Systematic and multifactor risk models revisited

TL;DR

This paper revisits systematic and multifactor risk models using advanced methods from signal processing and control theory, demonstrating their effectiveness through computer experiments.

Contribution

It introduces novel applications of signal processing and control methods to improve risk modeling, addressing common criticisms.

Findings

Successful computer experiments validate the approach

Enhanced robustness of risk models demonstrated

Potential for improved risk assessment techniques

Abstract

Systematic and multifactor risk models are revisited via methods which were already successfully developed in signal processing and in automatic control. The results, which bypass the usual criticisms on those risk modeling, are illustrated by several successful computer experiments.