Heterotic Risk Models

Zura Kakushadze

Papers from arXiv.org

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.

Date: 2015-08, Revised 2016-01
New Economics Papers: this item is included in nep-cmp, nep-ger and nep-rmg
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Citations: View citations in EconPapers (8)

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

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1508.04883

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