A Bayesian approach for the determinants of bitcoin returns

The aim of this paper is to identify potential determinants of bitcoin returns. We consider a wide range of various determinants including economic, financial and technology-related factors as well as uncertainty and attention indices. The analysis is conducted using LASSO models estimated using both frequentist and Bayesian methods. We evaluate the ability of these estimators to forecast bitcoin returns. The results indicate that a Bayesian LASSO model that takes into account the stochastic volatility and the leverage effect provides the most accurate forecasts. Using this model we are able to identify alternative drivers of bitcoin returns and analyse the underlying mechanisms that affect bitcoin returns.

Automated summary

The aim of this paper is to identify potential determinants of bitcoin returns. They consider various determinants including economic, financial, technology-related factors, uncertainty, and attention indices. Through the use of LASSO models estimated using both frequentist and Bayesian methods, they evaluate the ability of these estimators to forecast bitcoin returns. The results indicate that a Bayesian LASSO model considering stochastic volatility and the leverage effect provides the most accurate forecasts, allowing for the identification of alternative drivers and analysis of underlying mechanisms affecting bitcoin returns.