Learning under Ambiguous Reversion

Research output: Conference Papers (RGC: 31A, 31B, 32, 33)32_Refereed conference paper (no ISBN/ISSN)peer-review

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Author(s)

Detail(s)

Original languageEnglish
Publication statusPublished - Aug 2015
Externally publishedYes

Conference

TitleThe 11th Annual Conference of the Asia-Pacific Association of Derivatives (APAD 2015)
LocationWestin Chosun Hotel
PlaceKorea, Republic of
CityBusan
Period24 - 25 August 2015

Abstract

While “it is now widely accepted that excess returns are predictable” (Lettau and Ludvigson, 2001, Journal of Finance), there also are authors finding otherwise, claiming that the predictive models are unstable or even spurious. This paper proposes a model of learning through which we can study the behavior of an investor under such ambiguous circumstances. The proposed model describes how observations are translated into a set of probability measures that represents the investor’s view of the immediate future; and I explicitly characterize the set’s evolution up to a system of differential equations that generalizes the Kalman-Bucy filter in the presence of ambiguity. The model of learning is then applied to the portfolio choice problem of a log investor; and learning under ambiguity is seen to have a significant effect on hedging demand: under a reasonable calibration, the optimal demand for the risky asset at zero instantaneous equity premium decreases, as the investor loses confidence, by half of wealth.

Citation Format(s)

Learning under Ambiguous Reversion. / Choi, Hongseok.

2015. Paper presented at The 11th Annual Conference of the Asia-Pacific Association of Derivatives (APAD 2015), Busan, Korea, Republic of.

Research output: Conference Papers (RGC: 31A, 31B, 32, 33)32_Refereed conference paper (no ISBN/ISSN)peer-review