Predictive Decision Synthesis for Portfolios: Betting on Better Models

Emily Tallman () and Mike West ()
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Emily Tallman: Duke University, Department of Statistical Science
Mike West: Duke University, The Arts & Sciences Distinguished Professor Emeritus of Statistics & Decision Sciences, Department of Statistical Science

A chapter in Recent Developments in Bayesian Econometrics and Their Applications, 2025, pp 223-249 from Springer

Abstract: Abstract We discuss and develop Bayesian dynamic modeling and predictive decision synthesis for portfolio analysis. The context involves model uncertainty with a set of candidate models for financial time series with main foci in sequential learning, forecasting, and recursive decisions for portfolio reinvestments. The foundational perspective of Bayesian predictive decision synthesis (BPDS) defines novel, operational analysis and resulting predictive and decision outcomes. A detailed case study of BPDS in financial forecasting of international exchange rate time series and portfolio rebalancing, with resulting BPDS-based decision outcomes compared to traditional Bayesian analysis, exemplifies and highlights the practical advances achievable under the expanded, subjective Bayesian approach that BPDS defines.

Keywords: Bayesian predictive decision synthesis; Dynamic models; Financial forecasting; FX portfolios; Model uncertainty; Multivariate volatility; Portfolio decisions; Target portfolios; TV-VAR models (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-00110-8_10

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DOI: 10.1007/978-3-032-00110-8_10

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