Robust estimation of style analysis coefficients

Abstract

Style analysis, as originally proposed by Sharpe, is an asset class factor model aimed at obtaining information on the internal allocation of a financial portfolio and at comparing portfolios with similar investment strategies. The classical approach is based on a constrained linear regression model and the coefficients are usually estimated exploiting a least squares procedure. This solution clearly suffers from the presence of outlying observations. The aim of the paper is to investigate the use of a robust estimator for style coefficients based on constrained quantile regression. The performance of the novel procedure is evaluated by means of a Monte Carlo study where different sets of outliers (both in the constituent returns and in the portfolio returns) have been considered.