Incorporating Different Sources of Information for Bayesian Optimal Portfolio Selection

Bibliographic Data

ID19419081
AuthorsOlha Bodnar (0000-0003-1359-3311, Örebro University), Taras Bodnar (0000-0001-7855-8221, Linköping University), Vilhelm Niklasson (0000-0001-9228-0369, Stockholm University, corresponding author)
Year2025
Volume43
Issue2
Pages365-377
Publication date2025-04-03
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.2024.2379361
OpenAlexW4400679083
LanguageEN
References cited49

This article introduces Bayesian inference procedures for tangency portfolios, with a primary focus on deriving a new conjugate prior for portfolio weights. This approach not only enables direct inference about the weights but also seamlessly integrates additional information into the prior specification. Specifically, it automatically incorporates high-frequency returns and a market condition metric (MCM), exemplified by the CBOE Volatility Index (VIX) and Economic Policy Uncertainty Index (EPU), significantly enhancing the decision-making process for optimal portfolio construction. While the Jeffreys’ prior is also acknowledged, emphasis is placed on the advantages and practical applications of the conjugate prior. An extensive empirical study reveals that our method, leveraging this conjugate prior, consistently outperforms existing trading strategies in the majority of examined cases

Bayesian probability · Econometrics · Economics · Financial economics · Portfolio · Computer Science · Financial Markets and Investment Strategies · Market Dynamics and Volatility · Mathematics · Risk and Portfolio Optimization · Artificial Intelligence

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