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Russian-Doll Risk Models

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  • Zura Kakushadze

Abstract

We give a simple explicit algorithm for building multi-factor risk models. It dramatically reduces the number of or altogether eliminates the risk factors for which the factor covariance matrix needs to be computed. This is achieved via a nested "Russian-doll" embedding: the factor covariance matrix itself is modeled via a factor model, whose factor covariance matrix in turn is modeled via a factor model, and so on. We discuss in detail how to implement this algorithm in the case of (binary) industry classification based risk factors (e.g., "sector -> industry -> sub-industry"), and also in the presence of (non-binary) style factors. Our algorithm is particularly useful when long historical lookbacks are unavailable or undesirable, e.g., in short-horizon quant trading.

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  • Zura Kakushadze, 2014. "Russian-Doll Risk Models," Papers 1412.4342, arXiv.org, revised Nov 2017.
    • Handle: RePEc:arx:papers:1412.4342
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    Cited by:

    1. Zura Kakushadze, 2015. "Heterotic Risk Models," Papers 1508.04883, arXiv.org, revised Jan 2016.
    2. Zura Kakushadze, 2015. "Shrinkage = Factor Model," Papers 1511.04764, arXiv.org, revised Dec 2015.
    3. Zura Kakushadze & Willie Yu, 2016. "Statistical Risk Models," Papers 1602.08070, arXiv.org, revised Jan 2017.
    4. Zura Kakushadze & Willie Yu, 2017. "Open Source Fundamental Industry Classification," Data, MDPI, vol. 2(2), pages 1-77, June.
    5. Zura Kakushadze & Willie Yu, 2017. "Open Source Fundamental Industry Classification," Papers 1706.04210, arXiv.org, revised Dec 2017.
    6. Zura Kakushadze & Willie Yu, 2016. "Multifactor Risk Models and Heterotic CAPM," Papers 1602.04902, arXiv.org, revised Mar 2016.

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