Better Risk and Performance Estimates with Factor Model Monte Carlo
21 Pages Posted: 20 Jul 2013 Last revised: 10 Jun 2016
Date Written: July 18, 2013
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
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