'It is now widely accepted that excess returns are predictable' (Lettau and Lud-vigson, 2001). However, there also have been authors nding otherwise, claiming that most of the predictive models are 'unstable or even spurious' (Welch and Goyal, 2008). This paper proposes a model of learning through which we can investigate 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 di erential equations that generalizes the Kalman-Bucy lter 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 signi cant
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.
[2015년 제 2차] Learning under Ambiguous Reversion
작성자 : 관리자
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게시일 :
2015-06-03