Risk Evaluations with Robust Approximate Factor Models

48 Pages Posted: 17 Nov 2016 Last revised: 28 Sep 2019

See all articles by Ray Y. Chou

Ray Y. Chou

Academia Sinica

Tso-Jung Yen

Academia Sinica - Institute of Statistical Science

Yu-Min Yen

Department of International Business, National Chengchi University

Date Written: May 25, 2016

Abstract

Approximate factor models and their extensions are widely used in economic analysis and forecasting due to their ability to extracting useful information from a large number of relevant variables. In these models, candidate predictors are typically subject to some common components. In this paper we propose a new method for robustly estimating the approximate factor models and use it in risk assessments. We consider a class of approximate factor models in which the candidate predictors are additionally subject to idiosyncratic large uncommon components such as jumps or outliers. By assuming that occurrences of the uncommon components are rare, we develop an estimation procedure to simultaneously disentangle and estimate the common and uncommon components. We then use the proposed method to investigate whether risks from the latent factors are priced for expected returns of Fama and French 100 size and book-to-market ratio portfolios. We find that while the risk from the common factor is priced for the 100 portfolios, the risks from the idiosyncratic factors are not. However, we find that model uncertainty risks of the idiosyncratic factors are priced, suggesting that with effective diversifications, only the predictable idiosyncratic risks can be reduced, but the unpredictable ones may still exist. We also illustrate how the proposed method can be adopted on evaluating value at risk (VaR) and find it can delivery comparable results as the conventional methods on VaR evaluations.

Keywords: Approximate Factor Model, PCA, Norm Penalty, Common Factor, Idiosyncratic Risk, VaR

JEL Classification: C58, G12, G31

Suggested Citation

Chou, Ray Y. and Yen, Tso-Jung and Yen, Yu-Min, Risk Evaluations with Robust Approximate Factor Models (May 25, 2016). Journal of Banking and Finance, Vol. 82, 2017, Available at SSRN: https://ssrn.com/abstract=2871044

Ray Y. Chou

Academia Sinica ( email )

128 Academia Road, Section 2
Nankang
Taipei, 11529
Taiwan

Tso-Jung Yen

Academia Sinica - Institute of Statistical Science ( email )

Nankang
Taipei, 11529
Taiwan

Yu-Min Yen (Contact Author)

Department of International Business, National Chengchi University ( email )

64, Section 2, Zhi-nan Road
Wenshan
Taipei, 116
Taiwan

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