Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis

Özdemir, Onur

doi:10.1186/s40854-021-00319-0

Cited by 50 publications

(17 citation statements)

References 91 publications

(87 reference statements)

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“…Notably, BTC has greater wavelet coherence with ETH than any other cryptocurrency. This finding is consistent with the findings of Yousaf and Ali (2020), Ji et al (2019), andÖzdemir (2022). This indicates that the comovement effects between BTC and ETH are relatively stronger between mid-2020 and early 2022 in the 4-to-128-day frequency band.…”

Section: Time-frequency Comovement Analysissupporting

confidence: 92%

“…In this context, several studies have also confirmed that the COVID-19 pandemic has exerted significant impacts on the interactions among cryptocurrencies (e.g. Yousaf and Ali 2020;Polat and Günay 2021;Naeem et al 2021;Demiralay and Golitsis 2021;Raza et al 2022;Kumar et al 2022;Özdemir 2022;Ahmed and Sleem 2022). More importantly, asymmetric characteristics, tail risk, extreme volatility, and pricing bubbles in the cryptocurrency market have all been identified during the COVID-19 pandemic (see Nguyen et al 2020;Xu et al 2021;González et al 2021;Apergis 2022;Iqbal et al 2021;Montasser et al 2022;Ahn 2022;Shahzad et al 2022).…”

Section: Introductionmentioning

confidence: 82%

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Time–frequency co-movement and risk connectedness among cryptocurrencies: new evidence from the higher-order moments before and during the COVID-19 pandemic

Cui

Maghyereh

2022

Financ Innov

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Analyzing comovements and connectedness is critical for providing significant implications for crypto-portfolio risk management. However, most existing research focuses on the lower-order moment nexus (i.e. the return and volatility interactions). For the first time, this study investigates the higher-order moment comovements and risk connectedness among cryptocurrencies before and during the COVID-19 pandemic in both the time and frequency domains. We combine the realized moment measures and wavelet coherence, and the newly proposed time-varying parameter vector autoregression-based frequency connectedness approach (Chatziantoniou et al. in Integration and risk transmission in the market for crude oil a time-varying parameter frequency connectedness approach. Technical report, University of Pretoria, Department of Economics, 2021) using intraday high-frequency data. The empirical results demonstrate that the comovement of realized volatility between BTC and other cryptocurrencies is stronger than that of the realized skewness, realized kurtosis, and signed jump variation. The comovements among cryptocurrencies are both time-dependent and frequency-dependent. Besides the volatility spillovers, the risk spillovers of high-order moments and jumps are also significant, although their magnitudes vary with moments, making them moment-dependent as well and are lower than volatility connectedness. Frequency connectedness demonstrates that the risk connectedness is mainly transmitted in the short term (1–7 days). Furthermore, the total dynamic connectedness of all realized moments is time-varying and has been significantly affected by the outbreak of the COVID-19 pandemic. Several practical implications are drawn for crypto investors, portfolio managers, regulators, and policymakers in optimizing their investment and risk management tactics.

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Section: Time-frequency Comovement Analysissupporting

confidence: 92%

Section: Introductionmentioning

confidence: 82%

Time–frequency co-movement and risk connectedness among cryptocurrencies: new evidence from the higher-order moments before and during the COVID-19 pandemic

Cui

Maghyereh

2022

Financ Innov

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“…pre-pandemic and pandemic periods. This analytical tool captures low and high scale impacts of shocks between sectors by decomposing the time series into various frequency components across time making the tool technically superior in connectedness analyses (Özdemir, 2022). WCA is built up by two main components, namely the cross-wave transform (CWT) and coherence (Torrence and Compo, 1998).…”

Section: Methodsmentioning

confidence: 99%

Asymmetric volatility spillover between hospitality sub-sectors during COVID-19: evidence from the USA

Marobhe

Kansheba²

2022

JHTI

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PurposeThis article examines dynamic volatility spillovers between stock index returns of four main hospitality sub-sectors in US during the coronavirus disease 2019 (COVID-19) pandemic. These are tourism and travel, hotel and lodging, recreational services and food and beverages. Volatility spillovers are explicitly used as accurate and informative proxies for risk contagion between sectors during turbulent times.Design/methodology/approachThe authors employ dynamic conditional correlation-generalized autoregression heteroskedasticity (DCC-GARCH) and wavelet coherence analysis (WCA) to analyze the phenomenon. The authors’ timeframe is divided into three main sub-periods, namely the pre-pandemic, the first wave and the second wave periods.FindingsThis study’s results reveal immense negative shocks in returns of all four sub-sectors on the Black Monday (8th March 2020). Moreover, high volatility persistence was observed during both waves with an exception of tourism and travel which exhibited lower volatility persistence during the second wave. The authors discovered magnified contagion effects between tourism and travel, hotel and lodgment and recreational services during the first wave of the pandemic with tourism and travel being the main volatility transmitter. Lower magnitudes of spillovers were observed between food and beverages and other sub-sectors with a decoupling effect being evident during the second wave.Research limitations/implicationsThis study’s findings contribute to the contagion theory by providing evidence of disproportional volatility spillover among hospitality sub-sectors despite being exposed to similar turbulent economic conditions.Practical implicationsCrucial implications can be drawn from this study’s findings to assist in risk management, asset valuation and portfolio management. The importance of close monitoring, safety measures, international diversification and adequacy of liquid assets during health crises cannot be stresses enough for hospitality firms. Retail investors, speculators and asset managers can take advantage of this study’s findings to design trading strategies and hedge against risk.Originality/valueA body of knowledge pertaining to effects of crises such as COVID-19 on hospitality stocks has been proliferating. Nonetheless, there is still a relative dearth of empirical literature on volatility spillover between hospitality sub-sectors especially during periods of rising economic uncertainties.

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“…Zhang et al ( 2021 ) attempted to determine the role of downside risk in determining returns in the crypto money market through portfolio analysis and Fama–MacBeth regressions and could not find a relationship between them. Özdemir ( 2022 ) analyzed the daily closing prices of eight cryptocurrencies (BTC, ETH, Stellar, XRP, Tether, ADA, LTC, and EOS) using the E-GARCH and DCC-GARCH model and determined that volatility changed because of increasing uncertainty and risk during the coronavirus (COVID-19) pandemic.…”

Section: Literaturementioning

confidence: 99%

Artificial neural network analysis of the day of the week anomaly in cryptocurrencies

et al. 2023

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Anomalies, which are incompatible with the efficient market hypothesis and mean a deviation from normality, have attracted the attention of both financial investors and researchers. A salient research topic is the existence of anomalies in cryptocurrencies, which have a different financial structure from that of traditional financial markets. This study expands the literature by focusing on artificial neural networks to compare different 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 artificial neural networks as an alternative to traditional methods. An artificial neural network is an effective 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 effectiveness of the established models was tested with mean squared error, root mean squared error, mean absolute error, and Theil’s U1, and $${R}_{OOS}^{2}$$ R OOS 2 was used for out-of-sample. The Diebold–Mariano test was used to statistically reveal the difference between the out-of-sample prediction accuracies of the models. When the models created with feedforward artificial 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.

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Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis

Cited by 50 publications

References 91 publications

Time–frequency co-movement and risk connectedness among cryptocurrencies: new evidence from the higher-order moments before and during the COVID-19 pandemic

Time–frequency co-movement and risk connectedness among cryptocurrencies: new evidence from the higher-order moments before and during the COVID-19 pandemic

Asymmetric volatility spillover between hospitality sub-sectors during COVID-19: evidence from the USA

Artificial neural network analysis of the day of the week anomaly in cryptocurrencies

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