Saiful Izzuan Hussain, Nurulkamal Masseran, Nadiah Ruza, Muhammad Aslam Mohd Safari
Abstract Extreme value theory(EVT) has been used to study the frequency and probability related to extreme situations in finance. This approach focuses on the extreme values and able to provide a better estimation for risk models. In this study, Generalized Pareto Distribution (GPD) is employed to model daily extreme returns in the Bitcoin market from 2017 to 2019. These periods have witnessed three phases of extreme volatility for the cryptocurrency market. The returns level for the Bitcoin range between 17.011 and 18.746. The results demonstrate heavy tail and finite tail distribution characteristics for the tails. The findings provide a better understanding of the tails’ behaviour in the cryptocurrency market and help investors to make a financial decision.
Khalid Khan, Jiluo Sun, Sinem Derindere Köseoğlu, Ashfaq U. Rehman
This study examines the relationship between global economic policy uncertainty (GEPU) and bitcoin prices (BCP) employing the rolling window method. The full sample test shows that there is no causality between GEPU and BCP. However, the full sample causal relationship between the variables can be different when considering structural changes. The finding of the rolling window test indicates that there is causality in different subsamples. It has found both positive and negative bidirectional causalities between GEPU and BCP across various subsamples. The decision makers need to accelerate the development of blockchain technology that can be used for hedging and portfolio diversification. Moreover, enacting laws and regulations on state interventions and prohibitions ensures investor confidence. Information about policy changes should be incorporated into portfolio selection to avoid random market fluctuations. Its unregulated nature makes it more turbulent in the short term and has undergone sudden changes, so investors should be able to obtain comprehensive information about global economic and policy changes. Policy makers should ensure investor confidence by making legal regulations on state interventions and prohibitions.
Recently, the world of cryptocurrencies has experienced an undoubted increase in interest. Since the first cryptocurrency appeared in 2009 in the aftermath of the Great Recession, the popularity of digital currencies has, year by year, risen continuously. As of February 2021, there are more than 8525 cryptocurrencies with a market value of approximately USD 1676 billion. These particular assets can be used to diversify the portfolio as well as for speculative actions. For this reason, investigating the daily volatility and co-volatility of cryptocurrencies is crucial for investors and portfolio managers. In this work, the interdependencies among a panel of the most traded digital currencies are explored and evaluated from statistical and economic points of view. Taking advantage of the monthly Google queries (which appear to be the factors driving the price dynamics) on cryptocurrencies, we adopted a mixed-frequency approach within the Dynamic Conditional Correlation (DCC) model. In particular, we introduced the Double Asymmetric GARCH–MIDAS model in the DCC framework.
In recent years, cryptocurrency or virtual currency is becoming an essential medium of exchange in consumer and domestic trading. Nevertheless, the trading values of cryptocurrency compared to real money are very uncertain and can change dramatically. This article is aimed to assess the uncertainty or volatility of cryptocurrencies, mostly on Bitcoin. In the digital currencies market, Bitcoin is a widely accepted currency. Other digital currencies of the market may influence Bitcoin. For example, Ethereum, Litecoin, Zcash, Monero, Dash and Ripple have a positive impact on Bitcoin. Previous research only focuses on Bitcoin and other markets such as stock markets, energy markets, and exchange rates. However, here we focus on interlinkages and volatility dynamics within cryptocurrency markets by applying some econometrics models. In this article, we have shown that the relationship between Bitcoin and other currencies can be modelled in the ARCH, GARCH, VAR and MGARCH framework. Forecast values of the GARCH (3,3) model are given very close to the original data. VAR stability result shows that the model is stable. Using the CCC, VCC, and DCC of the MGARCH model on daily returns from 1st January 2017 to 15th March 2019, we found significant volatility and strong correlations between the variables.
Dirk Gerritsen, Rick A.C. Lugtigheid, Thomas Walther
Using a hand-collected dataset containing bullish, neutral, and bearish predictions for Bitcoin published by crypto experts, we show that neutral and bearish predictions are followed by negative abnormal returns whereas bullish predictions are not associated with nonzero abnormal returns. Based on all outstanding predictions, we compute prediction revisions relative to (i) the latest issued prediction and (ii) the outstanding consensus prediction. Downward revisions are followed by negative abnormal returns. We conclude that crypto experts are skilled information intermediaries on the Bitcoin market.
This study explores the bubble behavior in the prices of top five cryptocurrencies (i.e., Bitcoin, Ethereum, Ripple, Stellar, and Tether) using daily data of the closing level at the COVID-19 pandemic, covering the period from January 2, 2020 to January 2, 2021. The testing procedure of the bubble behavior in selected cryptocurrencies prices is investigated by two methodologies. Those covers the test statistics originated by the Supremum Augmented Dickey-Fuller (SADF) (Phillips et al., 2011) and Generalized Supremum Augmented Dickey-Fuller (GSADF) (Phillips et al., 2015) to define several bubble periods. The empirical results emphasize that bubble behavior is not a diverse and stable feature of Bitcoin, Ethereum, Ripple, and Stellar prices, except the Tether prices, which point out the emergence of a potential crisis in the digital assets market through an increasing degree of financial instability.
This discussion applies quantitative finance methods and economic arguments to cryptocurrencies in general and bitcoin in particular -- as there are about $10,000$ cryptocurrencies, we focus (unless otherwise specified) on the most discussed crypto of those that claim to hew to the original protocol (Nakamoto 2009) and the one with, by far, the largest market capitalization. In its current version, in spite of the hype, bitcoin failed to satisfy the notion of "currency without government" (it proved to not even be a currency at all), can be neither a short nor long term store of value (its expected value is no higher than $0$), cannot operate as a reliable inflation hedge, and, worst of all, does not constitute, not even remotely, a safe haven for one's investments, a shield against government tyranny, or a tail protection vehicle for catastrophic episodes. Furthermore, bitcoin promoters appear to conflate the success of a payment mechanism (as a decentralized mode of exchange), which so far has failed, with the speculative variations in the price of a zero-sum maximally fragile asset with massive negative externalities. Going through monetary history, we show how a true numeraire must be one of minimum variance with respect to an arbitrary basket of goods and services, how gold and silver lost their inflation hedge status during the Hunt brothers squeeze in the late 1970s and what would be required from a true inflation hedged store of value.
We apply quantitative finance methods and economic arguments to cryptocurrencies in general and bitcoin in particular -- as there are about $10,000$ cryptocurrencies, we focus (unless otherwise specified) on the most discussed crypto of those that claim to hew to the original protocol (Nakamoto, 2009) and the one with, by far, the largest market capitalization.
In its current version, in spite of the hype, bitcoin failed to satisfy the notion of without (it proved to not even be a currency at all), can be neither a short nor long term store of value (its expected value is no higher than $0$), cannot operate as a reliable inflation hedge, and, worst of all, does not constitute, not even remotely, a safe haven for one's investments, a shield against government tyranny, nor a tail protection vehicle for catastrophic episodes.
Furthermore, there appears to be an underlying conflation between the success of a payment mechanism (as a decentralized mode of exchange), which so far has failed, and the speculative variations in the price of a zero-sum asset with massive negative externalities.
Going through monetary history, we also show how a true numeraire must be one of minimum variance with respect to an arbitrary basket of goods and services, how gold and silver lost their inflation hedge status during the Hunt brothers squeeze in the late 1970s and what would be required from a true inflation hedged store of value.
Paulo Rupino da Cunha, Paulo Melo, Hélder Sebastião
We analyze the path from cryptocurrencies to official Central Bank Digital Currencies (CBDCs), to shed some light on the ultimate dematerialization of money. To that end, we made an extensive search that resulted in a review of more than 100 academic and grey literature references, including official positions from central banks. We present and discuss the characteristics of the different CBDC variants being considered—namely, wholesale, retail, and, for the latter, the account-based, and token-based—as well as ongoing pilots, scenarios of interoperability, and open issues. Our contribution enables decision-makers and society at large to understand the potential advantages and risks of introducing CBDCs, and how these vary according to many technical and economic design choices. The practical implication is that a debate becomes possible about the trade-offs that the stakeholders are willing to accept.
This study is an integrated survey of GARCH methodologies applications on 67 empirical papers that focus on cryptocurrencies. More sophisticated GARCH models are found to better explain the fluctuations in the volatility of cryptocurrencies. The main characteristics and the optimal approaches for modeling returns and volatility of cryptocurrencies are under scrutiny. Moreover, emphasis is placed on interconnectedness and hedging and/or diversifying abilities, measurement of profit-making and risk, efficiency and herding behavior. This leads to fruitful results and sheds light on a broad spectrum of aspects. In-depth analysis is provided of the speculative character of digital currencies and the possibility of improvement of the risk–return trade-off in investors’ portfolios. Overall, it is found that the inclusion of Bitcoin in portfolios with conventional assets could significantly improve the risk–return trade-off of investors’ decisions. Results on whether Bitcoin resembles gold are split. The same is true about whether Bitcoins volatility presents larger reactions to positive or negative shocks. Cryptocurrency markets are found not to be efficient. This study provides a roadmap for researchers and investors as well as authorities.
Hashem Abdullah AlNemer, Besma Hkiri, Muhammad Asif Khan
This study attempts to investigate the nexus between investor sentiment and cryptocurrencies prices. Our empirical investigation merges bivariate and multivariate wavelet tools to examine the investor sentiment nexus to inter-cryptocurrencies prices. The study outcomes show that the Sentix Investor Confidence index provides significant information in explaining long-term changes in Bitcoin and Litecoin prices. Moreover, the findings generated from the multiple wavelet coherence illustrate the simultaneous contribution of cryptocurrencies and the Sentix Investor Confidence index in explaining the Bitcoin index movement across frequencies and over horizons, especially during bubble burst periods. The study also suggests a time-dependent relationship of Bitcoin prices with alternative cryptocurrencies and the Sentix Investor Confidence index, mostly pronounced during the Bitcoin bubble. We discuss our results using GSV-based investor sentiment. Our findings remain robust and confirm the strong predictive power of investor sentiment in cryptocurrencies price movements over time and across scales.
The volatility of bitcoin (BTC) and time horizon is the center point for investment decisions. However, attention is not often drawn to the relationship between BTC and equity indices. Thus, the purpose of this paper is to investigate the volatility and time frequency domain of BTC with stock markets.
This study examines the interaction of Bitcoin with fiat currencies of three developed (euro, pound sterling and yen) and three emerging (yuan, rupee and ruble) market economies. Empirical investigations are executed through symmetric, asymmetric and non-linear causality tests, and Markov regime-switching regression (MRSR) analysis. Results show that Bitcoin has a causal nexus with Chinese yuan and Indian rupee for price and various return components. The MRSR analysis justifies these findings by demonstrating the presence of interaction in contractionary regimes. Accordingly, it can be stated that when markets display a downward trend, appreciation of the Chinese yuan and Indian rupee positively and strongly affects the value of Bitcoin, possibly due to the market timing. The MRSR analysis also exhibits a transition from a tranquil to a crisis regime in March 2020 because of the pandemic. However, a shorter duration spent in the crisis regime in 2020 indicates the limited and relatively less harmful effect of the pandemic on the cryptocurrency market when compared to the turmoil that occurred in 2018.
\nAmaç – Çalışmada önemli fiyat dalgalanmalarına sahip kripto paralardan en yüksek işlem hacmine sahip olan Bitcoin’in volatilite dinamiklerini tespit etmek için Bitcoin getirilerinin yükseliş/kazandıran ve düşüş/kaybettiren rejimleri, rejim geçiş olasılıkları ve rejimde kalma sürelerinin tespit edilmesi amaçlanmıştır. Yöntem – Çalışmada Bitcoin getirilerinin yükseliş/kazandıran ve düşüş/kaybettiren rejimleri, rejim geçiş olasılıkları ve rejimde kalma süreleri hem değişimlerin hem de rejim geçiş olasılıklarının hesaplanmasına imkan veren Markov Rejim Değişim Modeli kullanılmıştır. Bulgular – Çalışma kapsamında çalışmaya konu periyotta Bitcoin getiri serisi için en uygun modelin üç rejimli MSIH(3)-AR(1) modeli olduğu tespit edilmiştir. Modele ilişkin analizler yapıldığında ise söz konusu modelin doğrusal modele göre daha güçlü sonuçlar verdiği görülmektedir. Üç rejimden oluşan modelde katsayısı negatif olan rejim 1 daralma rejimi dönemini, katsayıları pozitif olan rejim 2 geçiş ve rejim 3 ise genişleme rejimi dönemini göstermektedir. Bitcoin getiri serisinin bir rejimdeyken bir sonraki dönemde aynı rejimde kalma olasılıkları yüksek iken bir sonraki dönemde özellikle rejim 1’den diğer rejimlere, diğer rejimlerden ise rejim 1’e geçiş olasılıklarının düşük olduğu tespit edilmiştir. Tartışma – Çalışma kapsamında ele alınan dönem için Bitcoin getirilerinin rejim kalıcılığının yüksek olduğu tespit edilmiştir. Bu bağlamda yatırımcıların, Bitcoin getirilerinin incelenen dönemde hangi rejimde olduğunu bilmesi durumunda, bir sonraki dönemde bu rejimde kalma olasılığını tahminini yaparak yatırım kararını buna göre verme imkanı bulunmaktadır. Bununla birlikte ortalama rejimlerde kalma sürelerinin düşük olduğu göz önüne alındığında özellikle Bitcoin’i portföylerinde bulunduran aktif yatırımcıların sürekli olarak bu aracın rejim değişimlerini takip etmesi durumunda portföylerinin faydasını arttırma imkanı yakalayacağı görülmektedir.\n
The worth of digital currencies is increasing due to its proposed advantages and profits. Though decentralized, these digital currencies can be bought with digital wallets using cryptocurrency platform. Efficient Market Hypothesis (EMH) suggests fundamentals for understanding of financial markets however the opponents believe that this theory is incompetent in explaining the functioning of the markets. EMH is not a perfect model nevertheless it provides a concrete base for the analysis of capital markets. EMH’s weak version is utilized for this study. This research compares three top cryptocurrencies- Bitcoin, Ethereum and Litecoin to analyze their long-range memory effect to check the market efficiency and also to estimate the volatility for further investments in different cryptocurrencies. Generalized Hurst exponent methodology is applied to examine long range memory in selected cryptocurrencies market. Daily data from 17th September 2015 till 17th October 2018 is used in this study. It was found that: (i) Long memory exists in the selected cryptocurrencies; (ii) Ethereum market is more persistent than Bitcoin and Litecoin as its Hurst exponent is more than the other cryptocurrencies. These findings can be a source of assistance for the policy makers and investors while making prudent decisions regarding investment in emerging cryptocurrencies market.
We investigated the differential impacts of a new Twitter-based Market Uncertainty index (TMU) and variables for Bitcoin before and during the COVID-19 pandemic. Results showed that TMU is a leading indicator of Bitcoin returns only during the pandemic, and the effect of the TMU on Bitcoin’s conditional volatility is significantly greater during the pandemic. Furthermore, during the pandemic, the uncertainty content of people’s tweets is impacted by the highly salient Bitcoin market. Taken together, our results suggest that the information contained in virtual communities such as Twitter have a much larger impact on cryptocurrency markets following COVID-19.
This paper focuses on forecasting the price of Bitcoin, motivated by its market growth and the recent interest of market participants and academics. We deploy six machine learning algorithms (e.g., Artificial Neural Network, Support Vector Machine, Random Forest, k-Nearest Neighbours, AdaBoost, Ridge regression), without deciding a priori which one is the ‘best’ model. The main contribution is to use these data analytics techniques with great caution in the parameterization, instead of classical parametric modelings (AR), to disentangle the non-stationary behavior of the data. As soon as Bitcoin is also used for diversification in portfolios, we need to investigate its interactions with stocks, bonds, foreign exchange, and commodities. We identify that other cryptocurrencies convey enough information to explain the daily variation of Bitcoin’s spot and futures prices. Forecasting results point to the segmentation of Bitcoin concerning alternative assets. Finally, trading strategies are implemented.
Christoph J. Börner, Ingo Hoffmann, Jonas Krettek, Tim Schmitz
Abstract Cryptocurrencies (CCs) have become increasingly interesting for institutional investors’ strategic asset allocation and will therefore be a fixed component of professional portfolios in the future. However, this asset class differs from established assets primarily in that it has a higher standard deviation and tail risk. The question then arises whether CCs with similar statistical key figures exist. On this basis, a core market incorporating CCs with comparable properties enables the implementation of a tracking error approach. A prerequisite for this is the segmentation of the CC market into a core and a satellite, with the latter comprising the accumulation of the residual CCs remaining in the complement. Using a concrete example, we segment the CC market into these components based on modern methods from image/pattern recognition.
Christoph J. Börner, Ingo Hoffmann, Lars M. Kürzinger, Tim Schmitz
This paper evaluates and assesses the risk associated with capital allocation in cryptocurrencies (CCs). In this regard, we take a basket of 27 CCs and the CC index EWCI$^-$ into account. After considering a series of statistical tests we find the stable distribution (SDI) to be the most appropriate to model the body of CCs returns. However, as we find the SDI to possess less favorable properties in the tail area for high quantiles, the generalized Pareto distribution is adapted for a more precise risk assessment. We use a combination of both distributions to calculate the Value at Risk and the Conditional Value at Risk, indicating two subgroups of CCs with differing risk characteristics.
To date, cryptocurrency prices are volatile and many cryptocurrency developers have adopted ad hoc approaches to stabilize their cryptocurrency price. When these currencies are not 100% backed by other valued assets, part of their price volatility may arise from self-fulfilling expectations of a speculative attack (as in Obstfeld (1996)). We show that an exchange rate policy, which is less than 100% backed and dynamically adjusts in response to traders’ conversion demand eliminates speculative attacks while, under some conditions, preserving much of the desired exchange rate stability. This dynamic exchange rate policy admits a great deal of discretion to and requires commitment by the party implementing the policy. We demonstrate how to implement this policy using the Ethereum network—a smart contract blockchain environment—and how this implementation yields commitment to the policy.