Heterotic Risk Models

TL;DR

This paper introduces heterotic risk models for equities that combine industry classification, principal component diagonality, and size reduction techniques, leading to more stable and effective risk modeling and alpha optimization.

Contribution

It presents a complete algorithm and source code for heterotic risk models, integrating industry classification and principal components to improve out-of-sample stability and performance.

Findings

Heterotic risk models outperform traditional models in Sharpe ratio optimization.

The approach reduces covariance matrix size significantly.

Models demonstrate improved out-of-sample stability.

Abstract

We give a complete algorithm and source code for constructing what we refer to as heterotic risk models (for equities), which combine: i) granularity of an industry classification; ii) diagonality of the principal component factor covariance matrix for any sub-cluster of stocks; and iii) dramatic reduction of the factor covariance matrix size in the Russian-doll risk model construction. This appears to prove a powerful approach for constructing out-of-sample stable short-lookback risk models. Thus, for intraday mean-reversion alphas based on overnight returns, Sharpe ratio optimization using our heterotic risk models sizably improves the performance characteristics compared to weighted regressions based on principal components or industry classification. We also give source code for: a) building statistical risk models; and ii) Sharpe ratio optimization with homogeneous linear constraints and position bounds.