Random Matrix Filtering in Portfolio Optimization

TL;DR

This paper explores a filtering method for empirical covariance matrices in finance, aiming to reduce noise and improve portfolio optimization accuracy through simulation-based validation.

Contribution

It introduces and tests a filtering procedure for noisy covariance matrices, enhancing their reliability in portfolio optimization tasks.

Findings

Filtering improves covariance matrix estimation accuracy.

Simulation demonstrates the method's effectiveness.

Potential for better portfolio risk management.

Abstract

We study empirical covariance matrices in finance. Due to the limited amount of available input information, these objects incorporate a huge amount of noise, so their naive use in optimization procedures, such as portfolio selection, may be misleading. In this paper we investigate a recently introduced filtering procedure, and demonstrate the applicability of this method in a controlled, simulation environment.