Incorporating different sources of information for Bayesian optimal portfolio selection

• This paper introduces Bayesian inference procedures for tangency portfolios, with a primary focus on deriving a new conjugate prior for portfolioweights. This approach not only enables direct inference about the weightsbut also seamlessly integrates additional information into the prior specification. Specifically, it automatically incorporates high-frequency returns and amarket 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 theJeffreys prior is also acknowledged, emphasis is placed on the advantages andpractical applications of the conjugate prior. An extensive empirical studyreveals that our method, leveraging this conjugate prior, consistently outperforms existing trading strategies in the majority of examined cases.

Ämnesord

NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)

NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

Nyckelord

Conjugate prior

EPU

high-frequency data

Jeffreys prior

value-weighted portfolio

VIX

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