“…This finding indicates that systemic risk relating to the outbreak and development of the COVID-19 pandemic intensifies the risk spillover across cryptocurrency markets (Goodell and Goutte 2020 ). Other studies identify similar results between cryptocurrencies and stock markets (Conlon and McGee 2020 ; Corbet, et al 2020 ), and cryptocurrency and commodity markets (Dutta et al 2020 ). Fig.…”
Section: Resultsmentioning
confidence: 55%
“…In doing so, we account for the three-factor pricing model of Shen et al ( 2020a ) which is able to outperform the CAPM model in the cryptocurrency markets and thus extends the literature dealing with spillovers in the cryptocurrency markets based on raw return data (e.g., Koutmos 2018 ; Ji et al 2019 ; Zięba et al 2019 ). Overall, our paper is very pertinent to the academic debate about the cryptocurrency markets during the COVID-19 outbreak (Conlon and McGee 2020 ; Chen et al 2020 ; Corbet et al 2020 ; Conlon et al 2020 ; Goodell and Goutte 2020 ; Dutta et al 2020 ) and how the dynamics of return spillovers among cryptocurrencies change in regard to market downturns or crashes.…”
Section: Introductionmentioning
confidence: 94%
“…In this paper, we study return spillovers among leading cryptocurrencies while capturing shifts in regimes due to the COVID-19 outbreak, sudden events, or changes in market conditions. Our current paper is related to a newly rising strand of literature that deals with the role of Bitcoin and other cryptocurrencies against conventional assets during the COVID-19 outbreak (Conlon and McGee 2020 ; Chen et al 2020 ; Corbet et al 2020 ; Conlon et al 2020 ; Goodell and Goutte 2020 ; Dutta et al 2020 ). However, it differs in several aspects.…”
The aim of this study is to examine the daily return spillover among 18 cryptocurrencies under low and high volatility regimes, while considering three pricing factors and the effect of the COVID-19 outbreak. To do so, we apply a Markov regime-switching (MS) vector autoregressive with exogenous variables (VARX) model to a daily dataset from 25-July-2016 to 1-April-2020. The results indicate various patterns of spillover in high and low volatility regimes, especially during the COVID-19 outbreak. The total spillover index varies with time and abruptly intensifies following the outbreak of COVID-19, especially in the high volatility regime. Notably, the network analysis reveals further evidence of much higher spillovers in the high volatility regime during the COVID-19 outbreak, which is consistent with the notion of contagion during stress periods.
“…This finding indicates that systemic risk relating to the outbreak and development of the COVID-19 pandemic intensifies the risk spillover across cryptocurrency markets (Goodell and Goutte 2020 ). Other studies identify similar results between cryptocurrencies and stock markets (Conlon and McGee 2020 ; Corbet, et al 2020 ), and cryptocurrency and commodity markets (Dutta et al 2020 ). Fig.…”
Section: Resultsmentioning
confidence: 55%
“…In doing so, we account for the three-factor pricing model of Shen et al ( 2020a ) which is able to outperform the CAPM model in the cryptocurrency markets and thus extends the literature dealing with spillovers in the cryptocurrency markets based on raw return data (e.g., Koutmos 2018 ; Ji et al 2019 ; Zięba et al 2019 ). Overall, our paper is very pertinent to the academic debate about the cryptocurrency markets during the COVID-19 outbreak (Conlon and McGee 2020 ; Chen et al 2020 ; Corbet et al 2020 ; Conlon et al 2020 ; Goodell and Goutte 2020 ; Dutta et al 2020 ) and how the dynamics of return spillovers among cryptocurrencies change in regard to market downturns or crashes.…”
Section: Introductionmentioning
confidence: 94%
“…In this paper, we study return spillovers among leading cryptocurrencies while capturing shifts in regimes due to the COVID-19 outbreak, sudden events, or changes in market conditions. Our current paper is related to a newly rising strand of literature that deals with the role of Bitcoin and other cryptocurrencies against conventional assets during the COVID-19 outbreak (Conlon and McGee 2020 ; Chen et al 2020 ; Corbet et al 2020 ; Conlon et al 2020 ; Goodell and Goutte 2020 ; Dutta et al 2020 ). However, it differs in several aspects.…”
The aim of this study is to examine the daily return spillover among 18 cryptocurrencies under low and high volatility regimes, while considering three pricing factors and the effect of the COVID-19 outbreak. To do so, we apply a Markov regime-switching (MS) vector autoregressive with exogenous variables (VARX) model to a daily dataset from 25-July-2016 to 1-April-2020. The results indicate various patterns of spillover in high and low volatility regimes, especially during the COVID-19 outbreak. The total spillover index varies with time and abruptly intensifies following the outbreak of COVID-19, especially in the high volatility regime. Notably, the network analysis reveals further evidence of much higher spillovers in the high volatility regime during the COVID-19 outbreak, which is consistent with the notion of contagion during stress periods.
“…(2019) and Qadan, 2019 . The relatively lower resilience of emerging stock markets during the COVID-19 pandemic suggests that investment in emerging stock markets is not a suitable safe-haven option for potential investors (see Dutta et al., 2020 ; Ji et al., 2020 ). In other words, risk-averse investors in emerging stock markets will need to shun domestic asset sentiment and diversify their investment portfolio with assets that are more resilient to pandemics and epidemics, such as gold.…”
Section: Resultsmentioning
confidence: 99%
“…In other words, risk-averse investors in emerging stock markets will need to shun domestic asset sentiment and diversify their investment portfolio with assets that are more resilient to pandemics and epidemics, such as gold. Evidently, Dutta et al. (2020) noted that gold serves better than bitcoin as a safe-haven asset.…”
In this study, we examine the response of emerging stock markets due to the uncertainty of pandemics and epidemics (UPE), including the COVID-19 pandemic. We demonstrate this by evaluating the stock return predictability of 24 emerging market stocks using the new datasets on uncertainty due to pandemics as well as the global fear index for the COVID-19 pandemic. We partition the data sample into periods before and after the announcement of the COVID-19 pandemic and employ panel data techniques that account for salient features of both the series and predictive model. We found that emerging stock markets are more vulnerable to UPE than developed market stocks. Put differently, developed stock markets provide a better hedge against UPE than emerging stock markets. We also find that incorporating the UPE indicator in the valuation of stocks, particularly during pandemics, is crucial for investment decisions.
This study examines the Bitcoin price by taking into account global factors, including the Chicago Board Options Exchange's Market Volatility Index (VIX), the US dollar index, the gold price, the oil price, and Bitcoin price volatility. The analysis is conducted using the structural vector autoregression (SVAR) model. The variance decomposition findings revealed that the influence of the VIX on the Bitcoin price was initially restricted, but progressively intensified over time. Among the indicators, Bitcoin price volatility had the highest explanatory share in both daily and weekly data analysis. The impulse response functions demonstrated a statistically significant inverse relationship between the VIX and the Bitcoin price. Furthermore, the analysis revealed that the Bitcoin price was mostly impacted by its own volatility. This implies that investing in Bitcoin requires a certain level of risk‐taking.
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