A multiscale analysis of returns and volatility spillovers in cryptocurrency markets: A post-COVID perspective

References

• Aharon, D., Butt, H., Jaffri, A., & Nichols, B. (2023). Asymmetric volatility in the cryptocurrency markets: New evidence from models with structural breaks. International Review of Financial Analysis, 87, e102651. https://doi.org/10.1016/j.irfa.2023.102651
   Web of Science ®Google Scholar
• Al-Shboul, M., Assaf, A., & Mokni, K. (2022). When Bitcoin lost its position: Cryptocurrency uncertainty and the dynamic spillover among cryptocurrencies before and during the COVID-19 pandemic. International Review of Financial Analysis, 83, e102309. https://doi.org/10.1016/j.irfa.2022.102309
   PubMed Web of Science ®Google Scholar
• Ando, T., Greenwood-Nimmo, M., & Shin, Y. (2022). Quantile connectedness: Modeling tail behavior in the topology of financial networks. Management Science, 68(4), 2377–3174. https://doi.org/10.1287/mnsc.2021.3984
   Web of Science ®Google Scholar
• Antonakakis, N., & Gabauer, D. (2017). Refined measures of dynamic connectedness based on TVP-VAR. MPRA Working Paper No. 78282. Munich: Munich Personal RePEc Archive.
   Google Scholar
• Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4), 84. https://doi.org/10.3390/jrfm13040084
   Web of Science ®Google Scholar
• Baruník, J., & Křehlík, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2), 271–296. https://doi.org/10.1093/jjfinec/nby001
   Web of Science ®Google Scholar
• Beneki, C., Koulis, A., Kyriazis, N., & Papadamou, S. (2019). Investigating volatility transmission and hedging properties between Bitcoin and Ethereum. Research in International Business and Finance, 48, 219–227. https://doi.org/10.1016/j.ribaf.2019.01.001
   Web of Science ®Google Scholar
• Borgards, O. (2021). Dynamic time series momentum of cryptocurrencies. The North American Journal of Economics and Finance, 57, e101428. https://doi.org/10.1016/j.najef.2021.101428
   Web of Science ®Google Scholar
• Bouri, E., Shahzad, S. J. H., & Roubaud, D. (2019). Co-explosivity in the cryptocurrency market. Finance Research Letters, 29, 178–183. https://doi.org/10.1016/j.frl.2018.07.005
   Web of Science ®Google Scholar
• Burch, T. R., Emery, D. R., & Fuerst, M. E. (2016). Who moves markets in a sudden marketwide crisis? Evidence from 9/11. Journal of Financial and Quantitative Analysis, 51(2), 463–487. https://doi.org/10.1017/S0022109016000211
   Web of Science ®Google Scholar
• Caporale, G. M., Kang, W., Spagnolo, F., & Spagnolo, N. (2021). Cyber-attacks. spillovers and contagion in the cryptocurrency markets. Journal of International Financial Markets, Institutions and Money, 74, 101298. https://doi.org/10.1016/j.intfin.2021.101298
   Web of Science ®Google Scholar
• Celeste, V., Corbet, S., & Gurdgiev, C. (2020). Fractal dynamics and wavelet analysis: Deep volatility and return properties of Bitcoin. Ethereum and Ripple. The Quarterly Review of Economics and Finance, 76, 310–324. https://doi.org/10.1016/j.qref.2019.09.011
   Web of Science ®Google Scholar
• Cui, J., & Maghyereh, A. (2022). Time-frequency co-movement and risk connectedness among cryptocurrencies: New evidence from the higher-order moments before and during the COVID-19 pandemic. Financial Innovation, 8(1), e90. https://doi.org/10.1186/s40854-022-00395-w
   PubMed Web of Science ®Google Scholar
• Diebold, F., & Yilmaz, K. (2012). Better to give than to receive: Predicitive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006
   Web of Science ®Google Scholar
• Diebold, F., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. https://doi.org/10.1016/j.jeconom.2014.04.012
   Web of Science ®Google Scholar
• Fernández-Macho, J. (2012). Wavelet multiple correlation and cross-correlation: A multiscale analysis of Eurozone stock markets. Physica A: Statistical Mechanics and its Applications, 391(4), 1097–1104. https://doi.org/10.1016/j.physa.2011.11.002
   Web of Science ®Google Scholar
• Fernández-Macho, J. (2018). Time-localized wavelet multiple regression and correlation. Physica A: Statistical Mechanics and its Applications, 492, 1226–1238. https://doi.org/10.1016/j.physa.2017.11.050
   Web of Science ®Google Scholar
• Giudici, G., Milne, A., & Vinogradov, D. (2020). Cryptocurrencies: Market analysis and perspectives. Economia e Politica Industriale, 47(1), 1–18. https://doi.org/10.1007/s40812-019-00138-6
   Google Scholar
• Huynh, T. L. D., Nasir, M. A., Vo, X. V., & Nguyen, T. T. (2020). Small things matter most: The spillover effects in the cryptocurrency market and gold as a silver bullet. The North American Journal of Economics and Finance, 54, 101277. https://doi.org/10.1016/j.najef.2020.101277
   Web of Science ®Google Scholar
• Kumar, A., Iqbal, N., Mitra, S., Kristoufek, L., & Bouri, E. (2022). Connectedness among major cryptocurrencies in standard times and during the COVID-19 outbreak. Journal of International Financial Markets, Institutions and Money, 77, e101523. https://doi.org/10.1016/j.intfin.2022.101523
   Web of Science ®Google Scholar
• Kumar, A., & Anandarao, S. (2019). Volatility spillover in crypto-currency markets: Some evidence from GARCH and wavelet analysis. Physica A: Statistical Mechanics and its Applications, 525, 448–458. https://doi.org/10.1016/j.physa.2019.04.154
   Google Scholar
• Kyriazis, N. (2022). A survey on empirical findings about spillovers in cryptocurrency markets. Journal of Risk and Financial Management, 12(4), e170. https://doi.org/10.3390/jrfm12040170
   Google Scholar
• Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91.
   Web of Science ®Google Scholar
• Moratis, G. (2021). Quantifying the spillover effect in the cryptocurrency market. Finance Research Letters, 38, e101534. https://doi.org/10.1016/j.frl.2020.101534
   Web of Science ®Google Scholar
• Moyo, C., & Phiri, A. (2023). Re-examining Bitcoin’s price-volume relationship: A time-varying spectral analysis. Journal of Risk and Financial Management, 16(7), 324. https://doi.org/10.3390/jrfm16070324
   Google Scholar
• Naeem, M., Qureshi, S., Rehman, M., & Balli, F. (2022). COVID-19 and cryptocurrency market: Evidence from quantile connectedness. Applied Economics, 54(3), 280–306. https://doi.org/10.1080/00036846.2021.1950908
   Web of Science ®Google Scholar
• Nair, J., & Kayal, P. (2022). A study of tail-risk spillovers in cryptocurrency markets. Global Business Review. Advance online publication. https://doi.org/10.1177/09721509221079969
   Web of Science ®Google Scholar
• Nguyen, L., Chevapatrakul, T., & Yao, K. (2020). Investigating tail-risk dependence in the cryptocurrency markets: A LASSO quantile regression approach. Journal of Empirical Finance, 58, 333–355. https://doi.org/10.1016/j.jempfin.2020.06.006
   Web of Science ®Google Scholar
• Omane-Adjepong, M., & Alagidede, I. (2019). Multiresolution analysis and spillovers of major cryptocurrency markets. Research in International Business and Finance, 49, 191–206. https://doi.org/10.1016/j.ribaf.2019.03.003
   Web of Science ®Google Scholar
• Oygur, T., & Unal, G. (2017). Evidence of large fluctuations of stock return and financial crisis from Turkey: Using wavelet coherency and Varma modelling to forecast stock return. Fluctuation and Noise Letters, 16(02), e1750020. https://doi.org/10.1142/S0219477517500201
   Google Scholar
• Oygur, T., & Unal, G. (2021). Vector wavelet coherence for multiple time series. International Journal of Dynamics and Control, 9, 403–409. https://doi.org/10.1007/s40435-020-00706-y
   Google Scholar
• Özdemir, O. (2022). Cue the volatility spillover in the cryptocurrency markets during the COVID–19 pandemic: Evidence from DCC–GARCH and wavelet analysis. Financial Innovation, 8, 12. https://doi.org/10.1186/s40854-021-00319-0
   PubMed Web of Science ®Google Scholar
• Phiri, A. (2022). Can wavelets produce a clearer picture of weak-form market efficiency in Bitcoin? Eurasian Economic Review, 12, 373–386. https://doi.org/10.1007/s40822-022-00214-8
   Web of Science ®Google Scholar
• Polanco-Martínez, J., Fernández-Macho, J., & Medina-Elizalde, M. (2020). Dynamic wavelet correlation analysis for multivariate climate time series. Scientific Reports, 10, e21277. https://doi.org/10.1038/s41598-020-77767-8
   PubMedGoogle Scholar
• Polanco-Martínez, J. (2023). W2CWM2C reloaded: Ten years later. Software Impacts, 16, e100495. https://doi.org/10.1016/j.simpa.2023.100495
   Google Scholar
• Qiao, X., Zhu, H., & Hau, L. (2020). Time-frequency co-movement of cryptocurrency return and volatility: Evidence from wavelet coherence analysis. International Review of Financial Analysis, 71, e101541. https://doi.org/10.1016/j.irfa.2020.101541
   Web of Science ®Google Scholar
• Qureshi, S., Aftab, M., Bouri, E., & Saeed, T. (2020). Dynamic interdependence of cryptocurrency markets: An analysis across time and frequency. Physica A: Statistical Mechanics and its Applications, 559, e125077. https://doi.org/10.1016/j.physa.2020.125077
   Web of Science ®Google Scholar
• Roll, S. A. (1989). Information and volatility: The no-arbitrage martingale approach to timing and resolution irrelevancy. The Journal of Finance, 44(1), 1–17.
   Web of Science ®Google Scholar
• Shahzad, S. J. H., Bouri, E., Kang, S. H., & Saeed, T. (2021). Regime specific spillover across cryptocurrencies and the role of COVID–19. Financial Innovation, 7, 5. https://doi.org/10.1186/s40854-020-00210-4
   PubMed Web of Science ®Google Scholar
• Shahzad, S., Bouri, E., Ahmad, T., & Naeem, M. (2022). Extreme tail network analysis of cryptocurrencies and trading strategies. Finance Research Letters, 44, e102106. https://doi.org/10.1016/j.frl.2021.102106
   Web of Science ®Google Scholar
• Yousaf, I., & Ali, S. (2020). The COVID-19 outbreak and high frequency information transmission between major cryptocurrencies: Evidence from the VA_DCC_GARCH approach. Borsa Istanbul Review, 20(1), s1–s10. https://doi.org/10.1016/j.bir.2020.10.003
   Google Scholar