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

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

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 ta...

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Bibliographic Details
Main Authors: Tu, Jun, Zhou, Guofu
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2010
Subjects:
Asset pricing tests: Investments
Data generating process
t distribution
Bayesian analysis
Business
Online Access:https://ink.library.smu.edu.sg/lkcsb_research/2692
https://doi.org/10.1016/j.jfineco.2003.05.003
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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