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Long-Run Risk is the Worst-Case Scenario: Ambiguity Aversion and Non-Parametric Estimation of the Endowment Process

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  • Bidder, Rhys

    () (Federal Reserve Bank of San Francisco)

  • Dew-Becker, Ian

    () (Kellogg School of Management, Northwestern University)

Abstract

We study an agent who is unsure of the dynamics of consumption growth. She estimates her consumption process non-parametrically to place minimal restrictions on dynamics. We analytically show that the worst-case model that she uses for pricing, given a penalty on deviations from the point estimate, is a model with long-run risks. This result cannot in general be matched in a fixed model with only parameter uncertainty. With a single parameter determining risk preferences, the model generates high and volatile risk premia and matches R2s from return forecasting regressions, even though risk aversion is equal to 5.3 and the worst-case dynamics are statistically nearly indistinguishable from the true model.

Suggested Citation

  • Bidder, Rhys & Dew-Becker, Ian, 2014. "Long-Run Risk is the Worst-Case Scenario: Ambiguity Aversion and Non-Parametric Estimation of the Endowment Process," Working Paper Series 2014-16, Federal Reserve Bank of San Francisco, revised 04 May 2016.
  • Handle: RePEc:fip:fedfwp:2014-16
    DOI: 10.24148/wp2014-16
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    Cited by:

    1. Andrew McKenna & Rhys Bidder, 2014. "Robust Stress Testing," 2014 Meeting Papers 853, Society for Economic Dynamics.

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