impact on trading activity (Park 2022;Akyildirim et al. 2021), the process of price discovery among the various Bitcoin trading exchanges (Pagnottoni and Dimpfl 2019), the illegal activities related to Bitcoin (Foley et al. 2019), the impact of investor attention on Bitcoin (Smales 2022;Choi 2021;Lin 2021), and the impact of investor sentiment on Bitcoin (Koutmos 2023;Mokni et al. 2022;Naeem et al. 2021;Guegan and Renault 2021). ...
... Although some shared factors influence both the Bitcoin spot prices and futures, there is evidence suggesting that the futures have a relatively greater impact on the price discovery process (Akyildirim et al. 2020;Kapar and Olmo 2019). Furthermore, there is alternative evidence, using Hasbrouck's (1995) information share methodology, indicating that the Bitcoin spot price leads and exerts an influence on the future prices with regard to price discovery (Baur and Dimpfl 2019;Corbet et al. 2018). These inconsistent findings could be a result of the relatively small data sample that is used in these studies, as well as a sharp decline in the market conditions for Bitcoin 6 , further highlighting the need to analyze these relations with larger sample periods. ...
This research investigates the function of price discovery between the Bitcoin futures and the spot markets while also analyzing the impact of investor sentiment and attention on these markets. This study utilizes various statistical models to examine the short-term and long-term relations between these variables, including the bivariate Granger causality model, the ARDL and NARDL models, and the Johansen cointegration procedure with a vector error correction mechanism. The results suggest that there is no statistical evidence of price discovery between the Bitcoin spot price and futures, and the term structure of the Bitcoin futures neither enriches nor impairs this lead lag relation. However, the study finds robust evidence of a long-run cointegrating relation between the two markets and the presence of asymmetry in them. Moreover, this research indicates that investor sentiment exhibits a lead lag relation with both the Bitcoin futures and the spot markets, while investor attention only leads to the Bitcoin spot market, without showing any lead lag relation with the Bitcoin futures. These findings highlight the crucial role of investor behavior in affecting both Bitcoin futures and spot prices.
... Two main distinct findings could be found; increase or decrease in Bitcoin spot market risk measures. Corbet et al. (2018) show that futures increase the spot market volatility and are not a good hedging instrument. Such results are also confirmed by Hale et al. (2018), who provide that spot volatility increases due to short sale pressure by pessimistic investors in the futures markets, while a partial agreement comes from Jalan et al. (2021) in distinguishing between downward effects on return and skewness, but upward results on volatility, kurtosis, and liquidity. ...
... Hence, there is a probability that both risk-averse and riskseeking investors will invest in the frontier of portfolios without futures because it is more efficient. The introduction of futures in portfolios does not help to hedge the risk of losses, confirming those of Corbet et al. (2018), Hale et al. (2018) in showing that Bitcoin futures are not a good hedge. Despite the minimal difference in volatility, the portfolio without futures is the one that minimizes the volatility and provides a higher return. ...
... In conclusion, the future of Bitcoin and cryptocurrencies is influenced by a multitude of factors, including emerging trends, technological innovations, environmental concerns, and the potential impact of CBDCs [5]. As these forces continue to shape the cryptocurrency landscape, the ongoing development and adoption of Bitcoin and other digital assets will likely transform the way we think about money, finance, and the global economy. ...
... In conclusion, the story of Bitcoin and cryptocurrencies is one of constant evolution, driven by a diverse and passionate community of developers, entrepreneurs, investors, and users. As the cryptocurrency landscape continues to mature and overcome the challenges it faces, the potential for Bitcoin and other digital assets to transform the global financial system and revolutionize the way we conduct transactions, store value, and access financial services seems increasingly likely [5]. The future of money is being reimagined, and cryptocurrencies like Bitcoin are at the forefront of this exciting transformation. ...
This research paper provides a comprehensive analysis of Bitcoin, delving into its evolution, adoption, and potential future implications. As the pioneering cryptocurrency, Bitcoin has sparked significant interest and debate in recent years, challenging traditional financial systems and introducing the world to the power of blockchain technology. This paper aims to offer a thorough understanding of Bitcoin's underlying cryptographic principles, network architecture, and consensus mechanisms, primarily focusing on the Proof-of-Work model. We also explore the economic aspects of Bitcoin, examining price fluctuations, market trends, and factors influencing its value. A detailed investigation of the regulatory landscape, including global regulatory approaches, taxation policies, and legal challenges, offers insights into the hurdles and opportunities faced by the cryptocurrency. Furthermore, we discuss the adoption of Bitcoin in various use cases, its impact on traditional finance, and its role in the growing decentralized finance (DeFi) sector. Finally, the paper addresses the future of Bitcoin and cryptocurrencies, identifying emerging trends, technological innovations, and environmental concerns. We evaluate the potential impact of central bank digital currencies (CBDCs) on Bitcoin's future, as well as the broader implications of this technology on global finance. By providing a holistic understanding of Bitcoin's past, present, and potential future, this paper aims to serve as a valuable resource for scholars, policymakers, and enthusiasts alike.
... Documents find that the volatility of Bitcoin is around eight times higher than that of stocks (Harvey, 2018). Corbet et al. (2018) and Smales (2018) have observed similar results more recently. ...
This study aims to examine the spillover effects of return and volatility between three different assets (Bitcoin, Gold, and Nasdaq) using GARCH-ARMA models. The data is taken from monthly closing prices from January 2015 to February 2024 through Investing.com. The analysis focuses on understanding how these three assets interact regarding the spillover effect of return and volatility, particularly during periods of economic uncertainty. Our findings indicate that spillover effects of return are visible from Bitcoin to Nasdaq, Nasdaq to Bitcoin, and Nasdaq to Gold. In addition, spillover effects of volatility are visible from Gold to Bitcoin, Bitcoin to Nasdaq, Nasdaq to Bitcoin, and Nasdaq to Gold. Our finding highlights the dynamic relationship between traditional and digital assets, emphasizing Bitcoin's potential role as a financial hedge likely to Gold and Nasdaq.
... It was introduced in 2009 and has gained significant attention for its potential to revolutionize the traditional financial system (Barrdear and Kumhof 2016). Unlike traditional currencies, Bitcoin is not controlled by any authority and relies on a peer-to-peer network (Corbet et al. 2018). This aspect makes it a volatile asset with prices that can experience fluctuations (Haritha and Sahana 2023). ...
Bitcoin, being one of the most triumphant cryptocurrencies, is gaining increasing popularity online and is being used in a variety of transactions. Recently, research on Bitcoin price predictions is receiving more attention, and researchers have investigated the various state-of-the-art machine learning (ML) and deep learning (DL) models to predict Bitcoin price. However, despite these models providing promising predictions, they consistently exhibit uncertainty, which cannot be adequately quantified by classical ML models alone. Motivated by the enormous success of applying Bayesian approaches in several disciplines of ML and DL, this study aims to use Bayesian methods alongside Long Short-Term Memory (LSTM) to predict the closing Bitcoin price and consequently measure the uncertainty of the prediction model. Specifically, we adopted the Monte Carlo dropout (MC-Dropout) method with the Bayesian LSTM model to quantify the epistemic uncertainty of the model's predictions and provided confidence intervals for the predicted outputs. Experimental results showed that the proposed model is efficient and outperforms other state-of-the-art models in terms of root mean square error (RMSE), mean absolute error (MAE) and R2. Thus, we believe that these models may assist the investors and traders in making critical decisions based on short-term predictions of Bitcoin price. This study illustrates the potential benefits of utilizing Bayesian DL approaches in time series analysis to improve data prediction accuracy and reliability.
... Harvey (2014) reports that the volatility of Bitcoin is over eight times higher than the overall stock market. Recent evidence obtained by Corbet et al. (2018) and Smales (2019) is consistent with that of Harvey (2014). Furthermore, Baur and Hoang (2021) indicate that price volatility in the Bitcoin market not only manifests in the long term, but is also significant on a daily basis. ...
We provide empirical evidence supporting the economic reasoning behind the impossibility of diversification benefits and the hedge attributes of cryptocurrencies remaining in force during the downside trends observed in bearish financial markets. We employ a spillover connectedness model driven by time-varying parameter vector autoregressions on daily data covering January 2018 to November 2022 to analyze spillover transmissions between conventional and digital markets, focusing on the role of stablecoin issuances. We study the stock, bond, cryptocurrency, and stablecoin markets and find very high connectedness, which varies over time in response to up/down trends in financial markets. The results show that during financial turmoil, cryptocurrencies amplify downside risks rather than serve as diversifiers. In addition to risky assets from conventional financial markets, cryptocurrencies champion the transmission of spillovers to digital and conventional markets. In contrast, changes in stablecoin issuances produce few shocks because of their pegged prices, but they facilitate investors’ switch from volatile cryptos to more stable digital instruments; that is, we observe a phenomenon designated by us as the “flight-to-cryptosafety.” We draw insightful conclusions, provoking new thinking regarding portfolio hedge strategies that could potentially benefit investors when searching for less volatile investment performance.
... They argue that the exchanges with the highest trading volumes are pronounced price leaders concerning information sharing and find the United States BTC-e and Japan's Mtgox to be price setters or leaders. Pagnottoni and Dimpfl (2019) find that price discovery largely takes place on China-based OKcoin, Baur and Dimpfl (2019), Corbet et al. (2018b) find that spot prices lead the process, and Fassas et al. (2020) and Kapar and Olmo (2019) find that Bitcoin futures are dominant. Giudici and Polinesi (2021) and Giudici and Pagnottoni (2020) find Bitstamp, Coinbase, and Bitfinex to lead and Kraken as a follower exchange. ...
As the crypto-asset ecosystem matures, the use of high-frequency data has become increasingly common in decentralized finance literature. Using bibliometric analysis, we characterize the existing cryptocurrency literature that employs high-frequency data. We highlighted the most influential authors, articles, and journals based on 189 articles from the Scopus database from 2015 to 2022. This approach enables us
to identify emerging trends and research hotspots with the aid of co-citation and car- tographic analyses. It shows knowledge expansion through authors’ collaboration
in cryptocurrency research with co-authorship analysis. We identify four major streams of research: (i) return prediction and measurement of cryptocurrency volatility, (ii) (in) efficiency of cryptocurrencies, (iii) price dynamics and bubbles in cryptocurrencies, and (iv) the diversification, safe haven, and hedging properties of Bitcoin. We conclude that highly traded cryptocurrencies’ investment features and economic outcomes are analyzed predominantly on a tick-by-tick basis. This study also provides recommenda- tions for future studies.
... He also points out that the fluctuations in the movement of cryptocurrencies expressed in dollars relatively high in dollar and that their price in dollars can be significantly different among different stock exchanges, which can cause problems in attempting to analyse price data. In short, in his opinion the cryptocurrencies are not currency but speculative property (Corbet, Lucey, Peat & Vigne, 2018). The idea that cryptocurrencies have no intrinsic value is also supported by other authors (Cheah & Fry, 2015), but there remains an open discussion of the economic value and future of use of cryptocurrencies and blockchain technology (Demir, Gozgor, Lau & Vigne et al., 2018). ...
In this paper scope and regulations on using cryptocurrencies in international commercial enterprise, banking and in financial markets the challenge of this research is to investigate usage of cryptocurrencies in the international commercial enterprise is discussed. The goals of the studies are to provide to the researchers and to practitioners and to the medical and professional public, a top-level view of cutting-edge studies on blockchain generation within the economic system and to determine the effect of the wider use of cryptocurrencies in worldwide commercial enterprise and their effect on the future of financial markets. The research turned into found out with the aid of the method description, literature analysis and conducted research. The selected examples are supplying the possibility of earning, shopping for and storing cryptocurrencies, paying with crypto money and spend money on them. In the paper we are looking for a solution to the query "What are the blessings and drawbacks of using cryptocurrencies in international fee and what is the security of the use of cryptocurrencies inside the future?" In the answer, this paper will be found in what path the use of crypts will expand in the future. Results are offering the blockchain era, although historical past technology, which would not be recognised that cryptocurrencies did no longer gain recognition, has a vibrant attitude. Conclusion offers that as long as the transaction expenses are lowered than the value of charge transactions, the rational behaviour of legal and natural individuals calls for that they should encourage the usage of cryptocurrencies amongst themselves which will lessen the fees of the transactions while paying and to triumph over the life of an intermediary.
... By providing a decentralized and secure ledger, blockchain can help streamline the verification and trading processes, reducing the risk of fraud and improving overall market integrity. Research explores the application of blockchain in carbon markets and its implications for the future of carbon pricing (Urda et al., 2020;Corbet et al., 2019). Artificial intelligence (AI) and predictive modeling offer opportunities to optimize the design and implementation of carbon pricing mechanisms. ...
The escalating threat of climate change has intensified the need for robust strategies to reduce greenhouse gas emissions. Carbon pricing mechanisms have emerged as pivotal tools in this endeavor, aiming to internalize the external cost of carbon emissions and incentivize cleaner, more sustainable practices. This review provides a comprehensive analysis of various carbon pricing mechanisms, evaluating their effectiveness and exploring the policy implications for future implementations. The review categorizes carbon pricing mechanisms into three main types: Carbon Taxes, Cap-and-Trade Systems, and Hybrid Approaches. Each mechanism is examined in terms of its characteristics, case studies from jurisdictions that have implemented them, and a critical assessment of their effectiveness and challenges. The paper evaluates the impact of carbon pricing mechanisms on economic sectors, assessing their influence on industry and their role in stimulating innovation. Furthermore, it scrutinizes the effectiveness of these mechanisms in achieving emission reductions, considering their impact on greenhouse gas emissions. Additionally, the review delves into the social and distributional impacts of carbon pricing, analyzing its equity aspects and identifying potential challenges. Drawing from implemented policies, the review extracts valuable lessons and insights. It explores the integration of carbon pricing with other climate policies, examining synergies and coordination possibilities. The discussion also includes the potential for global cooperation and harmonization, recognizing challenges and opportunities for aligning international efforts. As a forward-looking aspect, the paper explores innovative approaches to carbon pricing, considering technological advancements and market-based solutions. It concludes with policy recommendations aimed at enhancing the effectiveness of carbon pricing mechanisms, addressing challenges, and providing insights for policymakers and stakeholders. This review consolidates current knowledge on carbon pricing mechanisms, shedding light on their effectiveness and unveiling important policy implications. By synthesizing lessons learned from implemented policies, the paper contributes to the ongoing discourse on effective climate change mitigation strategies, emphasizing the role of dynamic, adaptable, and equitable carbon pricing mechanisms. Keywords: Carbon Pricing, Mechanisms, Climate change mitigation, Effectiveness, and Policy Implications.
... With the growth of new innovations and technologies in the economy market, the emergence of cryptocurrencies has tremendously changed the way of financial transaction. The financial crisis that occurred in the year 2008 has led to the development of the first cryptocurrency Bitcoin by Satoshi Nakamoto that allows end-to-end electronic transaction by groundbreaking the market capitalisation and scientific interest (Corbet et al., 2018;Jalal et al., 2021;Mezquita et al., 2023). It provides a secure online transaction through peer-to-peer connection with cryptographic algorithm in a decentralised mechanism, which denies the interference of any middleman or government body due which it is globally accessible from any location. ...
Purpose-The emergence of cryptocurrencies has tremendously changed the way of financial transactions around the world which has led to form distinct discussions in the field regarding its reliability. This paper aims to evaluate the published literatures on cryptocurrency identifying its growth, citation, prolific authors, journals, countries, active funding agencies, collaboration pattern and emerging research hotspots in the area. Design/methodology/approach-Scientometrics and Altmetrics parameters have been incorporated in the study. Literatures covered from the Scopus database searching within "Article Title, Abstract, Keywords" with keywords "cryptocurrency" OR "digital currency" OR "bitcoin" OR "Ethereum" by limiting the time range of 2013-2022, English language and journal articles only. Total 6,107 documents have been identified. The further analysis and visualisation is performed using MSExcel, VOSviewer, Biblioshiny and Tableau. Another tool, Dimension.ai is used to identify the Altmetric Attention Score. Findings-The findings reveal that the growth of research and citation rate hiked from the year 2017 till now. Elie Bouri is the top contributor, IEEE Access is the most prolific journal, China being the prolific country. Topics like Blockchain, Bitcoin, Ethereum, smart contracts, financial markets are emerging researched hotspots. The reliability of crypto market is still not clear because of its high volatility. The findings of the study will be more useful in the academia, subject specialists, research institutions, funding agencies, publishing agencies in decision-making. Originality/value-To the best of the authors' knowledge, there is no such study found considering both Scientometrics and Altmetrics approaches on cryptocurrency research with the selected time bound.
... They argue that the exchanges with the highest trading volume are the pronounced price leaders with respect to the information share and find the United States' BTC-e and Japan's Mtgox to be price setters or leaders. While Pagnottoni and Dimpfl (2019) find that price discovery largely takes place on China-based OKcoin, Baur and Dimpfl (2019) and Corbet et al. (2018b) find spot prices lead the process, whereas Fassas et al. (2020) and Kapar and Olmo (2019) find Bitcoin futures to be dominant. Giudici and Polinesi (2021) and Giudici and Pagnottoni (2020) find ...
... underlined in a few relevant articles, such as Yermack (2015). Corbet et al. (2018) analyze Bitcoin futures markets in light of the dichotomy between speculative assets versus currency. They conclude that the introduction of futures trading does not truly change the view of Bitcoin as a speculative asset rather than a currency. ...
... For instance, Corbet et al. (2018b) make a pivotal revelation about the bubble-like behavior in bitcoin and Ethereum, pointing towards an inherent instability. Simultaneously, Corbet et al. (2018c) delve into the world of bitcoin futures, asserting that despite their introduction, bitcoin continues to dwell in the realm of speculative assets rather than embracing the stability of a currency. Building upon the volatility narrative, Demir et al. (2018) take a unique angle by bringing economic policy uncertainty into the picture. ...
In the burgeoning field of bitcoin research, a cohesive understanding of how knowledge and insights have evolved over time is lacking. This study aims to address this gap through an exploration of 4123 academic articles pertaining to bitcoin. Utilizing co-word analysis and main path analysis (MPA), it uncovers key themes and seminal works that have substantially influenced the field's progression. The identified clusters, including safe haven, internet of things (IoT), proof of work (PoW), market efficiency, sentiment analysis, digital currency, and privacy, shed light on the multifaceted discourse surrounding bitcoin. The MPA, incorporating both forward and backward local paths, traces an evolving narrative, starting from an in-depth exploration of bitcoin's structure, anonymity, and contrasts against traditional financial assets. It tracks the shift in focus to broader market dynamics, volatility, speculative nature, and reactions to economic policy fluctuations. The analysis underscores the transformation of bitcoin research, from its beginnings as a decentralized, privacy-oriented currency to its role in global economics and green financing, revealing a complex narrative of an innovative financial instrument to a multifaceted entity. Implications drawn from this analysis include the need for further research on the potential integration of bitcoin within emerging technologies like AI and cybersecurity, the implications of bitcoin's interplay with traditional financial systems, and the environmental impacts of bitcoin and blockchain utilization. Overall, the current study not only enhances our understanding of the bitcoin field but also charts its dynamic evolution and stimulates further academic inquiry.
... This event led to a multitude of speculative traders becoming bullish in the Bitcoin market, knowing that the new futures contract could act as a means of safeguarding them from volatility. Ironically, Corbet et al. (2018) suggest that Bitcoin futures had no positive impact on stabilizing Bitcoin's value since spot volatility increased after the introduction of the financial derivative. Hence, this event implies that Bitcoin demand exceeded expectations, leading Bitcoin futures to become superfluous in terms of their real purpose. ...
We examine the fractal volatility and long‐range dependence of Bitcoin, Ethereum, Tether and USD Coin by employing the continuous wavelet transform, maximal overlap discrete wavelet transform and rescaled range. Our dataset consists of daily prices spanning from January 2017 through to October 2022, encapsulating pre‐ and post‐epidemic eras. Generally, our findings suggest that Tether presents the least overall volatility throughout the time‐frequency spectrum. USD Coin demonstrates ephemeral turbulence, contrary to Tether's maturity in influencing market equilibrium through token issuance and trade responses. In the post‐epidemic sample, both stablecoins indicate mean reversion, with USD Coin showing marginally better efficiency. Conversely, investment tokens display persistent clusters due to retail traders and long‐term fundamental institutions. Although both tokens illustrate multifractal volatility, Ethereum unveils more essence of self‐similarity than Bitcoin. Hence, there is no evidence that Ethereum truly duplicates Bitcoin since policy‐related events differ between them, as both return series move incongruously. Conditional dynamics signify that all cryptocurrencies, except Tether, were affected by the pandemic transition of COVID‐19 and subsequent macroeconomic news. The unconditional volatility of stablecoins evinces zero‐mean errors, antithetical to investment tokens exhibiting annual cycles. The fractal geometry suggests that investment tokens simulate one‐dimensional lines, whereas stablecoins mimic two‐dimensional planes.
... Despite the perceived importance of self-insurance by risk-averse agents under a habit-based behavioral asset pricing framework (Campbell and Cochrane, 1999), asset pricing theory remains silent on the role of the hedging factor of risk-averse agents who buy insurance protection. 1 Our study examines the hedging behavior of commercial traders (hedgers) in the Bitcoin futures market, whose trading behavior (short minus long open interest positions) can provide insights into the price formation process. Corbet et al. (2018) provide an overview on Bitcoin futures. We examine trading behavior using the weekly net position in the Commitment of Traders (COT) report issued by the U.S. Commodity Futures Trading Commission. ...
... Li et al., 2021). Bitcoin was also transformed into a commodity asset through bitcoin futures contracts launched on the Chicago Mercantile Exchange (CME) and the Chicago Board Options Exchange (CBOE) in December 2017 (Corbet et al., 2018;López-Cabarcos et al., 2021). The transformation of Bitcoin as a digital asset and commodity has encouraged investors to invest due to its increasing value and widespread utilization of Blockchain technology (Poongodi et al., 2020). ...
The study explores the most powerful between Bitcoin and Gold in boosting the Shariah Equity Index in Malaysia, the United Arab Emirates, China, Indonesia, The United States of America (USA), Japan, Oman, and Saudi Arabia in the short and long term. The study uses analysis of the first and second stages of the Granger Causality Test and Vector Error Correction Model (VECM), then Impulse Response Function (IRF) and Variance Decomposition (VDC) over the period 2013 to 2021. The finding proves that only Gold can affect the Islamic Equity Index in the short term, then Bitcoin and Gold proved to contribute equally to the Islamic Equity Index in the long term. However, Bitcoin has the potential to provide positively correlated shocks and dominate the value of Islamic equity indices in the long term. The results demonstrate that government intervention is decisive in maintaining the stability of the Shariah Equity Index from future Bitcoin threats. The study’s finding has practical implications for Islamic capital market Investors, Managers, and Authorities.
... Moreover, Liu et al. (2020) concur that the introduction of Bitcoin futures is somewhat to blame for the decline in the price of Bitcoin in 2018. Corbet et al. (2018) assert that the introduction of Bitcoin futures has increased spot market volatility and that Bitcoin futures are not a useful hedging tool since Bitcoin is a speculative asset rather than a currency. The opinions of Blau and Whitby (2019) are congruent. ...
Keywords: Bitcoin; Risk Measurement; Returns Since The Chicago Board Options Exchange (CBOE) and the Chicago Mercantile Exchange (CME) presented Bitcoin future contracts in December 2017. This study examines the risk and return relationship between Bitcoin spot and futures intraday returns. We have five-minute intraday data for the Bitcoin spot market and futures markets. This data is obtained from Bloomberg between December 10, 2017, at 17:15, and April 6, 2018, at 00:00. The Augmented Dicky Fuller (ADF) test and the GARCH in Mean (GARCH-M) Model through variance of risk and variance of volatility are used to examine this relationship. According to empirical findings, several selected models in various combinations revealed a positive relationship between risk and return for both the spot market and the futures market for Bitcoin. Volatilities and previous returns both suggested a positive, significant effect on current stocks. Based on historical Spot prices and Future prices of Bitcoin, our results indicate that the GARCH in mean (GARCH-M) model is highly helpful to explain the risk and return link in Bitcoin spot and futures intraday returns.
This paper provides a first economic analysis of liquid staking tokens, which are derivatives representing a share of staked tokens in Proof‐of‐Stake blockchains. We document substantial time‐variation in the “liquid staking basis” as given by the price difference between a derivative staking token and its underlying cryptocurrency. We find evidence that staking rewards, concentration risks, limits to arbitrage, and behavioral factors influence this basis. The liquid staking basis is wider when the yields offered by the liquid staking protocol are low relative to the alternative of staking directly, when cryptocurrency returns are more volatile, and when secondary market liquidity is low. In contrast, it is smaller when investors pay more attention to liquid staking and when investor sentiment is positive. Furthermore, liquid staking tokens contribute a significant and overall growing amount to price discovery in the underlying cryptocurrencies.
Purpose
This paper aims to improve how investors can better manage their exposure to bitcoin (BTC), given the growing importance of BTC and the accompanying high volatility of BTC. This paper tests whether altcoins can serve as safe havens and diversifiers against exposure to BTC.
Design/methodology/approach
Using daily returns of altcoins and BTC from 2014 to early 2022, this paper examines the relationship between altcoins and BTC in a GARCH regression framework.
Findings
This paper finds that altcoins act as reliable safe havens during periods of extremely negative BTC returns and provide BTC investors with diversification benefits during normal periods. The safe haven effect of altcoins is superior to that of conventional assets. This paper presents evidence that this safe haven property of altcoins can be attributed to the informational efficiency channel, which arose from the increased adoption of BTC by institutional investors.
Research limitations/implications
The study uses a data set from 2014 to early 2022. While the sample is among the largest samples in the literature on crypto assets and includes adequate BTC tail events to test the hypotheses, it may not capture more recent changes in the crypto markets.
Practical implications
The findings suggest that BTC investors can enjoy diversification and safe haven protections by including altcoins in their portfolios.
Originality/value
This paper’s focus on alternative cryptocurrencies (altcoins) as potential diversifiers and safe havens is original. The hypothesis about altcoins being better alternatives during extreme negative movements in BTC prices is a unique contribution. The test of the role of the information efficiency channel further enhances the paper’s originality.
Artificial Intelligence (AI) stands as a transformative force across business, technology, and science, yet its comprehensive impact on innovative industries remains relatively unexplored. This study delves into the interconnectedness between AI and pivotal sectors such as cryptocurrency, blockchain, metaverse, democratized banking, and Cleantech, among others. Employing the conditional autoregressive value-at-risk (CAViaR) and time-varying parameters vector autoregressions (TVP-VAR) methods, we scrutinize daily data spanning from June 1, 2018, to October 11, 2023, encompassing 12 stock indices representing each industry. Our findings unveil a strong contagion effect from AI to other innovative sectors, with the exception of Cleantech, which appears to have decoupled from the AI surge. Notably, democratized banking and the metaverse emerge as key recipients of this contagion. Examination of tail-risk spillovers highlights AI as one of the most influential risk transmitters during market tumult, while cryptocurrency and blockchain consistently function as net risk receivers throughout the sample period. The implications of these findings are multifaceted, offering substantive insights into the risk profiles of these critical innovative sectors. Investors and regulatory bodies stand to benefit significantly from this analysis, as it illuminates potential avenues for portfolio diversification and deepens understanding of contagion mechanisms within these evolving industries.
The introduction of regulated CME futures contracts on Bitcoin in 2017 raised an expectation that cryptocurrencies would become part of mainstream financial markets. This also heightened links between traditional markets and Bitcoin, implying that the cryptocurrency would be subject to systematic spillovers. This paper uses high‐frequency data to examine whether Bitcoin basis risk is linked to investor sentiment from established financial markets. Our findings indicate that extreme investor sentiment, as reflected by the tail risk in various volatility indices, including the VIX, consistently correlates with a negative Bitcoin basis, where Bitcoin futures prices are lower than spot prices. Fluctuations significantly influence this relationship in the trading volume of Bitcoin futures and are more pronounced during periods of substantial unexpected inflation and deflation. These results underline the complex dynamics between market sentiment and cryptocurrency pricing, offering insights with substantial implications for investors and policymakers.
The annual electricity consumption of cryptocurrency mining has witnessed significant growth in recent years, fueled by an increase in market participation and the escalating complexity of the mining process. This has led to carbon emissions that exceed those generated by several developed nations. The growing impact of global warming and rising environmental concerns has brought increased scrutiny to Bitcoin’s energy consumption, particularly its potential to influence prices in unforeseen ways. This study investigates multifractal behavior in the cross-correlation of the Cambridge Bitcoin Electricity Consumption Index (CBECI) with both conventional and renewable energy prices using the Multifractal Detrended Cross-Correlation Analysis (MFDCCA) method. For renewable energy, we considered WilderHill Clean Energy, S&P Global Eco, S&P Global Clean Energy, OMX Solar Energy, and OMX Renewable Energy Index. For conventional energy, we considered the daily prices of WTI crude oil, Brent oil, heating oil, Newcastle coal, and natural gas. The daily price data range from 2 April 2013, to 29 August 2023, encompassing 1709 observations. Additionally, we employed a rolling window analysis to uncover the time-varying dynamics in the cross-correlations and persistence levels between Bitcoin electricity consumption and energy prices. The findings reveal the existence of a cross-correlation between the CBECI and energy markets. Overall, the CBECI exhibits a persistent cross-correlation with both energy markets; however, it is more persistent in the fossil fuel market, specifically in the coal market. These findings suggest the incorporation of dynamic changes in the CBECI in portfolio management for effective risk management strategies.
Artificial Intelligence (AI) stands as a transformative force across business, technology, and science, yet its comprehensive impact on innovative industries remains relatively unexplored. This study delves into the interconnectedness between AI and pivotal sectors such as cryptocurrency, blockchain, metaverse, democratized banking, and Cleantech, among others. Employing the conditional autoregressive value-at-risk (CAViaR) and time-varying parameters vector autoregressions (TVP-VAR) methods, we scrutinize daily data spanning from June 1, 2018, to October 11, 2023, encompassing 12 stock indices representing each industry. Our findings unveil a strong contagion effect from AI to other innovative sectors, with the exception of Cleantech, which appears to have decoupled from the AI surge. Notably, democratized banking and the metaverse emerge as key recipients of this contagion. Examination of tail-risk spillovers highlights AI as one of the most influential risk transmitters during market tumult, while cryptocurrency and blockchain consistently function as net risk receivers throughout the sample period. The implications of these findings are multifaceted, offering substantive insights into the risk profiles of these critical innovative sectors. Investors and regulatory bodies stand to benefit significantly from this analysis, as it illuminates potential avenues for portfolio diversification and deepens understanding of contagion mechanisms within these evolving industries.
From the launch of Bitcoin till the present moment, cryptocurrency market had expanded continuously, gaining more and more influence over the global economy with each passing year. Yet, the events of 2020 marked a new phase for the cryptocurrency ecosystem, which has experienced a significant increase in size and complexity. The third halving cycle that led to an increase in cryptocurrency prices, the beginning of pandemic, and afterward inflation and economic uncertainty made Bitcoin an attractive asset for both retail and institutional investors. Although the liquidity of the cryptocurrency assets increased, their volatile nature is still persistent, causing mixed views on its status. While crypto-enthusiasts are perceiving it as a worthwhile investment with novel economic properties, the more skeptic participants consider it only a speculative asset with a transitory presence. The absence of a consensus on this topic has attracted the interest of the academic community, which aims to analyze whether cryptocurrencies display economic properties. A keystone characteristic for considering cryptocurrency an economic asset is the lack of price manipulation. In this respect, numerous papers have investigated the efficiency of the cryptocurrency market. Even though the results are mixed, a large body of studies indicate that the efficiency of the crypto-assets market varies, increasing from period to period. However, most of the papers focus on testing information efficiency only on the spot market. Thus, the objective of this study is to analyze whether the futures cryptocurrency market is efficient. In this regard, the futures prices for Bitcoin from 2018 to 2022 are used. On them, a battery of tests is applied, which investigate several statistical properties that can assess the efficiency hypothesis. Furthermore, under the assumption of efficient market hypothesis the spot and future prices are supposed to move together. In the contrary case, the efficient market hypothesis is rejected. Thus, the property is evaluated from a double perspective by using statistical tests and evaluating the relation between the spot and the future price.
Résumé :
Ce papier se concentre sur la technologie blockchain et son implémentation au système financier et aux marchés financiers en particulier. En outre, il vise à identifier et étudier les applications significatives de cette technologie dans le marché financier, ainsi que fournir une analyse précise à ses avantages et menaces probables sur le système financier dans l’ensemble. La blockchain est actuellement en pleine essor au sein des marchés financiers et leurs structures avec les avantages significatifs des registres distribués. Les monnaies virtuelles, Altchains et Colored coins, smart contracts, contrats à terme crypto, ICO, tokens, la décentralisation et les modèles de gouvernance partagées sont tous des éléments originellement issus de la révolution blockchain. Mais, cet évolution non réglementée pourrait constituer une menace sérieuse pour la stabilité financière si les régulateurs n'agissent pas a déclaré un organisme de surveillance mondial.
Mots clés : blockchain, système financier, marchés financiers, avantages.
Abstract:
This paper focuses on blockchain technology and its implementation in the financial system and financial markets in particular. In addition, it aims to identify and study the significant applications of this technology in the financial market, as well as provide an accurate analysis of its likely benefits and threats facing financial system as a whole. Blockchain is currently booming within financial markets and their structures with the significant benefits of distributed ledgers. Crypto- currencies, Altchains and Colored coins, smart contracts, crypto-futures, ICOs, tokens, decentralization and shared governance models are all elements originally from the blockchain revolution. However, this unregulated development could pose a serious threat to financial stability if regulators do not act to state a global oversight body.
Keywords: blockchain, financial system, financial markets, benefits.
Cryptocurrencies provide a unique opportunity to identify how derivatives impact spot markets. They are fully fungible and trade across multiple spot exchanges at different prices, and futures contracts were selectively introduced on bitcoin (BTC) exchange rates against the U.S. dollar (USD) in December 2017. Following the futures introduction, we find a significantly greater increase in cross-exchange price synchronicity for BTC–USD relative to other exchange rate pairs as demonstrated by an increase in price correlations and a reduction in arbitrage opportunities and volatility. We also find support for an increase in price efficiency, market quality, and liquidity. The evidence suggests that futures contracts allowed investors to circumvent arbitrage frictions associated with short-sale constraints, arbitrage risk associated with block confirmation time, and market segmentation. Overall, our analysis supports the view that the introduction of BTC–USD futures was beneficial to the bitcoin spot market by making the underlying prices more informative.
This paper was accepted by Lin William Cong, special issue of Management Science: blockchains and crypto economics.
Funding: The authors acknowledge financial support from the Global Risk Institute. P. Augustin acknowledges financial support from the Canadian Derivatives Institute and the Social Sciences and Humanities Research Council Canada. The paper has benefited significantly from a fellow visit of P. Augustin at the Center for Advanced Studies Foundations of Law and Finance funded by the German Research Foundation, project FOR 2774, and from a visiting position of P. Augustin at the finance department of the University of Luxembourg facilitated through the Inter Mobility Programme of the Luxembourg National Research Fund.
Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4900 .
Anomalies, which are incompatible with the efcient market hypothesis and mean a
deviation from normality, have attracted the attention of both fnancial investors and
researchers. A salient research topic is the existence of anomalies in cryptocurrencies, which have a diferent fnancial structure from that of traditional fnancial markets. This study expands the literature by focusing on artifcial neural networks to compare diferent currencies of the cryptocurrency market, which is hard to predict. It aims to investigate the existence of the day-of-the-week anomaly in cryptocurrencies with feedforward artifcial neural networks as an alternative to traditional methods. An
artifcial neural network is an efective approach that can model the nonlinear and
complex behavior of cryptocurrencies. On October 6, 2021, Bitcoin (BTC), Ethereum
(ETH), and Cardano (ADA), which are the top three cryptocurrencies in terms of market value, were selected for this study. The data for the analysis, consisting of the daily closing prices for BTC, ETH, and ADA, were obtained from the Coinmarket.com website from January 1, 2018 to May 31, 2022. The efectiveness of the established models was
tested with mean squared error, root mean squared error, mean absolute error, and
Theil’s U1, and R2 OOS was used for out-of-sample. The Diebold–Mariano test was used to statistically reveal the diference between the out-of-sample prediction accuracies of the models. When the models created with feedforward artifcial neural networks are examined, the existence of the day-of-the-week anomaly is established for BTC, but no day-of-the-week anomaly for ETH and ADA was found.
The change point model framework introduced in Hawkins, Qiu, and Kang (2003) and Hawkins and Zamba (2005a) provides an effective and computationally efficient method for detecting multiple mean or variance change points in sequences of Gaussian random variables, when no prior information is available regarding the parameters of the distribution in the various segments. It has since been extended in various ways by Hawkins and Deng (2010), Ross, Tasoulis, and Adams (2011), Ross and Adams (2012) to allow for fully nonparametric change detection in non-Gaussian sequences, when no knowledge is available regarding even the distributional form of the sequence. Another extension comes from Ross and Adams (2011) and Ross (2014) which allows change detection in streams of Bernoulli and Exponential random variables respectively, again when the values of the parameters are unknown.
This paper describes the R package cpm, which provides a fast implementation of all the above change point models in both batch (Phase I) and sequential (Phase II) settings, where the sequences may contain either a single or multiple change points.
The analysis of data streams requires methods which can cope with a very high volume of data points. Under the requirement that algorithms must have constant computational complexity and a fixed amount of memory, we develop a framework for detecting changes in data streams when the distributional form of the stream variables is unknown. We consider the general problem of detecting a change in the location and/or scale parameter of a stream of random variables, and adapt several nonparametric hypothesis tests to create a streaming change detection algorithm. This algorithm uses a test statistic with a null distribution independent of the data. This allows a desired rate of false alarms to be maintained for any stream even when its distribution is unknown. Our method is based on hypothesis tests which involve ranking data points, and we propose a method for calculating these ranks online in a manner which respects the constraints of data stream analysis.
This paper analyzes the prediction power of the economic policy uncertainty (EPU) index on the daily Bitcoin returns. Using the Bayesian Graphical Structural Vector Autoregressive model as well as the Ordinary Least Squares and the Quantile-on-Quantile Regression estimations, the paper finds that the EPU has a predictive power on Bitcoin returns. Fundamentally, Bitcoin returns are negatively associated with the EPU. However, the effect is positive and significant at both lower and higher quantiles of Bitcoin returns and the EPU. In the light of these findings, the paper concludes that Bitcoin can serve as a hedging tool against uncertainty.
This paper is the result of a crowdsourced effort to surface perspectives on the present and future direction of international finance. The authors are researchers in financial economics who attended the INFINITI 2017 conference in the University of Valencia in June 2017 and who participated in the crowdsourcing via the Overleaf platform. This paper highlights the actual state of scientific knowledge in a multitude of fields in finance and proposes different directions for future research.
We investigate which of the two main centers of gold trading-the London spot market and the New York futures market-plays a more important role in setting the price of gold. Using intraday data during a 17-year period, we find that although both markets contribute to price discovery, the New York futures play a larger role on average. This is striking given the volume of gold traded in New York is less than a tenth of the London spot volume, and illustrates the importance of market structure on the process of price discovery. We find considerable variation in price discovery shares both intraday and across years. The variation is related to the structure and liquidity of the markets, daylight hours, and macroeconomic announcements that affect the price of gold. We find that a major upgrade in the New York trading platform reduces the relative amount of noise in New York futures prices, reduces the impact of daylight hours on the location of price discovery, but does not greatly increase the speed with which information is reflected in prices.
The study of cointegration in large systems requires a reduction of their dimensionality. To achieve this, we propose to obtain the 1(1) common factors in every subsystem and then analyze cointegration among them. In this article, a new way of estimating common long-memory components of a cointegrated system is proposed. The identification of these 1(1) common factors is achieved by imposing that they be linear combinations of the original variables Xt, and that the error-correction terms do not cause the common factors at low frequencies. Estimation is done from a fuliy specified error-correction model, which makes it possible to test hypotheses on the common factors using standard chi-squared tests. Several empirical examples illustrate the procedure.
A market is typically considered to dominate price discovery if it is the first to reflect new information about the fundamental value. Our simulations indicate that common price discovery metrics - Hasbrouck information share and Harris-McInish-Wood component share - are only consistent with this view of price discovery if the price series have equal levels of noise, including microstructure frictions and liquidity. If the noise in the price series differs, the information and component shares measure a combination of leadership in impounding new information and relative avoidance of noise, to varying degrees. A third price discovery metric the 'information leadership share' uses the information share and the component share together to identify the price series that is first to impound new information. This third metric is robust to differences in noise levels and therefore correctly attributes price discovery in a wider range of settings. Using four recent empirical studies of price discovery we show that the choice and interpretation of price discovery metrics can have a substantial impact on conclusions about price discovery.
Amid its rapidly increasing usage and immense public interest the subject of Bitcoin has raised profound economic and societal issues. In this paper we undertake economic and econometric modelling of Bitcoin prices. As with many asset classes we show that Bitcoin exhibits speculative bubbles. Further, we find empirical evidence that the fundamental price of Bitcoin is zero.
In 2007 futures contracts were introduced based upon the listed real estate market in Europe. Following their launch they have received increasing attention from property investors, however, few studies have considered the impact their introduction has had. This study considers two key elements. Firstly, a traditional Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, the approach of Bessembinder & Seguin (1992) and the Gray’s (1996) Markov-switching-GARCH model are used to examine the impact of futures trading on the European real estate securities market. The results show that futures trading did not destabilize the underlying listed market. Importantly, the results also reveal that the introduction of a futures market has improved the speed and quality of information flowing to the spot market. Secondly, we assess the hedging effectiveness of the contracts using two alternative strategies (naïve and Ordinary Least Squares models). The empirical results also show that the contracts are effective hedging instruments, leading to a reduction in risk of 64 %.
Previous literature on price discovery in stock index futures and spot markets neglects the role of different investor groups. This study relates time-varying spot-futures linkages studied within a VECM-DCC-GARCH framework to changes in the investor structure of the futures market over time. Empirical results suggest that during the dominance of presumably uninformed private investors, the futures market does not contribute to price discovery. By contrast, there is evidence of information flows from futures to spot markets and a significant increase in conditional correlation between both markets as institutional investors' share in trading volume increases. We derive implications for the design of emerging futures markets. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark31:282–306, 2011
Using intraday data, this study investigates the contribution to the price discovery of Euro and Japanese Yen exchange rates in three foreign exchange markets based on electronic trading systems: the CME GLOBEX regular futures, E-mini futures, and the EBS interdealer spot market. Contrary to evidence in equity markets and more recent evidence in foreign exchange markets, the spot market is found to consistently lead the price discovery process for both currencies during the sample period. Furthermore, E-mini futures do not contribute more to the price discovery than the electronically traded regular futures. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 29:137–156, 2009
Most research on hedging has disregarded both the long-run cointegrating relationship between financial assets and the dynamic nature of the distributions of the assets. This study argues that neglecting these affects the hedging performance of the existing models and proposes an alternative model that accounts for both of them. Using a bivariate error correction model with a GARCH error structure, the risk-minimizing futures hedge ratios for several currencies are estimated. Both within-sample comparisons and out-of-sample comparisons reveal that the proposed model provides greater risk reduction than the conventional models. Furthermore, a dynamic hedging strategy is proposed in which the potential risk reduction is more than enough to offset the transactions costs for most investors.
This paper investigates the effects of the long-run relationship between stock cash index and futures index on the hedging effectiveness of six stock futures markets. Effectiveness of five different hedging ratios depending on different estimation procedures is investigated. The unhedged, the traditional hedge and the minimum variance hedge ratios are all constant while the bivariate GARCH and GARCH-X hedge ratios are time varying. The effectiveness of the hedge ratio is compared by investigating the total sample and the out-of-sample performance of the five ratios. The total sample period consists of daily returns from January 1990 to December 1999. Two out-of-sample periods used are from January 1998 to December 1999 (2 years) and from January 1999 to December 1999 (1 year). Results show that the time-varying hedge ratio outperforms the constant hedge ratio.
We analyze the structural determinants of two widely used measures of price discovery between multiple markets that trade closely related securities. Using a structural cointegration model, we show that both the information share (IS) and component share (CS) measures account for the relative avoidance of noise trading and liquidity shocks, but that only the IS can provide information on the relative informativeness of individual markets. In particular, the IS of one market is higher if it incorporates more new information and/or impounds less liquidity shocks. Use of the CS in conjunction with the IS can help sort out the confounding effects of the two types of shocks. Furthermore, we find that the IS only accounts for the immediate (one-period) responses of market prices to the news innovation, which implies that the IS estimates based on high sampling frequencies may be distorted by transitory frictions and may miss important price discovery dynamics.
When homogeneous or closely-linked securities trade in multiple markets, it is often of interest to determine where price discovery (the incorporation of new information) occurs. This article suggests an econometric approach based on an implicit unobservable efficient price common to all markets. The information share associated with a particular market is defined as the proportional contribution of that market's innovations to the innovation in the common efficient price. Applied to quotes for the thirty Dow stocks, the technique suggests that the preponderance of the price discovery takes place at the New York Stock Exchange (a median 92.7 percent information share). Copyright 1995 by American Finance Association.
New York University. I would like to thank the Interactive Data Corporation for providing data and computer support for this project and my colleagues in the NYU Finance Department for helpful comments and suggestions. Thanks also to Steven Freund for able research assistance.
Stock index futures trading and volatility in international equity markets
- Gulen
Price discovery in the foreign currency futures and spot market
- Rosenberg