The cryptocurrency market has experienced stunning growth, with market value exceeding USD 1.5 trillion. We use a DCC-MGARCH model to examine the return and volatility spillovers across three distinct classes of cryptocurrencies: coins, tokens, and stablecoins. Our results demonstrate that conditional correlations are time-varying, peaking during the COVID-19 pandemic sell-off of March 2020, and that both ARCH and GARCH effects play an important role in determining conditional volatility among cryptocurrencies. We find a bi-directional relationship for returns and long-term (GARCH) spillovers between BTC and ETH, but only a unidirectional short-term (ARCH) spillover effect from BTC to ETH. We also find spillovers from BTC and ETH to USDT, but no influence running in the other direction. Our results suggest that USDT does not currently play an important role in volatility transmission across cryptocurrency markets. We also demonstrate applications of our results to hedging and optimal portfolio construction.
The price of a stock rises or falls in relation to a number of different factors, including changes to the economy brought about by pandemics. A few studies have already identified the effect of the COVID-19 pandemic on the stock market. However, empirical evidence is lacking on changes in stock price performance of blockchain-based companies as a result of the COVID-19 pandemic. We use the event study approach to estimate stock expected returns by applying an asset pricing model over a thirty-day event window around the announcement on March 11, 2020 by the World Health Organization (WHO) regarding the outbreak of the coronavirus (COVID-19) as a global pandemic, using a sample of S&P Global 1200 companies. Overall, our results indicate more sensitivity in blockchain-based companies’ stock prices to the COVID-19 pandemic compared to those of non-blockchain-based companies. Cumulative abnormal returns show that the stock price of blockchain-based companies recover losses slower than non-blockchain companies. Our findings are important for investors and shareholders for future pandemics and events.
In this study, we predicted the log returns of the top 10 cryptocurrencies based on market cap, using univariate and multivariate machine learning methods such as recurrent neural networks, deep learning neural networks, Holt’s exponential smoothing, autoregressive integrated moving average, ForecastX, and long short-term memory networks. The multivariate long short-term memory networks performed better than the univariate machine learning methods in terms of the prediction error measures.
Motivated by the unique transaction cost structure of the cryptocurrency (CC) market1, this study investigates the phenomenon of liquidity commonality across a sample of 53 CCs. The study employs the google search volume index (GSVI) measure to capture the retail investor’s attention towards the CC market. Using the quantile regression method, we document the liquidity dynamics of CCs that is contrasting to other asset classes, and is ascribed to its unique transaction cost structure. In view of the relatively high liquidity commonality levels found in the CC market, this paper sounds a note of caution to retail investors on episodic non-availability of liquidity in CCs.
The structural variations related to legal tender of cryptocurrencies operating across the world markets has been instable since their inception. There have been many changes incorporated for their status to be recognized as a legal financial instrument for investment purposes over the virtual financial markets. The concept of anomalies associated with the popular efficient market hypotheses given by Eugene Fama existed for Bitcoin and few other cryptocurrencies over a period of time. The present work attempts to contribute to the existing literature on cryptocurrency studies. A focused investigation has been carried for cryptocurrencies to find different behaviour of returns on these currencies over a varied response from various countries with respect to their permissible manoeuvres. The study has used independent sample t-test, one-way ANOVA and dummy regression analysis to examine the day of the week effect for a time period between 2014–2020 split into multiple sub-periods. Anomalies have been found for cryptocurrencies across multiple sub-periods with varied magnitude.
In this paper, I examine the effect of the May 18th, 2021 Chinese ban of cryptocurrency transactions on the overall volatility of the cryptocurrency market. To do this, I analyze, in both univariate and multivariate settings, range-based volatility in various event windows surrounding the event. I find clear economic and statistical change in volatility in the five days after the ban. In the ten-day period after the ban, there is a moderate economic change in volatility. In the forty-day period after the ban, there is little economic change in volatility. I conclude that the Chinese ban had a clear short-term impact on the volatility of the cryptocurrency marketplace, but the effects wore off shortly thereafter.
Sep 30, 2021·Vestnik Voronezhskogo gosudarstvennogo universiteta Ser Ekonomika i upravlenie = Proceedings of Voronezh State University Series Economics and Management
Dmitry А. Endovitsky, Вячеслав Владимирович Коротких
Introduction. Digital financial assets are a relatively new phenomenon. More and more, they include virtual currencies, and in particular cryptocurrencies. Both regulators and financial market players are becoming increasingly interested in such assets. Cryptocurrencies have no intrinsic value, and this encourages scientific studies on the problem of price formation and risk management associated with cryptocurrency operations. Most papers on the problem lack a systematic ap-proach and do not provide solutions to a large number of fundamental issues. Purpose. The purpose of our study was to develop a method for the risk analysis of operations with digital financial assets, namely cryptocurrencies. Methodology. In our study, we used parametric methods of data analysis and ma-chine learning methods, description, analysis, synthesis, induction, deduction, comparison, and grouping method. The sample was accumulated between April 2013 and April 2021 and included cryptocurrencies with the market capitalization of over 1 million USD. Results. The study determined the common risk factors for the cryptocurrency market. The risk factors are presented as linear combinations of returns of subsets of cryptocurrencies with dynamically changing weight coefficients. The risk factors were formed based on the market information, which included the price of the cryptocurrency, the trading volume, and its market capitalization. Conclusions. The study demonstrated that the cryptocurrency market is suscepti-ble to market anomalies common to traditional financial asset markets. In addition to the risk factors based on the market capitalization of cryptocurrencies (the size) and their aggregate profitability (the momentum), the article presents statistically relevant risk factors which reflect the growth rate of the market capitalization and the level of illiquidity of cryptocurrencies. In order to explain the market anomalies and the arbitrary strategies based on them, the article presents several factor models of cryptocurrency price formation. These models can be used to develop an in-tegrated approach to the risks associated with operations with digital financial assets.
Adam Zaremba, Mehmet Hüseyin Bilgin, Huaigang Long, Aleksander Mercik · 5 authors
We demonstrate a new powerful predictive signal for cryptocurrency returns: the last day's return. Based on daily prices of more than 3600 coins, we document that the cryptocurrencies with low last day's return significantly outperform their counterparts with high last day's return. The effect is confirmed by a battery of cross-sectional tests and portfolio sorts, and is not subsumed by a broad range of other return predictors. We argue that the daily reversals result from the illiquidity of the vast majority of traded cryptocurrencies. In consequence, the pattern is cross-sectionally dependent on liquidity, and the handful of largest and most tradeable coins exhibit daily momentum rather than a reversal. Our findings help to reconcile earlier conflicting evidence on return persistence in cryptocurrency markets.
In this paper we extend the analysis of an agent-based model for adaptive trading, called asynchronous stochastic price pump (ASPP) introduced by Perepelitsa and Timofeyev (2019), to the model with heterogeneous distribution of psychological parameters of speculative optimism and pessimism across the population of traders. We show that the new model has a range of qualitatively different dynamics when the correlation between those factors ranges from low negative to large positive values. A statistical parameter estimation suggests a heterogeneous ASPP with negative correlation as a model of price variations of Bitcoin.
In December 2017, two leading derivative exchanges, CBOE and CME, introduced the first regulated Bitcoin futures. Our aim is estimating their causal impact on Bitcoin volatility and trading volume. Employing a new causal approach, C-ARIMA, we find that the CME future triggered an increase in both outcomes. There is also evidence of a positive volume-volatility relationship and that the effect on volatility was partially due to the higher trading volumes induced by the launch of the contract. After controlling for the effect on volumes, we find that the CME instrument caused Bitcoin volatility to increase by more than double.
We study recurrent patterns in volatility and volume for major cryptocurrencies, Bitcoin and Ether, using data from two centralized exchanges (Coinbase Pro and Binance) and a decentralized exchange (Uniswap V2). We find systematic patterns in both volatility and volume across day-of-the-week, hour-of-the-day, and within the hour. These patterns have grown stronger over the years and can be related to algorithmic trading and funding times in futures markets. We also document that price formation mainly takes place on the centralized exchanges while price adjustments on the decentralized exchanges can be sluggish.
Non-linear interactions between cryptocurrency price movements can elicit cross-frequency coupling (CFC) wherein one set of frequencies in the 1st timeseries is coupled to another set of frequencies in the 2nd timeseries. To investigate this, we use a generalized coherence approach to detect and quantify both linear (i.e., iso-frequency coupling, IFC) and non-linear coherence (CFC) and the associated phase relationships between the intra-day price changes of various pairs of cryptocurrencies for the year 2020. Using this information, we further assess the risk reduction associated with diversification of portfolios between each pair of a small market capital and a large market capital cryptocurrency, for both synchronous and asynchronous trading conditions. While mean pairwise IFC values were lower for smaller cryptocurrencies, pairwise CFC values were more heterogeneous and had no correlation with the market capital size. Diversification of portfolios resulted in reduced risk for synchronously-traded pairs of those cryptocurrencies which had low IFC. For asynchronous trading conditions, if the larger market capital cryptocurrency was traded at a higher frequency, diversification almost always reduced risk. Thus, the novel approach used in this study reveals important insights into the complex dynamics that govern the price trends of cryptocurrencies.
Ceren Kocaoğullar, Arthur Gervais, Benjamin Livshits
While quantitative automation related to trading crypto-assets such as ERC-20 tokens has become relatively commonplace, with services such as 3Commas and Shrimpy offering user-friendly web-driven services for even the average crypto trader, we have not yet seen the emergence of on-chain trading as a phenomenon. We hypothesize that just like decentralized exchanges (DEXes) that by now are by some measures more popular than traditional exchanges, process in the space of decentralized finance (DeFi) may enable attractive online trading automation options. In this paper we present ChainBot, an approach for creating algorithmic trading bots with the help of blockchain technology. We show how to partition the computation into on- and off-chain components in a way that provides a measure of end-to-end integrity, while preserving the algorithmic "secret sauce". Our system is enabled with a careful use of algorithm partitioning, zero-knowledge proofs and smart contracts. We also show that with layer-2 (L2) technologies, trades can be kept private, which means that algorithmic parameters are difficult to recover by a chain observer. Our approach offers more transparent access to liquidity and better censorship-resistance compared to traditional off-chain trading approaches. We develop a sample ChainBot and train it on historical data, resulting in returns that are up to 2.4x the buy-and-hold strategy, which we use as our baseline. Our measurements show that across 1000 runs, the end-to-end average execution time for our system is 48.4 seconds. We demonstrate that the frequency of trading does not significantly affect the rate of return and Sharpe ratio, which indicates that we do not have to trade at every block, thereby significantly saving in terms of gas fees. In our implementation, a user who invests \$1,000 would earn \$105, and spend \$3 on gas; assuming a user pool of 1,000 subscribers.
Purpose This study aims to investigate the diversification benefits attached to the crypto portfolios when combined with stocks, Forex instruments and commodity assets. Design/methodology/approach Markowitz diversification techniques have been used to analyze the risk-return tradeoffs of the individual portfolios. Daily prices on cryptocurrencies and the selected asset classes, cover the period before and during the pandemic COVID-19. The portfolio risk of the portfolios was calculated by identical techniques and analyzed with equal criteria. Findings The results with 270 trails indicate that stocks on average reduce the portfolio risk of crypto portfolios by 36% followed by fiat currency with 30.9% and commodities by 20.8%. Average daily returns stand in line with the standard portfolio theories where riskier portfolios offer higher returns and the other way around. Originality/value The authors contribute to the current literature by investigating the portfolio risk attached to the crypto portfolios when stocks, commodities and Forex instruments were added separately. To this end, results inform not only retail investors but also portfolio managers on the asset classes that generate better optimization for crypto portfolios.
Mateus Portelinha, Carlos Heitor Campani, Raphael Moses Roquete
This study analyses the impact of including cryptocurrencies in Brazilian stocks' portfolios performances from September 2014 to April 2020. The comparisons were made between stocks' only portfolios against portfolios that allowed stocks and cryptocurrencies. Three portfolios served as benchmarks: the naïve but relevant equally weighted portfolio, the tangency and the MVP portfolios built from the Markowitz mean-variance theory. Performances were compared through out-of-sample returns, volatilities, Sharpe, Sortino and Omega ratios. Our results indicate positive statistically significant return and risk-adjusted improvements after the inclusion of cryptocurrencies, although also increasing the volatility. The equally weighted portfolios with cryptocurrencies often outperformed the tangency and minimum variance models, which only exhibited better results when more data was used as input to the models. Moreover, the portfolios that included cryptocurrencies consistently outperformed the IBrX-100 in the period studied. The results of this study are important for investors and fund managers, especially because cryptocurrencies are yet not considered by most of them.
Do behavioral factors mediate the relationship between industry returns and Bitcoin returns? We use four industry indices in technology, energy, clean energy, and banking, and the Sentiment index from Thomson Reuters Marketpsych Indices as a behavioral factor to investigate this question. We show that the sensitivities of technology and clean energy industry indices to Sentiment, positively and significantly, strengthen the relationship between sentiment and Bitcoin returns. By showing that behavioral factors mediate the association between the returns of industry indices and Bitcoin returns, we provide evidence that investors’ Sentiment captures the association between Bitcoin and sectors related to cryptocurrencies. Our results, however, do not support prior studies’ findings of a direct relationship between the industry indices and Bitcoin returns.
Dimitrios Koutmos, Timothy King, Constantin Zopounidis
Abstract Are cryptocurrencies useful minimum‐variance hedging instruments? This paper develops a two‐step analytical framework to explore this question across time. First, it estimates dynamic optimal weights, calibrated when investing between the aggregate market and a respective sampled cryptocurrency. This is performed separately for 11 major cryptocurrencies using the dynamic conditional correlation approach of Engle. Second, using a fractional regression approach, it uncovers linkages between optimal weights in cryptocurrencies and sources of economic uncertainty. Overall, this paper makes the following important findings. First, optimal weights in cryptocurrencies all rose rapidly during the COVID‐19 pandemic. In all, bitcoin showed to be the leading cryptocurrency in terms of hedging effectiveness during this recent time period. Second, most cryptocurrencies exhibit zero or negative betas consistently across time, thus making them natural hedging instruments for investors seeking to reduce their portfolio's comovement with the market. Finally, cryptocurrencies serve as better hedges for economic uncertainties arising from equity and commodity markets. They are relatively less effective for uncertainties arising from risks in the banking industry and firm default risk. This paper contributes broadly to the asset pricing literature since our two‐step approach herein can tractably be extended to other asset classes or other econometric measures of systematic risk.
This paper examines, in the time and frequency domains, the quantile dependence and directional predictability of investor attention to cryptocurrency returns. We find that there is significant tail dependence between investor attention and cryptocurrency returns. When market pays very high or very low attention to cryptocurrencies, there is an increased likelihood to have very large positive gains and suffer from very large negative results. Our results indicate that the quantile dependence between investor attention and cryptocurrency returns has a higher statistical significance in the long-term than medium-term and short-term. This implies that quantile dependence between investor attention and cryptocurrency returns is mainly dominated by low-frequency components. These findings have important implications for cryptocurrency investors.
This paper studies the impact of investor sentiment on the Bitcoin returns and conditional volatility taking into account the Covid-19 outbreak by using different investor sentiment proxies and by employing the EGARCH model. Estimation results show that investor sentiment has a positive impact on the Bitcoin returns and their volatility, especially after the Covid-19 outbreak. The VAR model is employed to investigate whether investor sentiment and Bitcoin returns are related in a dynamic setting and to make distinguish between rational and irrational investor sentiments. The results from the VAR model show that both rational and irrational investor sentiments have an impact on Bitcoin returns indicating that the Bitcoin market is also driven by emotions and noise traders have an impact on the data generating process of Bitcoin returns. The positive impact of investor sentiment can be attributed to the fear of missing out (FOMO) behavior of speculative and irrational investors.