Predictability of cryptocurrency returns: evidence from robust tests

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Title: Predictability of cryptocurrency returns: evidence from robust tests
Authors: He, S
Ibragimov, R
Item Type: Journal Article
Abstract: The paper provides a comparative empirical study of predictability of cryptocurrency returns and prices using econometrically justified robust inference methods. We present robust econometric analysis of predictive regressions incorporating factors, which were suggested by Liu, Y., & Tsyvinski, A. (2018). Risks and returns of cryptocurrency. NBER working paper no. 24877; Liu, Y., & Tsyvinski, A. (2021). Risks and returns of cryptocurrency. The Review of Financial Studies, 34(6), 2689–2727, as useful predictors for cryptocurrency returns, including cryptocurrency momentum, stock market factors, acceptance of Bitcoin, and Google trends measure of investors’ attention. Due to inherent heterogeneity and dependence properties of returns and other time series in financial and crypto markets, we provide the analysis of the predictive regressions using both heteroskedasticity and autocorrelation consistent (HAC) standard-errors and also the recently developed t -statistic robust inference approaches, Ibragimov, R., & Müller, U. K. (2010). t-statistic based correlation and heterogeneity robust inference. Journal of Business and Economic Statistics, 28, 453–468; Ibragimov, R., & Müller, U. K. (2016). Inference with few heterogeneous clusters. Review of Economics and Statistics, 98, 83–96. We provide comparisons of robust predictive regression estimates between different cryptocurrencies and their corresponding risk and factor exposures. In general, the number of significant factors decreases as we use more robust t-tests, and the t-statistic robust inference approaches appear to perform better than the t-tests based on HAC standard errors in terms of pointing out interpretable economic conclusions. The results in this paper emphasize the importance of the use of robust inference approaches in the analysis of economic and financial data affected by the problems of heterogeneity and dependence.
Issue Date: 14-Jun-2022
Date of Acceptance: 31-Mar-2022
DOI: 10.1515/demo-2022-0111
ISSN: 2300-2298
Publisher: De Gruyter Open
Start Page: 191
End Page: 206
Journal / Book Title: Dependence Modeling
Volume: 10
Issue: 1
Copyright Statement: © 2022 Siyun He and Rustam Ibragimov, published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 International License.
Sponsor/Funder: Russian Science Foundation
Funder's Grant Number: 16-18-10432
Publication Status: Published
Open Access location:
Online Publication Date: 2022-06-14
Appears in Collections:Imperial College Business School

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