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A1101
Title: Predictability of cryptocurrency returns: Evidence from robust tests Authors:  Siyun He - University of Michigan (United States) [presenting]
Rustam Ibragimov - Imperial College London and St. Petersburg State University (United Kingdom)
Abstract: The purpose is to provide a comparative empirical study of predictability of cryptocurrency returns and prices using econometrically justified robust inference methods. We present a robust econometric analysis of predictive regressions incorporating factors 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 HAC standard errors. We further present the analysis of the predictive regressions using recently developed $t$-statistic robust inference approaches. 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 emphasise 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.