Optimal Portfolio Choice under Decision-Based Model Combinations

Davide Pettenuzzo and Francesco Ravazzolo

No 80, Working Papers from Brandeis School of Business and Economics

Abstract: We propose a novel Bayesian model combination approach where the combination weights depend on the past forecasting performance of the individual models entering the combina- tion through a utility-based objective function. We use this approach in the context of stock return predictability and optimal portfolio decisions, and investigate its forecasting perfor- mance relative to a host of existing combination schemes. We find that our method produces markedly more accurate predictions than the existing model combinations, both in terms of statistical and economic measures of out-of-sample predictability. We also investigate the incremental role of our model combination method in the presence of model instabilities, by considering predictive regressions that feature time-varying regression coefficients and volatil- ity. We find that the gains from using our model combination method increase significantly when we allow for instabilities in the individual models entering the combination.

Keywords: Bayesian econometrics; Time-varying parameters; Model combinations; Port- folio choice. (search for similar items in EconPapers)
JEL-codes: C11 C22 G11 G12 (search for similar items in EconPapers)
Pages: 58 pages
Date: 2014-10
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-for and nep-upt
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Related works:
Journal Article: Optimal Portfolio Choice Under Decision‐Based Model Combinations (2016)
Working Paper: Optimal Portfolio Choice under Decision-Based Model Combinations (2015)
Working Paper: Optimal portfolio choice under decision-based model combinations (2014)
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