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Mutual Fund Theorem for Ambiguity-Averse Investors and the Optimality of the Market Portfolio

Author

Listed:
  • Chiaki Hara

    (Kyoto Institute of Economic Research, Kyoto University)

  • Toshiki Honda

    (Graduate School of International Corporate Strategy, Hitotsubashi University)

Abstract

We study the optimal portfolio choice problem for an ambiguity-averse investor having a utility function of the form of Klibanoff, Marinacci, and Mukerji (2005) and Maccheroni, Marinacci, and Ru no (2013) in an ambiguity-inclusive CARA- normal setup. We extend the mutual fund theorem to accommodate ambiguity, identify a necessary and sufficient condition for a given portfolio to be optimal for some ambiguity-averse investor, characterize all the ambiguity structure under which the given portfolio is optimal, and nd the minimal ones in two senses to be made precise. We also calculate the minimal ambiguity structures based on the U.S. equity market data and nd the smallest coefficient of ambiguity aversion with which the market portfolio is optimal is equal to 9.31.

Suggested Citation

  • Chiaki Hara & Toshiki Honda, 2016. "Mutual Fund Theorem for Ambiguity-Averse Investors and the Optimality of the Market Portfolio," KIER Working Papers 943, Kyoto University, Institute of Economic Research.
  • Handle: RePEc:kyo:wpaper:943
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    File URL: http://www.kier.kyoto-u.ac.jp/DP/DP943.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Ambiguity aversion; optimal portfolio; mutual fund theorem; FF6; portfolios; market portfolio.;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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