CEBA talks 2020-2022
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Predictability of Cryptocurrency Returns: Evidence from Robust Tests

Authors: Siyun He and Rustam Ibragimov

Abstract: The paper provides 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 that were suggested by Liu and Tsyvinski (2018) as useful predictors for cryptocurrency returns, including cryptocurrency momentum, stock market factors, an analogue of price-to-acceptance ratio, 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 heteroskedasticity and autocorrelation consistent (HAC) standard errors. We further present the analysis of the predictive regressions using recently developed t-statistic robust inference approaches (Ibragimov and Müller, 2010, 2016). 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.


Presentation slides