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Learning-Induced Securities Price Volatility

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Author Info
Peter Bossaerts

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Abstract

This paper tests whether the high average returns on the S&P 500 index in recent history can be attributed to mistaken expectations (the ex-ante risk premium -- taken to be constant -- is systematically less than the ex-post measured risk premium), or, alternatively, whether can they be explained as the result of selection bias (the U.S. experience is exceptional). The tests reject these hypotheses over the periods 1/81 to 12/97 (p = 0.02), and 1/41-12/60 (p = 0.03). They do not reject over the periods 1/28-12/40 and 1/61-12/80. The tests are based on a bound that the ex-post Sharpe ratios impose on the volatility of the ratio of the market's prior and posterior beliefs about future outcomes. The bound derives from a property of Bayesian learning first noted in an earlier paper. Qualitatively, for the bound not to be violated, higher absolute mean excess returns may need to be accompanied with higher volatility. This should be interpreted as predicting that large price movements (positive as well as negative) may have to be erratic. We confirm this prediction for the S&P 500 data.

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Publisher Info
Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2000 with number 299.

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Date of creation: 05 Jul 2000
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Handle: RePEc:sce:scecf0:299

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Postal: CEF 2000, Departament d'Economia i Empresa, Universitat Pompeu Fabra, Ramon Trias Fargas, 25,27, 08005, Barcelona, Spain
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Web page: http://enginy.upf.es/SCE/
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  1. Larry Epstein & Martin Schneider, 2005. "Ambiguity, Information Quality and Asset Pricing," RCER Working Papers 519, University of Rochester - Center for Economic Research (RCER). [Downloadable!]
    Other versions:
  2. Larry Epstein & Martin Schneider, 2002. "Learning Under Ambiguity," RCER Working Papers 497, University of Rochester - Center for Economic Research (RCER), revised Mar 2005. [Downloadable!]
    Other versions:
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