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Recursive `thick´ modeling of excess returns and portfolio allocation

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  • Carlo A. Favero
  • Marco Aiolfi
  • Giorgio Primiceri

Abstract

This paper explores the extent to which predictability of asset returns could be exploited for dynamic portfolio allocation among several (seven) assets taking model uncertainty explicitly into account.We consider model uncertainty when solving the problem of a representative fund manager who allocates funds between stock and bonds in three geographical areas: Europe, USA and Japan. We consider explicitly model uncertainty by implementing ´thick modelling´ to derive the average portfolio allocation generated by the recursively selected top fifty per cent of models in term of adjusted R-squared The portfolio allocation based on this strategy leads to systematic over-performance with respect to optimal portfolio allocation among several assets is based on the predictions of the best model as selected by the adjusted R-squared . Such over performance is mainly attributable to a reduction in the volatility of the returns on the selected portfolios. Thick modelling leads also to systematic replication, but not to over-performance, of a typical benchmark\ portfolio for our asset allocation problem.

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Bibliographic Info

Paper provided by IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University in its series Working Papers with number 197.

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Handle: RePEc:igi:igierp:197

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Cited by:
  1. Pesaran, M Hashem & Zaffaroni, Paolo, 2005. "Model Averaging and Value-at-Risk Based Evaluation of Large Multi-Asset Volatility Models for Risk Management," CEPR Discussion Papers 5279, C.E.P.R. Discussion Papers.
  2. Dell'Aquila, Rosario & Ronchetti, Elvezio, 2006. "Stock and bond return predictability: the discrimination power of model selection criteria," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1478-1495, March.
  3. M. Hashem Pesaran & Christoph Schleicher & Paolo Zaffaroni, 2008. "Model Averaging in Risk Management with an Application to Futures Markets," CESifo Working Paper Series 2231, CESifo Group Munich.
  4. Marco Aiolfi & Carlo Ambrogio Favero, . "Model Uncertainty, Thick Modelling and the predictability of Stock Returns," Working Papers 221, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  5. Rosario Dell'Aquila & Elvezio Ronchetti, 2004. "Stock and Bond Return Predictability : The Discrimination Power of Model Selection Criteria," Research Papers by the Institute of Economics and Econometrics, Geneva School of Economics and Management, University of Geneva 2004.05, Institut d'Economie et Econométrie, Université de Genève.
  6. Vanina Forget, 2012. "Doing well and doing good: a multi-dimensional puzzle," Working Papers hal-00672037, HAL.

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