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Dynamic portfolio choice under ambiguity and regime switching mean returns

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  • Liu, Hening
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    Abstract

    I examine a continuous-time intertemporal consumption and portfolio choice problem under ambiguity, where expected returns of a risky asset follow a hidden Markov chain. Investors with Chen and Epstein's (2002) recursive multiple priors utility possess a set of priors for unobservable investment opportunities. The optimal consumption and portfolio policies are explicitly characterized in terms of the Malliavin derivatives and stochastic integrals. When the model is calibrated to U.S. stock market data, I find that continuous Bayesian revisions under incomplete information generate ambiguity-driven hedging demands that mitigate intertemporal hedging demands. In addition, ambiguity aversion magnifies the importance of hedging demands in the optimal portfolio policies. Out-of-sample experiments demonstrate the economic importance of accounting for ambiguity.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

    Volume (Year): 35 (2011)
    Issue (Month): 4 (April)
    Pages: 623-640

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    Handle: RePEc:eee:dyncon:v:35:y:2011:i:4:p:623-640

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    Web page: http://www.elsevier.com/locate/jedc

    Related research

    Keywords: Hidden Markov model Malliavin derivative Portfolio choice Recursive multiple priors;

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    Cited by:
    1. Branger, Nicole & Larsen, Linda Sandris & Munk, Claus, 2013. "Robust portfolio choice with ambiguity and learning about return predictability," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1397-1411.
    2. Massimo Guidolin & Hening Liu, 2013. "Ambiguity Aversion and Under-diversification," Working Papers 483, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    3. Massimo Guidolin & Francesca Rinaldi, 2011. "Ambiguity in Asset Pricing and Portfolio Choice: A Review of the Literature," Working Papers 417, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    4. Ling, Aifan & Sun, Jie & Yang, Xiaoguang, 2014. "Robust tracking error portfolio selection with worst-case downside risk measures," Journal of Economic Dynamics and Control, Elsevier, vol. 39(C), pages 178-207.
    5. Branger, Nicole & Kraft, Holger & Meinerding, Christoph, 2014. "Partial information about contagion risk, self-exciting processes and portfolio optimization," Journal of Economic Dynamics and Control, Elsevier, vol. 39(C), pages 18-36.
    6. Larsen, Linda Sandris & Munk, Claus, 2012. "The costs of suboptimal dynamic asset allocation: General results and applications to interest rate risk, stock volatility risk, and growth/value tilts," Journal of Economic Dynamics and Control, Elsevier, vol. 36(2), pages 266-293.
    7. Nyberg, Henri, 2013. "Predicting bear and bull stock markets with dynamic binary time series models," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3351-3363.
    8. Branger, Nicole & Larsen, Linda Sandris, 2013. "Robust portfolio choice with uncertainty about jump and diffusion risk," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5036-5047.
    9. Zimper, Alexander, 2012. "Asset pricing in a Lucas fruit-tree economy with the best and worst in mind," Journal of Economic Dynamics and Control, Elsevier, vol. 36(4), pages 610-628.
    10. Ameur, H. Ben & Prigent, J.L., 2013. "Optimal portfolio positioning under ambiguity," Economic Modelling, Elsevier, vol. 34(C), pages 89-97.

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