Better Risk and Performance Estimates with Factor Model Monte Carlo

21 Pages Posted: 20 Jul 2013 Last revised: 10 Jun 2016

See all articles by Yindeng Jiang

Yindeng Jiang

University of Washington Investment Management Company

R. Douglas Martin

University of Washington

Date Written: July 18, 2013

Abstract

A common problem in asset and portfolio risk and performance analysis is that the manager has such a short history of asset returns that risk and performance measure estimates are quite unreliable. But the manager has available long histories of many risk factors and can use a subset of them to construct a high R-squared risk-factor model for the asset returns. We introduce a simple method of simulating from such a factor-model that yields considerably improved accuracy of risk and performance measures, and show that it is important to use a statistically justified method of choosing the risk factors. The resulting factor-model Monte Carlo (FMMC) method works well by virtue of adequately reflecting the non-normality of the factor and asset returns, and by borrowing strength from the correlation between the risk factors and the asset returns.

Keywords: risk and performance measures, estimation accuracy, short returns histories, nonnormality, factor models, model selection, bootstrap

JEL Classification: C13, C15

Suggested Citation

Jiang, Yindeng and Martin, R. Douglas, Better Risk and Performance Estimates with Factor Model Monte Carlo (July 18, 2013). Journal of Risk, June 2015, Available at SSRN: https://ssrn.com/abstract=2295602 or http://dx.doi.org/10.2139/ssrn.2295602

Yindeng Jiang (Contact Author)

University of Washington Investment Management Company ( email )

Seattle, WA 98195
United States

R. Douglas Martin

University of Washington ( email )

Applied Mathematics & Statistics
Seattle, WA 98195
United States

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