Short-Term Risk and Adapting Covariance Models to Current Market Conditions

9 Pages Posted: 26 Sep 2014 Last revised: 8 Apr 2021

See all articles by Anish Shah

Anish Shah

Investment Grade Modeling; Brown University - Division of Applied Mathematics

Date Written: September 3, 2015

Abstract

Covariance models of stock returns appear throughout the investment process, e.g., forecasting portfolio risk, hedging, constructing mean-variance optimal portfolios, and algorithmic trading. Typically built from historic time-series, they estimate the past but–because markets and regimes continually change–not the present and future.

There are non-time-series, instantaneous ways to infer or predict volatility, e.g., option implied volatility, intra-period trading range, machine learning on alternative data. This paper describes a conceptual framework and algorithm for incorporating these inferences into any linear factor covariance model so that it represents one's beliefs about future behavior instead of echoing the past.

Keywords: Covariance, Factor Models, Nowcasting, Projection Algorithms

JEL Classification: C00, C11, C53, G19

Suggested Citation

Shah, Anish, Short-Term Risk and Adapting Covariance Models to Current Market Conditions (September 3, 2015). Available at SSRN: https://ssrn.com/abstract=2501071 or http://dx.doi.org/10.2139/ssrn.2501071

Anish Shah (Contact Author)

Investment Grade Modeling ( email )

Cambridge, MA 02139
United States

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

Brown University - Division of Applied Mathematics

182 George St
Providence, RI 02912
United States

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