Heterotic Risk Models

Wilmott Magazine 2015(80) (2015) 40-55

41 Pages Posted: 30 Apr 2015 Last revised: 25 Jan 2016

See all articles by Zura Kakushadze

Zura Kakushadze

Quantigic Solutions LLC; Free University of Tbilisi

Date Written: April 30, 2015


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.

Keywords: multi-factor risk model, risk factors, optimization, regression, specific risk, factor risk, mean-reversion, covariance matrix, correlation matrix, factor loadings, dollar neutrality, style factors, industry factors, principal components

JEL Classification: G00

Suggested Citation

Kakushadze, Zura, Heterotic Risk Models (April 30, 2015). Wilmott Magazine 2015(80) (2015) 40-55, Available at SSRN: https://ssrn.com/abstract=2600798 or http://dx.doi.org/10.2139/ssrn.2600798

Zura Kakushadze (Contact Author)

Quantigic Solutions LLC ( email )

680 E Main St #543
Stamford, CT 06901
United States
6462210440 (Phone)
6467923264 (Fax)

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

Free University of Tbilisi ( email )

Business School and School of Physics
240, David Agmashenebeli Alley
Tbilisi, 0159

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