Return and volatility spillovers among global assets: Comparing health crisis with geopolitical crisis

This study investigates the dynamic mechanism across equity, cryptocurrency, and commodity markets before and during health and geopolitical crisis (Covid-19 and the Ukrainian war). We apply the (TVP-VAR) based extended joint connectedness methodology, to understand return and volatility connectedness of financial markets for 2010–2023 period. The empirical results indicate that spillovers were particularly high during the Covid-19 and Russia-Ukraine war. First, health and geopolitical risks considerably impact the return and volatility system. Second, the value of total joint connectedness during the COVID-19 period was greater than during Russia-Ukraine war crisis. Also, evidence suggests that Commodity markets, received the highest shocks from other markets after Russia-Ukraine war and wheat was the main commodity receiving chocks from both health and geopolitical crisis. Our findings indicate that spillover channels differ depending on the type of crisis. Specifically, low-frequency components are the main transmission channels during the health crisis, whereas high-frequency components are the main transmission channels during the geopolitical crisis. Finally, results indicate that, cryptocurrency markets played some minor role in transmitting risks between markets. Our results are important in understanding how assets affect return and volatility spillover during geopolitical and health crises and are of particular importance to policymakers, market regulators, investors, and portfolio managers.

Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19

In this paper, we study the long memory behavior of the hourly cryptocurrency returns during the COVID-19 pandemic period. Initially, we apply different tests against the spurious long memory, with the results indicating the presence of true long memory for most cryptocurrencies. Yet, using the multivariate test, the series are found to be contaminated by level shifts or smooth trends. Then, we adopt the wavelet-based multivariate long memory approach suggested by Achard and Gannaz (2016) to model their long memory connectivity. The findings indicate a change in persistence for all series during the sample period. The fractal connectivity clustering indicates a similarity among Ethereum (ETH) and Litecoin (LTC), Monero (XMR), Bitcoin (BTC), and EOC token (EOS), while Stellar (XLM) is clustered away from the remaining series, indicating the absence of any interdependence with other crypto returns. Overall, shocks arising from COVID-19 crisis have led to changes in long-run correlation structure. © 2022 Elsevier B.V.

The rapid growth of cryptocurrencies: How profitable is trading in digital money?

There has been a tremendous growth in cryptocurrencies, which has challenged policy makers around the globe. We obtain millisecond data of some of the most frequently traded cryptocurrencies - bitcoin, ethereum, ripple, litecoin and dash - and two cryptocurrency indices - CRIX and CCI30 - to examine their profitability. Our profitability findings suggest that cryptocurrency traders generate significant profits after considering reasonable transaction costs. We also observe that cryptocurrency market participants can expand and sustain the levels of profitability levels in the subsequent trading activity. Our robustness checks with more recent post-Covid data are consistent with the initial profitability findings, although we observe lower levels of profits for the two indices and weaker profit persistency for all digital assets.

Long Memory in the High Frequency Cryptocurrency Markets using Fractal Connectivity Analysis: The Impact of COVID-19

In this paper, we study the long memory behavior of the hourly cryptocurrency returns during the COVID-19 pandemic period. Initially, we apply different tests against the spurious long memory, with the results indicating the presence of true long memory for most cryptocurrencies. Yet, using the multivariate test, the series are found to be contaminated by level shifts or smooth trends. Then, we adopt the wavelet-based multivariate long memory approach suggested by Achard and Gannaz (2016) to model their long memory connectivity. The findings indicate a change in persistence for all series during the sample period. The fractal connectivity clustering indicates a similarity among Ethereum (ETH) and Litecoin (LTC), Monero (XMR), Bitcoin (BTC), and EOC token (EOS), while Stellar (XLM) is clustered away from the remaining series, indicating the absence of any interdependence with other crypto returns. Overall, shocks arising from COVID-19 crisis have led to changes in long-run correlation structure.

Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19 br

In this paper, we study the long memory behavior of Bitcoin, Litecoin, Ethereum, Ripple, Monero, and Dash with a focus on the COVID-19 period. Initially, we apply a time-varying Lifting method to estimate the Hurst exponent for each cryptocurrency. Then we test for a change in persistence over time. To model the multivariate con-nectivity, the wavelet-based multivariate long memory approach proposed by Achard and Gannaz (2016) is implemented. Our results indicate a change in the long-range dependence for the majority of cryptocurrencies, with a noticeable downward trend in persistence after the 2017 bubble and then a dramatic drop after the outbreak of COVID-19. The drop in persistence after COVID-19 is further illustrated by the Fractal connectivity matrix obtained from the Wavelet long-memory model. Our findings provide important implications regarding the evolution of market efficiency in the cryptocurrency market and the associated fractal structure and dy-namics of the crypto prices over time

Dataset of discourses about COVID-19 and financial markets from Twitter.

In this data article, a collection of 11,625,887 tweets on the topic of the COVID-19 pandemic are provided. The data from Twitter were collected through Twitter API from January 2020 to June 2020. In addition, we also provided subsets of tweets containing discourses on both COVID-19 and financial topics. In order to facilitate the research on sentiment analysis, the Sentiment140 dataset containing 1,600,000 tweets that were annotated as positive or negative sentiment was also provided (Go et al., 2009) We used Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to transform documents to numeric vectors and used logistic regression classifier to train and predict sentiments of tweets. These datasets may garner interest from data science, economists, social science, natural language processing, epidemiology, and public health groups.

Intraday return predictability in the cryptocurrency markets: momentum, reversal, or both

This paper reports evidence of intraday return predictability, consisting of both intraday momentum and reversal, in the cryptocurrency market. Using high-frequency price data on Bitcoin from March 3, 2013, to May 31, 2020, it shows that the patterns of intraday return predictability change in the presence of large intraday price jumps, FOMC announcement release, liquidity levels, and the outbreak of the COVID-19. Intraday return predictability is also found in other actively traded cryptocurrencies such as Ethereum, Litecoin, and Ripple. Further analysis shows that the timing strategy based on the intraday predictors produces higher economic value than the benchmark strategy such as the always-long or the buy-and-hold. Evidence of intraday momentum can be explained in light of the theory of late-informed investors, whereas evidence of intraday reversal, which is unique to the cryptocurrency market, can be related to investors’ overreaction to non-fundamental information and overconfidence bias.

Multivariate long memory structure in the cryptocurrency markets: The impact of COVID-19

In this paper, we study the long memory behavior of Bitcoin, Litecoin, Ethereum, Ripple, Monero, and Dash with a focus on the COVID-19 period. Initially, we apply a time-varying Lifting method to estimate the Hurst exponent for each cryptocurrency. Then we test for a change in persistence over time. To model the multivariate connectivity, the wavelet-based multivariate long memory approach proposed by Achard and Gannaz (2016) is implemented. Our results indicate a change in the long-range dependence for the majority of cryptocurrencies, with a noticeable downward trend in persistence after the 2017 bubble and then a dramatic drop after the outbreak of COVID-19. The drop in persistence after COVID-19 is further illustrated by the Fractal connectivity matrix obtained from the Wavelet long-memory model. Our findings provide important implications regarding the evolution of market efficiency in the cryptocurrency market and the associated fractal structure and dynamics of the crypto prices over time.

Economic Policy Uncertainty in China and Bitcoin Returns: Evidence From the COVID-19 Period.

This paper analyses the effects of the Chinese Economic Policy Uncertainty (CEPU) index on the daily returns of Bitcoin for the period from December 31, 2019 to May 20, 2020. Utilizing the Ordinary Least Squares (OLS) and the Generalized Quantile Regression (GQR) estimation techniques, the paper illustrates that the current CEPU has a positive impact on the returns of Bitcoin. However, the positive impact is statistically significant only at the higher quantiles of the current CEPU. It is concluded that Bitcoin can be used in hedging against policy uncertainties in China since significant rises in uncertainty leads to a higher return in Bitcoin. JEL Codes: G32; G15; C22.