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Variance Decomposition and Cryptocurrency Return Prediction

Published online by Cambridge University Press:  15 April 2024

Suzanne S. Lee Open the ORCID record for Suzanne S. Lee [Opens in a new window]  and
Minho Wang Open the ORCID record for Minho Wang [Opens in a new window]
Suzanne S. Lee*
Affiliation:
Georgia Institute of Technology Scheller College of Business
Minho Wang
Affiliation:
Florida International University College of Business minwang@fiu.edu
*
suzanne.lee@scheller.gatech.edu (corresponding author)
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Abstract

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This article examines how realized variances predict cryptocurrency returns in the cross section using intraday data. We find that cryptocurrencies with higher variances exhibit lower returns in subsequent weeks. Decomposing total variances into signed jump and jump-robust variances reveals that the negative predictability is attributable to positive jump and jump-robust variances. The negative pricing effect is more pronounced for smaller cryptocurrencies with lower prices, less liquidity, more retail trading activities, and more positive sentiment. Our results suggest that cryptocurrency markets are unique because retail investors and preferences for lottery-like payoffs play important roles in the partial variance effects.

Information

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 60 , Issue 4 , June 2025 , pp. 1859 - 1890
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - SA
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike licence (http://creativecommons.org/licenses/by-nc-sa/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the same Creative Commons licence is used to distribute the re-used or adapted article and the original article is properly cited. The written permission of Cambridge University Press must be obtained prior to any commercial use.
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

We thank Dexin Zhou, Olivier Scaillet, Fabio Trojani, Narayan Jayaraman, Sudheer Chava, and seminar participants at the University of Geneva, the University of Georgia, Baruch College, Australian Finance and Banking Conference, Midwest Finance Association annual meeting, SKKU International Conference, and Joint Conference with the Allied Korea Finance Associations for their helpful discussions, comments, and encouragement. We particularly thank George Pennacchi (the editor) and an anonymous referee for their constructive suggestions and comments.

References

Aït-Sahalia, Y., and Jacod, J.. “Testing for Jumps in a Discretely Observed Process.” Annals of Statististics, 37 (2009), 184–222.Google Scholar
Amaya, D.; Christoffersen, P.; Jacobs, K.; and Vasquez, A.. “Does Realized Skewness Predict the Cross-Section of Equity Returns?” Journal of Financial Economics, 118 (2015), 135–167.CrossRefGoogle Scholar
Amihud, Y. “Illiquidity and Stock Returns: Cross-Section and Time-Series Effects.” Journal of Financial Markets, 5 (2002), 31–56.CrossRefGoogle Scholar
Andersen, T.G.; Bollerslev, T.; and Dobrev, D.. “No-Arbitrage Semi-Martingale Restrictions for Continuous-Time Volatility Models Subject to Leverage Effects, Jumps and i.i.d. Noise: Theory and Testable Distributional Implications.” Journal of Econometrics, 138 (2007), 125–180.CrossRefGoogle Scholar
Andersen, T. G.; Bollerslev, T.; Diebold, F. X.; and Ebens, H.. “The Distribution of Realized Stock Return Volatility.” Journal of Financial Economics, 61 (2001a), 43–76.CrossRefGoogle Scholar
Andersen, T. G.; Bollerslev, T.; Diebold, F. X.; and Labys, P.. “The Distribution of Realized Exchange Rate Volatility.” Journal of the American Statistical Association, 96 (2001b), 42–55.CrossRefGoogle Scholar
Ang, A.; Hodrick, R. J.; Xing, Y.; and Zhang, X.. “The Cross-Section of Volatility and Expected Returns.” Journal of Finance, 61 (2006), 259–299.CrossRefGoogle Scholar
Ang, A.; Hodrick, R. J.; Xing, Y.; and Zhang, X.. “High Idiosyncratic Volatility and Low Returns: Internationa and Further U.S. Evidence.” Journal of Financial Economics, 91 (2009), 1–23.CrossRefGoogle Scholar
Avramov, D.; Chordia, T.; and Goyal, A.. “Liquidity and Autocorrelations in Individual Stock Returns.” Journal of Finance, 61 (2006), 2365–2394.CrossRefGoogle Scholar
Baker, M., and Wurgler, J.. “Investor Sentiment and the Cross-Section of Stock Returns.” Journal of Finance, 61 (2006), 1645–1680.CrossRefGoogle Scholar
Bali, T. G.; Cakici, N.; and Whitelaw, R. F.. “Maxing Out: Stocks as Lotteries and the Cross-Section of Expected Returns.” Journal of Financial Economics, 99 (2011), 427–446.CrossRefGoogle Scholar
Bandi, F. M., and Russell, J. R.. “Separating Microstructure Noise from Volatility.” Journal of Financial Economics, 79 (2006), 655–692.CrossRefGoogle Scholar
Barberis, N., and Huang, M.. “Stocks as Lotteries: The Implications of Probability Weighting for Security Prices.” American Economic Review, 98 (2008), 2066–2100.CrossRefGoogle Scholar
Barberis, N.; Mukherjee, A.; and Wang, B.. “Prospect Theory and Stock Returns: An Empirical Test.” Review of Financial Studies, 29 (2016), 3068–3107.CrossRefGoogle Scholar
Barberis, N., and Xiong, W.. “What Drives the Disposition Effect? An Analysis of a Long-Standing Preference-Based Explanation.” Journal of Finance, 64 (2009), 751–784.CrossRefGoogle Scholar
Barberis, N., and Xiong, W.. “Realization Utility.” Journal of Financial Economics, 104 (2012), 251–271.CrossRefGoogle Scholar
Barndorff-Nielsen, O., and Shephard, N.. “Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation.” Journal of Financial Econometrics, 4 (2006), 1–30.CrossRefGoogle Scholar
Biais, B.; Bisiere, C.; Bouvard, M.; Casamatta, C.; and Menkveld, A. J.. “Equilibrium Bitcoin Pricing.” Journal of Finance, 78 (2023), 967–1014.CrossRefGoogle Scholar
Bianchi, D., and Babiak, M.. “A Factor Model for Cryptocurrency Returns.” CERGE-EI Working Paper (2021), available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3935934.Google Scholar
Bollerslev, T.; Li, S. Z.; and Zhao, B.. “Good Volatility, Bad Volatility, and the Cross Section of Stock Returns.” Journal of Financial and Quantitative Analysis, 55 (2020), 751–781.CrossRefGoogle Scholar
Bollerslev, T.; Medeiros, M. C.; Patton, A. J.; and Quaedvlieg, R.. “From Zero to Hero: Realized Partial (Co) Variances.” Journal of Econometrics, 231 (2021), 348–360.CrossRefGoogle Scholar
Borri, N. “Conditional Tail-Risk in Cryptocurrency Markets.” Journal of Empirical Finance, 50 (2019), 1–19.CrossRefGoogle Scholar
Borri, N., and de Magistris, P. Santucci. “Crypto Premium, Higher-Order Moments and Tail Risk.” Available at SSRN (2022).Google Scholar
Borri, N.; Massacci, D.; Rubin, M.; and Ruzzi, D.. “Crypto Risk Premia.” Working Paper, LUISS University (2022).Google Scholar
Borri, N., and Shakhnov, K.. “The Cross-Section of Cryptocurrency Returns.” Review of Asset Pricing Studies, 12 (2022), 667–705.CrossRefGoogle Scholar
Boyer, B.; Mitton, T.; and Vorkink, K.. “Expected Idiosyncratic Skewness.” Review of Financial Studies, 23 (2010), 169–202.CrossRefGoogle Scholar
Brandt, M. W.; Brav, A.; Graham, J. R.; and Kumar, A.. “The Idiosyncratic Volatility Puzzle: Time Trend or Speculative Episodes?” Review of Financial Studies, 23 (2010), 863–899.CrossRefGoogle Scholar
Chernov, M.; Graveline, J.; and Zviadadze, I.. “Crash Risk in Currency Returns.” Journal of Financial and Quantitative Analysis, 53 (2018), 137–170.CrossRefGoogle Scholar
Chordia, T.; Roll, R.; and Subrahmanyam, A.. “Market Liquidity and Trading Activity.” Journal of Finance, 56 (2001), 501–530.CrossRefGoogle Scholar
Cong, L. W.; Karolyi, G. A.; Tang, K.; and Zhao, W.. “Value Premium, Network Adoption, and Factor Pricing of Crypto Assets.” Working Paper, Cornell University (2022).Google Scholar
Corbet, S.; Cumming, D. J.; Lucey, B. M.; Peat, M.; and Vigne, S. A.. “The Destabilising Effects of Cryptocurrency Cybercriminality.” Economics Letters, 191 (2020), 108741.CrossRefGoogle Scholar
De Long, J. B.; Shleifer, A.; Summers, L. H.; and Waldmann, R. J.. “Noise Trader Risk in Financial Markets.” Journal of Political Economy, 98 (1990), 703–738.CrossRefGoogle Scholar
Duz Tan, S., and Tas, O.. “Social Media Sentiment in International Stock Returns and Trading Activity.” Journal of Behavioral Finance, 22 (2021), 221–234.CrossRefGoogle Scholar
Fama, E. F., and MacBeth, J. D.. “Risk, Return, and Equilibrium: Empirical Tests.” Journal of Political Economy, 81 (1973), 607–636.CrossRefGoogle Scholar
Foucault, T.; Sraer, D.; and Thesmar, D. J.. “Individual Investors and Volatility.” Journal of Finance, 66 (2011), 1369–1406.CrossRefGoogle Scholar
Fu, F. “Idiosyncratic Risk and the Cross-Section of Expected Stock Returns.” Journal of Financial Economics, 91 (2009), 24–37.CrossRefGoogle Scholar
Griffin, J. M., and Shams, A.. “Is Bitcoin Really Untethered?” Journal of Finance, 75 (2020), 1913–1964.CrossRefGoogle Scholar
Han, B., and Kumar, A.. “Speculative Retail Trading and Asset Prices.” Journal of Financial and Quantitative Analysis, 48 (2013), 377–404.CrossRefGoogle Scholar
Hou, K., and Loh, R. K.. “Have We Solved the Idiosyncratic Volatility Puzzle?” Journal of Financial Economics, 121 (2016), 167–194.CrossRefGoogle Scholar
Hou, K., and Moskowitz, T. J.. “Market Frictions, Price Delay, and the Cross-Section of Expected Returns.” Review of Financial Studies, 18 (2005), 981–1020.CrossRefGoogle Scholar
Huang, W.; Liu, Q.; Rhee, S. G.; and Zhang, L.. “Return Reversals, Idiosyncratic Risk, and Expected Returns.” Review of Financial Studies, 23 (2010), 147–168.CrossRefGoogle Scholar
Jegadeesh, N. “Evidence of Predictable Behavior of Security Returns.” Journal of Finance, 45 (1990), 881–898.CrossRefGoogle Scholar
Jegadeesh, N., and Titman, S.. “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.” Journal of Finance, 48 (1993), 65–91.CrossRefGoogle Scholar
Jia, Y.; Liu, Y.; and Yan, S.. “Higher Moments, Extreme Returns, and Cross-Section of Cryptocurrency Returns.” Finance Research Letters, 39 (2021), 101536.CrossRefGoogle Scholar
Jiang, G. J., and Oomen, R. C.. “Testing for Jumps When Asset Prices Are Observed with Noise–A “Swap Variance” Approach.” Journal of Econometrics, 144 (2008), 352–370.CrossRefGoogle Scholar
Kilic, M., and Shaliastovich, I.. “Good and Bad Variance Premia and Expected Returns.” Management Science, 65 (2019), 2522–2544.CrossRefGoogle Scholar
Kogan, S.; Makarov, I.; Niessner, M.; and Schoar, A.. “Are Cryptos Different? Evidence from Retail Trading.” NBER Working Paper No. 31317 (2023).Google Scholar
Lee, S. S. “Jumps and Information Flow in Financial Markets.” Review of Financial Studies, 25 (2012), 439–479.CrossRefGoogle Scholar
Lee, S. S., and Mykland, P. A.. “Jumps in Financial Markets: A New Nonparametric Test and Jump Dynamics.” Review of Financial Studies, 21 (2008), 2535–2563.CrossRefGoogle Scholar
Lee, S. S., and Wang, M.. “The Impact of Jumps on Carry Trade Returns.” Journal of Financial Economics, 131 (2019), 433–455.CrossRefGoogle Scholar
Lee, S. S., and Wang, M.. “Tales of Tails: Jumps in Currency Markets.” Journal of Financial Markets, 48 (2020), 100497.CrossRefGoogle Scholar
Lehmann, B. N. “Fads, Martingales, and Market Efficiency.” Quarterly Journal of Economics, 105 (1990), 1–28.CrossRefGoogle Scholar
Li, T.; Shin, D.; and Wang, B.. “Cryptocurrency Pump-and-Dump Schemes.” Working Paper, University of Florida (2021).Google Scholar
Liu, Y., and Tsyvinski, A.. “Risks and Returns of Cryptocurrency.” Review of Financial Studies, 34 (2021), 2689–2727.CrossRefGoogle Scholar
Liu, Y.; Tsyvinski, A.; and Wu, X.. “Common Risk Factors in Cryptocurrency.” Journal of Finance, 77 (2022), 1133–1177.CrossRefGoogle Scholar
Makarov, I., and Schoar, A.. “Trading and Arbitrage in Cryptocurrency Markets.” Journal of Financial Economics, 135 (2020), 293–319.CrossRefGoogle Scholar
Menkhoff, L.; Sarno, L.; Schmeling, M.; and Schrimpf, A.. “Carry Trades and Global Foreign Exchange Volatility.” Journal of Finance, 67 (2012), 681–718.CrossRefGoogle Scholar
Merton, R. C. “Option Pricing When Underlying Stock Returns Are Discontinuous.” Journal of Financial Economics, 3 (1976), 125–144.CrossRefGoogle Scholar
Merton, R. C. “A Simple Model of Capital Market Equilibrium with Incomplete Information.” Journal of Finance, 42 (1987), 483–510.CrossRefGoogle Scholar
Patton, A. J., and Sheppard, K.. “Good Volatility, Bad Volatility: Signed Jumps and the Persistence of Volatility.” Review of Economics and Statistics, 97 (2015), 683–697.CrossRefGoogle Scholar
Pedersen, L. H. “Game On: Social Networks and Markets.” Journal of Financial Economics, 146 (2022), 1097–1119.CrossRefGoogle Scholar
Piazzesi, M. “Bond Yields and the Federal Reserve.” Journal of Political Economy, 113 (2005), 311–344.CrossRefGoogle Scholar
Price, K.; Price, B.; and Nantell, T. J.. “Variance and Lower Partial Moment Measures of Systematic Risk: Some Analytical and Empirical Results.” Journal of Finance, 37 (1982), 843–855.CrossRefGoogle Scholar
Scaillet, O.; Treccani, A.; and Trevisan, C.. “High-Frequency Jump Analysis of the Bitcoin Market.” Journal of Financial Econometrics, 18 (2020), 209–232.Google Scholar
Shams, A. “The Structure of Cryptocurrency Returns.” Charles A. Dice Center Working Paper No. 2020-11 (2020).Google Scholar
Sockin, M., and Xiong, W.. “A Model of Cryptocurrencies.” Management Science, 69 (2023), 6684–6707.CrossRefGoogle Scholar
Vosoughi, S.; Roy, D.; and Aral, S.. “The Spread of True and False News Online.” Science, 359 (2018), 1146–1151.CrossRefGoogle ScholarPubMed
Xiong, W., and Yu, J.. “The Chinese Warrants Bubble.” American Economic Review, 101 (2011), 2723–2753.CrossRefGoogle Scholar
Yermack, D. “Is Bitcoin a Real Currency? An Economic Appraisal.” In Handbook of Digital Currency. Amsterdam, The Netherlands: Elsevier (2015), 31–43.CrossRefGoogle Scholar
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