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Risks 2015, 3(2), 112-138; doi:10.3390/risks3020112

Custom v. Standardized Risk Models

Quantigic Solutions LLC, 1127 High Ridge Road #135, Stamford, CT 06905, USA
Business School & School of Physics, Free University of Tbilisi, 240, David Agmashenebeli Alley, Tbilisi 0159, Georgia
The Johns Hopkins Carey Business School, 100 International Drive, Baltimore, MD 21202, USA
Author to whom correspondence should be addressed.
Academic Editor: Andrea Consiglio
Received: 19 February 2015 / Accepted: 15 May 2015 / Published: 20 May 2015
(This article belongs to the Special Issue Financial Engineering to Address Complexity)
View Full-Text   |   Download PDF [382 KB, uploaded 28 May 2015]


We discuss when and why custom multi-factor risk models are warranted and give source code for computing some risk factors. Pension/mutual funds do not require customization but standardization. However, using standardized risk models in quant trading with much shorter holding horizons is suboptimal: (1) longer horizon risk factors (value, growth, etc.) increase noise trades and trading costs; (2) arbitrary risk factors can neutralize alpha; (3) “standardized” industries are artificial and insufficiently granular; (4) normalization of style risk factors is lost for the trading universe; (5) diversifying risk models lowers P&L correlations, reduces turnover and market impact, and increases capacity. We discuss various aspects of custom risk model building. View Full-Text
Keywords: risk model; multi-factor; risk factor; short horizon; quant trading; style; industry; specific risk; factor risk; portfolio optimization risk model; multi-factor; risk factor; short horizon; quant trading; style; industry; specific risk; factor risk; portfolio optimization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kakushadze, Z.; Liew, J.K.-S. Custom v. Standardized Risk Models. Risks 2015, 3, 112-138.

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