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Dynamic Asset Allocation with Ambiguous Return Predictability

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  • Hui Chen

    ()
    (MIT Sloan School of Management)

  • Nengjiu Ju

    ()
    (Department of Finance, the Hong Kong University of Science and Technology)

  • Jianjun Miao

    ()
    (Department of Economics, Boston University)

Abstract

We study an investor's optimal consumption and portfolio choice problem when he confronts with two possibly misspecified submodels of stock returns: one with IID returns and the other with predictability. We adopt a generalized recursive ambiguity model to accommodate the investor's aversion to model uncertainty. The investor deals with specification doubts by slanting his beliefs about submodels of returns pessimistically, causing his investment strategy to be more conservative than the Bayesian strategy. This effect is large for high and low values of the predictive variable. Unlike in the Bayesian framework, the hedging demand against model uncertainty may cause the investor's stock allocations to first decrease sharply and then increase with his prior probability of the IID model, even when the expected stock return under the IID model is lower than under the predictability model. Adopting suboptimal investment strategies by ignoring model uncertainty can lead to sizable welfare costs.

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Bibliographic Info

Paper provided by Boston University - Department of Economics in its series Boston University - Department of Economics - Working Papers Series with number wp2009-015.

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Length: 46
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Handle: RePEc:bos:wpaper:wp2009-015

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Keywords: generalized recursive ambiguity utility; ambiguity aversion; model uncertainty; learning; portfolio choice; robustness; return predictability;

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Cited by:
  1. Massimo Guidolin & Francesca Rinaldi, 2010. "Ambiguity in asset pricing and portfolio choice: a review of the literature," Working Papers 2010-028, Federal Reserve Bank of St. Louis.
  2. Jessica A. Wachter & Missaka Warusawitharana, 2011. "What is the Chance that the Equity Premium Varies over Time? Evidence from Regressions on the Dividend-Price Ratio," NBER Working Papers 17334, National Bureau of Economic Research, Inc.
  3. Jianjun Miao & Bin Wei & Hao Zhou, 2012. "Ambiguity Aversion and Variance Premium," Boston University - Department of Economics - Working Papers Series WP2012-009, Boston University - Department of Economics.
  4. Jessica A. Wachter, 2010. "Asset Allocation," Annual Review of Financial Economics, Annual Reviews, vol. 2(1), pages 175-206, December.
  5. Liu, Hening, 2011. "Dynamic portfolio choice under ambiguity and regime switching mean returns," Journal of Economic Dynamics and Control, Elsevier, vol. 35(4), pages 623-640, April.
  6. Nengjiu Ju & Jianjun Miao, . "Ambiguity, Learning, and Asset Returns," Boston University - Department of Economics - Working Papers Series wp2009-014, Boston University - Department of Economics.
  7. Missaka Warusawitharana & Jessica A. Wachter, 2009. "What is the chance that the equity premium varies over time? evidence from predictive regressions," Finance and Economics Discussion Series 2009-26, Board of Governors of the Federal Reserve System (U.S.).
  8. Massimo Guidolin & Hening Liu, 2013. "Ambiguity Aversion and Under-diversification," Working Papers 483, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  9. Martin Schneider, 2010. "The Research Agenda: Martin Schneider on Multiple Priors Preferences and Financial Markets," EconomicDynamics Newsletter, Review of Economic Dynamics, vol. 11(2), April.
  10. Ulrich, Maxim, 2013. "Inflation ambiguity and the term structure of U.S. Government bonds," Journal of Monetary Economics, Elsevier, vol. 60(2), pages 295-309.
  11. Jayant Ganguli & Scott Condie & Philipp Karl Illeditsch, 2012. "Information Inertia," Economics Discussion Papers 719, University of Essex, Department of Economics.
  12. Gonçalo Faria & João Correia-da-Silva, 2012. "Is Stochastic Volatility relevant for Dynamic Portfolio Choice under Ambiguity?," FEP Working Papers 472, Universidade do Porto, Faculdade de Economia do Porto.

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