Uncertain Covariance Models and Uncertainty-Penalized Portfolio Optimization

20 Pages Posted: 9 Jun 2015 Last revised: 4 Apr 2023

See all articles by Anish Shah

Anish Shah

Investment Grade Modeling; Brown University - Division of Applied Mathematics

Date Written: June 9, 2015

Abstract

Covariance appears throughout investment management, e.g., risk reporting, portfolio optimization, risk parity, smart beta, algorithmic trading, and hedging. It is often represented via multi-factor model. The imposed structure - comovement through sensitivity to common factors, diagonal residuals - offers advantages such as less data for estimation, easy proxying, and removing transient behavior and low eigenvalue directions. Nevertheless, parameter values are inferred and imperfect. Though it's common to ignore the error and proceed from point estimates, the spread of possible values is considered in Bayesian techniques and robust optimization. The research in this paper connects to both: Forecasts of mean and variance of the factor model parameters propagate to forecast the mean and variance of the covariance matrix. Using the result, optimization is performed to maximize portfolio utility a given number of standard deviations below the mean.

Keywords: Covariance, Estimation error, Multi-factor models, Portfolio optimization, Regularization, Uncertainty

JEL Classification: C00, C11, C53, G19

Suggested Citation

Shah, Anish, Uncertain Covariance Models and Uncertainty-Penalized Portfolio Optimization (June 9, 2015). Available at SSRN: https://ssrn.com/abstract=2616109 or http://dx.doi.org/10.2139/ssrn.2616109

Anish Shah (Contact Author)

Investment Grade Modeling ( email )

Cambridge, MA 02139
United States

HOME PAGE: http://www.linkedin.com/in/anishrshah

Brown University - Division of Applied Mathematics

182 George St
Providence, RI 02912
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
507
Abstract Views
2,500
Rank
102,252
PlumX Metrics