Data-Generating Process Uncertainty: What Difference Does It Make in Portfolio Decisions

Publication Type

Journal Article

Publication Date

4-2010

Abstract

As the usual normality assumption is firmly rejected by the data, investors encounter a data-generating process (DGP) uncertainty in making investment decisions. In this paper, we propose a novel way to incorporate uncertainty about the DGP into portfolio analysis. We find that accounting for fat tails leads to nontrivial changes in both parameter estimates and optimal portfolio weights, but the certainty–equivalent losses associated with ignoring fat tails are small. This suggests that the normality assumption works well in evaluating portfolio performance for a mean-variance investor.

Keywords

Asset pricing tests: Investments, Data generating process, t distribution, Bayesian analysis

Discipline

Business

Research Areas

Finance

Publication

Journal of Financial Economics

Volume

72

Issue

2

First Page

385

Last Page

421

ISSN

0304-405X

Identifier

10.1016/j.jfineco.2003.05.003

Publisher

Elsevier

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