A Bayesian approach for the determinants of bitcoin returns

Theodore Panagiotidis, Georgios Papapanagiotou and Thanasis Stengos

Discussion Paper Series from Department of Economics, University of Macedonia

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)
JEL-codes: C11 D80 G12 G15 (search for similar items in EconPapers)
Date: 2023-05, Revised 2023-05
New Economics Papers: this item is included in nep-pay
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http://econwp.uom.gr/pdf/dp052023.pdf

Related works:
Journal Article: A Bayesian approach for the determinants of bitcoin returns (2024)
Working Paper: A Bayesian approach for the determinants of bitcoin returns (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:mcd:mcddps:2023_05

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