 A Bayesian approach for the determinants of bitcoin returnsTheodore Panagiotidis, Georgios Papapanagiotou and Thanasis Stengos International Review of Financial Analysis, 2024, vol. 91, issue C Abstract: 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. Keywords: Bitcoin; Cryptocurrency; LASSO; Bayesian; CBDC (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:91:y:2024:i:c:s1057521923005549 DOI: 10.1016/j.irfa.2023.103038 Access Statistics for this article International Review of Financial Analysis is currently edited by B.M. Lucey More articles in International Review of Financial Analysis from Elsevier |
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