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Does the realized distribution-based measure dominate particular moments? Evidence from cryptocurrency markets

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  • Yang, Jen-Wei
  • Chiu, Shih-Yung
  • Yen, Kuang-Chieh

Abstract

This study investigates the relationship between the daily returns of cryptocurrencies and their realized performance measure and moments (variance, skewness, and kurtosis). Because the cryptocurrency returns are non-Gaussian distributions and perform bubble-like behaviors, we adopt the performance measure proposed by Schnytzer and Westreich (2013) to capture all informational content related to the distribution of a return. First, the empirical results revealed that there is a positive relationship between the realized performance measure and daily returns. Furthermore, this realized performance measure dominates the realized variance, skewness, and kurtosis in terms of affecting and predicting Bitcoin and Ethereum daily returns.

Suggested Citation

  • Yang, Jen-Wei & Chiu, Shih-Yung & Yen, Kuang-Chieh, 2023. "Does the realized distribution-based measure dominate particular moments? Evidence from cryptocurrency markets," Finance Research Letters, Elsevier, vol. 51(C).
  • Handle: RePEc:eee:finlet:v:51:y:2023:i:c:s1544612322005736
    DOI: 10.1016/j.frl.2022.103396
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    as
    1. Robert J. Aumann & Roberto Serrano, 2008. "An Economic Index of Riskiness," Journal of Political Economy, University of Chicago Press, vol. 116(5), pages 810-836, October.
    2. Cheng, Hui-Pei & Yen, Kuang-Chieh, 2020. "The relationship between the economic policy uncertainty and the cryptocurrency market," Finance Research Letters, Elsevier, vol. 35(C).
    3. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    4. Gkillas, Konstantinos & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2022. "Spillovers in Higher-Order Moments of Crude Oil, Gold, and Bitcoin," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 398-406.
    5. Lin William Cong & Zhiguo He & Jiasun Li & Wei Jiang, 2021. "Decentralized Mining in Centralized Pools [Concentrating on the fall of the labor share]," The Review of Financial Studies, Society for Financial Studies, vol. 34(3), pages 1191-1235.
    6. Demir, Ender & Gozgor, Giray & Lau, Chi Keung Marco & Vigne, Samuel A., 2018. "Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation," Finance Research Letters, Elsevier, vol. 26(C), pages 145-149.
    7. Zhang, Wei & Li, Yi & Xiong, Xiong & Wang, Pengfei, 2021. "Downside risk and the cross-section of cryptocurrency returns," Journal of Banking & Finance, Elsevier, vol. 133(C).
    8. Corbet, Shaen & Lucey, Brian & Yarovaya, Larisa, 2018. "Datestamping the Bitcoin and Ethereum bubbles," Finance Research Letters, Elsevier, vol. 26(C), pages 81-88.
    9. Turan G. Bali & Nusret Cakici & Fousseni Chabi-Yo, 2011. "A Generalized Measure of Riskiness," Management Science, INFORMS, vol. 57(8), pages 1406-1423, August.
    10. Jia, Yuecheng & Liu, Yuzheng & Yan, Shu, 2021. "Higher moments, extreme returns, and cross–section of cryptocurrency returns," Finance Research Letters, Elsevier, vol. 39(C).
    11. Amaya, Diego & Christoffersen, Peter & Jacobs, Kris & Vasquez, Aurelio, 2015. "Does realized skewness predict the cross-section of equity returns?," Journal of Financial Economics, Elsevier, vol. 118(1), pages 135-167.
    12. Dean P. Foster & Sergiu Hart, 2009. "An Operational Measure of Riskiness," Journal of Political Economy, University of Chicago Press, vol. 117(5), pages 785-814.
    13. Bali, Turan G. & Cakici, Nusret & Chabi-Yo, Fousseni, 2015. "A new approach to measuring riskiness in the equity market: Implications for the risk premium," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 101-117.
    14. Chang, Bo Young & Christoffersen, Peter & Jacobs, Kris, 2013. "Market skewness risk and the cross section of stock returns," Journal of Financial Economics, Elsevier, vol. 107(1), pages 46-68.
    15. Kinateder, Harald & Papavassiliou, Vassilios G., 2019. "Sovereign bond return prediction with realized higher moments," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 53-73.
    16. Geuder, Julian & Kinateder, Harald & Wagner, Niklas F., 2019. "Cryptocurrencies as financial bubbles: The case of Bitcoin," Finance Research Letters, Elsevier, vol. 31(C).
    17. Shen, Dehua & Urquhart, Andrew & Wang, Pengfei, 2019. "Does twitter predict Bitcoin?," Economics Letters, Elsevier, vol. 174(C), pages 118-122.
    18. Ahmed, Walid M.A. & Al Mafrachi, Mustafa, 2021. "Do higher-order realized moments matter for cryptocurrency returns?," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 483-499.
    19. Michael Sockin & Wei Xiong, 2020. "A Model of Cryptocurrencies," NBER Working Papers 26816, National Bureau of Economic Research, Inc.
    20. Aalborg, Halvor Aarhus & Molnár, Peter & de Vries, Jon Erik, 2019. "What can explain the price, volatility and trading volume of Bitcoin?," Finance Research Letters, Elsevier, vol. 29(C), pages 255-265.
    21. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    22. Homm, Ulrich & Pigorsch, Christian, 2012. "Beyond the Sharpe ratio: An application of the Aumann–Serrano index to performance measurement," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2274-2284.
    23. Kurka, Josef, 2019. "Do cryptocurrencies and traditional asset classes influence each other?," Finance Research Letters, Elsevier, vol. 31(C), pages 38-46.
    24. Bollerslev, Tim & Osterrieder, Daniela & Sizova, Natalia & Tauchen, George, 2013. "Risk and return: Long-run relations, fractional cointegration, and return predictability," Journal of Financial Economics, Elsevier, vol. 108(2), pages 409-424.
    25. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    26. Yukun Liu & Aleh Tsyvinski & Xi Wu, 2022. "Common Risk Factors in Cryptocurrency," Journal of Finance, American Finance Association, vol. 77(2), pages 1133-1177, April.
    27. Yukun Liu & Aleh Tsyvinski, 2021. "Risks and Returns of Cryptocurrency," Review of Financial Studies, Society for Financial Studies, vol. 34(6), pages 2689-2727.
    28. Kadan, Ohad & Liu, Fang, 2014. "Performance evaluation with high moments and disaster risk," Journal of Financial Economics, Elsevier, vol. 113(1), pages 131-155.
    29. Schnytzer, Adi & Westreich, Sara, 2013. "A global index of riskiness," Economics Letters, Elsevier, vol. 118(3), pages 493-496.
    30. Borri, Nicola, 2019. "Conditional tail-risk in cryptocurrency markets," Journal of Empirical Finance, Elsevier, vol. 50(C), pages 1-19.
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