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A Bayesian approach for the determinants of bitcoin returns

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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.

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  • Theodore Panagiotidis & Georgios Papapanagiotou & Thanasis Stengos, 2023. "A Bayesian approach for the determinants of bitcoin returns," Discussion Paper Series 2023_05, Department of Economics, University of Macedonia, revised May 2023.
    • Handle: RePEc:mcd:mcddps:2023_05
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    More about this item

    Keywords

    bitcoin; cryptocurrency; LASSO; Bayesian; CBDC;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General

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