High-frequency return and volatility spillovers among cryptocurrencies

References

• Akhtaruzzaman, M., A. Sensoy, and S. Corbet. 2020. “The Influence of Bitcoin on Portfolio Diversification and Design.” Finance Research Letters 37: 101344. doi:10.1016/j.frl.2019.101344.
   Web of Science ®Google Scholar
• Akyildirim, E., S. Corbet, P. Katsiampa, N. Kellard, and A. Sensoy. 2020a. “The Development of Bitcoin Futures: Exploring the Interactions between Cryptocurrency Derivatives.” Finance Research Letters 37: 101234. doi:10.1016/j.frl.2019.07.007.
   Web of Science ®Google Scholar
• Akyildirim, E., S. Corbet, B. Lucey, A. Sensoy, and L. Yarovaya. 2020b. “The Relationship between Implied Volatility and Cryptocurrency Returns.” Finance Research Letters 33: 101212. doi:10.1016/j.frl.2019.06.010.
   Web of Science ®Google Scholar
• Arouri, M., J. Jouini, and D. Nguyen. 2012. “On the Impacts of Oil Price Fluctuations on European Equity Markets: Volatility Spillover and Hedging Effectiveness.” Energy Economics 34 (2): 611–617. doi:10.1016/j.eneco.2011.08.009.
   Web of Science ®Google Scholar
• Aslan, A., and A. Sensoy. 2020. “Intraday Efficiency-frequency Nexus in the Cryptocurrency Markets.” Finance Research Letters 35: 101298. doi:10.1016/j.frl.2019.09.013.
   Web of Science ®Google Scholar
• Awartani, B., and A. Maghyereh. 2013. “Dynamic Spillovers between Oil and Stock Markets in the Gulf Cooperation Council Countries.” Energy Economics 36: 28–42. doi:10.1016/j.eneco.2012.11.024.
   Web of Science ®Google Scholar
• Baele, L. 2005. “Volatility Spillover Effects in European Equity Markets.” Journal of Financial and Quantitative Analysis 40 (2): 373–401. doi:10.1017/S0022109000002350.
   Web of Science ®Google Scholar
• Bariviera, A. 2017. “The Inefficiency of Bitcoin Revisited: A Dynamic Approach.” Economics Letters 161: 1–4. doi:10.1016/j.econlet.2017.09.013.
   Web of Science ®Google Scholar
• Barunik, J., and T. Krehlik. 2018. “Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk*.” Journal of Financial Econometrics 16 (2): 271–296. doi:10.1093/jjfinec/nby001.
   Web of Science ®Google Scholar
• Baumhl, E., E. Koenda, Å. Lycsa, and T. Vrost. 2018. “Networks of Volatility Spillovers among Stock Markets.” Physica A: Statistical Mechanics and Its Applications 490: 1555–1574. doi:10.1016/j.physa.2017.08.123.
   Web of Science ®Google Scholar
• Baur, D., K. Hong, and A. Lee. 2018. “Bitcoin: Medium of Exchange or Speculative Assets?” Journal of International Financial Markets, Institutions and Money 54: 177–189. doi:10.1016/j.intfin.2017.12.004.
   Web of Science ®Google Scholar
• Begusic, S., Z. Kostanjar, H. Eugene Stanley, and B. Podobnik. 2018. “Scaling Properties of Extreme Price Fluctuations in Bitcoin Markets.” Physica A: Statistical Mechanics and Its Applications 510: 400–406. doi:10.1016/j.physa.2018.06.131.
   Web of Science ®Google Scholar
• Bouri, E., M. Das, R. Gupta, and D. Roubaud. 2018. “Spillovers between Bitcoin and Other Assets during Bear and Bull Markets.” Applied Economics 50 (55): 5935–5949. doi:10.1080/00036846.2018.1488075.
   Web of Science ®Google Scholar
• Celeste, V., S. Corbet, and C. Gurdgiev. 2020. “Fractal Dynamics and Wavelet Analysis: Deep Volatility and Return Properties of Bitcoin, Ethereum and Ripple.” Quarterly Review of Economics and Finance 76: 310–324. doi:10.1016/j.qref.2019.09.011.
   Web of Science ®Google Scholar
• Chaim, P., and M. Laurini. 2018. “Volatility and Return Jumps in Bitcoin.” Economics Letters 173: 158–163. doi:10.1016/j.econlet.2018.10.011.
   Web of Science ®Google Scholar
• Corbet, S., D. Cumming, B. Lucey, M. Peat, and S. Vigne. 2020a. “The Destabilising Effects of Cryptocurrency Cybercriminality.” Economics Letters 191: 108741. doi:10.1016/j.econlet.2019.108741.
   Web of Science ®Google Scholar
• Corbet, S., and P. Katsiampa. 2020. “Asymmetric Mean Reversion of Bitcoin Price Returns.” International Review of Financial Analysis 71: 101267. doi:10.1016/j.irfa.2018.10.004.
   Web of Science ®Google Scholar
• Corbet, S., C. Larkin, B. Lucey, A. Meegan, and L. Yarovaya. 2020b. “Cryptocurrency Reaction to Fomc Announcements: Evidence of Heterogeneity Based on Blockchain Stack Position.” Journal of Financial Stability 46: 100706. doi:10.1016/j.jfs.2019.100706.
   Web of Science ®Google Scholar
• Corbet, S., B. Lucey, M. Peat, and S. Vigne. 2018a. “Bitcoin Futureswhat Use are They?” Economics Letters 172: 23–27. doi:10.1016/j.econlet.2018.07.031.
   Web of Science ®Google Scholar
• Corbet, S., B. Lucey, M. Peat, and S. Vigne. 2019a. “What Sort of Asset? Bitcoin Analysed.” Lecture Notes in Business Information Processing 345: 52–65.
   Google Scholar
• Corbet, S., B. Lucey, A. Urquhart, and L. Yarovaya. 2019b. “Cryptocurrencies as A Financial Asset: A Systematic Analysis.” Inter- National Review of Financial Analysis 62: 182–199. doi:10.1016/j.irfa.2018.09.003.
   Web of Science ®Google Scholar
• Corbet, S., B. Lucey, and L. Yarovaya. 2018b. “Datestamping the Bitcoin and Ethereum Bubbles.” Finance Research Letters 26: 81–88. doi:10.1016/j.frl.2017.12.006.
   Web of Science ®Google Scholar
• Corbet, S., G. McHugh, and A. Meegan. 2017. “The Influence of Central Bank Monetary Policy Announcements on Cryptocurrency Return Volatility.” Investment Management and Financial Innovations 14 (4): 60–72. doi:10.21511/imfi.14(4).2017.07.
   Google Scholar
• Corbet, S., A. Meegan, C. Larkin, B. Lucey, and L. Yarovaya. 2018c. “Exploring the Dynamic Relationships between Cryptocur- Rencies and Other Financial Assets.” Economics Letters 165: 28–34. doi:10.1016/j.econlet.2018.01.004.
   Web of Science ®Google Scholar
• Csardi, G., and T. Nepusz. 2006. “The Igraph Software Package for Complex Network Research.” InterJournal, Complex Systems 1695 URL: http://igraph.org
   Google Scholar
• Das, D., P. Bhowmik, and R. Jana. 2018. “A Multiscale Analysis of Stock Return Co-movements and Spillovers: Evidence from Pacific Developed Markets.” Physica A: Statistical Mechanics and Its Applications 502: 379–393. doi:10.1016/j.physa.2018.02.143.
   Web of Science ®Google Scholar
• Diebold, F. X., and K. Yilmaz. 2012. “Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers.” International Journal of Forecasting 28 (1): 57–66. Special Section 1: The Predictability of Financial Markets Special Section 2: Credit Risk Modelling and Forecasting. doi:10.1016/j.ijforecast.2011.02.006.
   Web of Science ®Google Scholar
• Diebold, F. X., and K. Yilmaz. 2014. “On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms.” Journal of Econometrics 182 (1): 119–134. Causality, Prediction, and Specification Analysis: Recent Advances and Future Directions. doi:10.1016/j.jeconom.2014.04.012.
   Web of Science ®Google Scholar
• Feng, W., Y. Wang, and Z. Zhang. 2018. “Can Cryptocurrencies Be a Safe Haven: A Tail Risk Perspective Analysis.” Applied Economics 50 (44): 4745–4762. doi:10.1080/00036846.2018.1466993.
   Web of Science ®Google Scholar
• Fry, J. 2018. “Booms, Busts and Heavy-tails: The Story of Bitcoin and Cryptocurrency Markets?” Economics Letters 171: 225–229. doi:10.1016/j.econlet.2018.08.008.
   Web of Science ®Google Scholar
• Fry, J., and E.-T. Cheah. 2016. “Negative Bubbles and Shocks in Cryptocurrency Markets.” International Review of Financial Analysis 47: 343–352. doi:10.1016/j.irfa.2016.02.008.
   Web of Science ®Google Scholar
• Gandal, N., J. Hamrick, T. Moore, and T. Oberman. 2018. “Price Manipulation in the Bitcoin Ecosystem.” Journal of Monetary Economics 95: 86–96. doi:10.1016/j.jmoneco.2017.12.004.
   Web of Science ®Google Scholar
• Griffins, J., and A. Shams. 2020. “Is Bitcoin Really Un-tethered?” Journal of Finance 75 (4): 1913–1964. doi:10.1111/jofi.12903.
   Web of Science ®Google Scholar
• Ji, Q., E. Bouri, C. Lau, and D. Roubaud. 2019a. “Dynamic Connectedness and Integration in Cryptocurrency Markets.” International Review of Financial Analysis 63: 257–272. doi:10.1016/j.irfa.2018.12.002.
   Web of Science ®Google Scholar
• Ji, Q., E. Bouri, D. Roubaud, and L. Kristoufek. 2019b. “Information Interdependence among Energy, Cryptocurrency and Major Commodity Markets.” Energy Economics 81: 1042–1055. doi:10.1016/j.eneco.2019.06.005.
   Web of Science ®Google Scholar
• Ji, Q., and Y. Fan. 2012. “How Does Oil Price Volatility Affect Non-energy Commodity Markets? Applied Energy, 89, 273–280. Kanas, A. (2000). Volatility Spillovers between Stock Returns and Exchange Rate Changes: International Evidence.” Journal of Business Finance and Accounting 27: 447–467.
   Google Scholar
• Kang, S. H., and J. W. Lee. 2019. “The Network Connectedness of Volatility Spillovers across Global Futures Markets.” Physica A: Statistical Mechanics and Its Applications 526: 120756. doi:10.1016/j.physa.2019.03.121.
   Web of Science ®Google Scholar
• Katsiampa, P., S. Corbet, and B. Lucey. 2019a. “High Frequency Volatility Co-movements in Cryptocurrency Markets.” Journal of International Financial Markets, Institutions and Money 62: 35–52. doi:10.1016/j.intfin.2019.05.003.
   Web of Science ®Google Scholar
• Katsiampa, P., S. Corbet, and B. Lucey. 2019b. “Volatility Spillover Effects in Leading Cryptocurrencies: A Bekk-mgarch Analysis.” Finance Research Letters 29: 68–74. doi:10.1016/j.frl.2019.03.009.
   Web of Science ®Google Scholar
• Krehlik, T. (2020). “Frequency Connectedness: Spectral Decomposition of Connectedness Measures.” URL: https://CRAN.R-project.org/package=frequencyConnectednessrpackageversion0.2.2
   Google Scholar
• Kumar, A., and S. Anandarao. 2019. “Volatility Spillover in Crypto-currency Markets: Some Evidences from Garch and Wavelet Analysis.” Physica A: Statistical Mechanics and Its Applications 524: 448–458. doi:10.1016/j.physa.2019.04.154.
   Web of Science ®Google Scholar
• Lahmiri, S., and S. Bekiros. 2020a. “Big Data Analytics Using Multi-fractal Wavelet Leaders in High-frequency Bitcoin Markets.” Chaos, Solitons Fractals 131: 109472. doi:10.1016/j.chaos.2019.109472.
   Web of Science ®Google Scholar
• Liu, X., H. An, S. Huang, and S. Wen. 2017. “The Evolution of Spillover Effects between Oil and Stock Markets across Multi-scales Using a Wavelet-based Garchbekk Model.” Physica A: Statistical Mechanics and Its Applications 465: 374–383. doi:10.1016/j.physa.2016.08.043.
   Web of Science ®Google Scholar
• Makarov, I., and A. Schoar. 2020. “Trading and Arbitrage in Cryptocurrency Markets.” Journal of Financial Economics 135 (2): 293–319. doi:10.1016/j.jfineco.2019.07.001.
   Web of Science ®Google Scholar
• Mensi, W., Y.-J. Lee, K. H. Al-Yahyaee, A. Sensoy, and S.-M. Yoon. 2019. “Intraday Downward/upward Multifractality and Long Memory in Bitcoin and Ethereum Markets: An Asymmetric Multifractal Detrended Fluctuation Analysis.” Finance Research Letters 31: 19–25. doi:10.1016/j.frl.2019.03.029.
   Web of Science ®Google Scholar
• Nicholson, W., D. Matteson, and J. Bien (2019). “BigVAR: Dimension Reduction Methods for Multivariate Time Series.” URL: https://CRAN.R-project.org/package=BigVARrpackageversion1.0.6
   Google Scholar
• Omane-Adjepong, M., and I. Alagidede. 2019. “Multiresolution Analysis and Spillovers of Major Cryptocurrency Markets.” Research in International Business and Finance 49: 191–206. doi:10.1016/j.ribaf.2019.03.003.
   Web of Science ®Google Scholar
• Phillip, A., J. Chan, and S. Peiris. 2018. “A New Look at Cryptocurrencies.” Economics Letters 163: 6–9. doi:10.1016/j.econlet.2017.11.020.
   Web of Science ®Google Scholar
• Stavroyiannis, S., V. Babalos, S. Bekiros, S. Lahmiri, and G. S. Uddin. 2019. “The High Frequency Multifractal Properties of Bitcoin.” Physica A: Statistical Mechanics and Its Applications 520: 62–71. doi:10.1016/j.physa.2018.12.037.
   Web of Science ®Google Scholar
• Stosic, D., D. Stosic, T. Ludermir, and T. Stosic. 2018. “Collective Behavior of Cryptocurrency Price Changes.” Physica A: Statistical Mechanics and Its Applications 507: 499–509. doi:10.1016/j.physa.2018.05.050.
   Web of Science ®Google Scholar
• Wei, W. 2018. “Liquidity and Market Efficiency in Cryptocurrencies.” Economics Letters 168: 21–24. doi:10.1016/j.econlet.2018.04.003.
   Web of Science ®Google Scholar
• Yi, S., Z. Xu, and G.-J. Wang. 2018. “Volatility Connectedness in the Cryptocurrency Market: Is Bitcoin a Dominant Cryptocurrency?” International Review of Financial Analysis 60: 98–114. doi:10.1016/j.irfa.2018.08.012.
   Web of Science ®Google Scholar
• Zhang, W., P. Wang, X. Li, and D. Shen. 2018. “The Inefficiency of Cryptocurrency and Its Cross-correlation with Dow Jones Industrial Average.” Physica A: Statistical Mechanics and Its Applications 510: 658–670. doi:10.1016/j.physa.2018.07.032.
   Web of Science ®Google Scholar