Determinants of cryptocurrency returns: A LASSO quantile regression approach

Abstract

We consider a relatively large set of predictors and investigate the determinants of cryptocurrency returns at different quantiles. Our analysis exclusively focuses on the highly volatile period of COVID-19. The innovation in the paper stems from the fact that we employ the LASSO penalty in a quantile regression framework to select informative variables. We find that US government bond indices and small company stock returns, a new predictor introduce in this study, significantly impact the tail behavior of the cryptocurrency returns.