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Predictive Decision Synthesis for Portfolios: Betting on Better Models

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  • Emily Tallman
  • Mike West

Abstract

We discuss and develop Bayesian dynamic modelling 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.

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  • Emily Tallman & Mike West, 2024. "Predictive Decision Synthesis for Portfolios: Betting on Better Models," Papers 2405.01598, arXiv.org.
    • Handle: RePEc:arx:papers:2405.01598
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    References listed on IDEAS

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    1. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
    2. Sune Karlsson & Tor Jacobson, 2004. "Finding good predictors for inflation: a Bayesian model averaging approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(7), pages 479-496.
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    Cited by:

    1. Tony Chernis & Gary Koop & Emily Tallman & Mike West, 2024. "Decision Synthesis in Monetary Policy," Staff Working Papers 24-30, Bank of Canada.

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