A Simple Method for Predicting Covariance Matrices of Financial Returns

Kasper Johansson, Mehmet G. Ogut, Markus Pelger, Thomas Schmelzer and Stephen Boyd

Foundations and Trends(R) in Econometrics, 2023, vol. 12, issue 4, 324-407

Abstract: We consider the well-studied problem of predicting the timevarying covariance matrix of a vector of financial returns. Popular methods range from simple predictors like rolling window or exponentially weighted moving average (EWMA) to more sophisticated predictors such as generalized autoregressive conditional heteroscedastic (GARCH) type methods. Building on a specific covariance estimator suggested by Engle in 2002, we propose a relatively simple extension that requires little or no tuning or fitting, is interpretable, and produces results at least as good as MGARCH, a popular extension of GARCH that handles multiple assets. To evaluate predictors we introduce a novel approach, evaluating the regret of the log-likelihood over a time period such as a quarter. This metric allows us to see not only how well a covariance predictor does overall, but also how quickly it reacts to changes in market conditions. Our simple predictor outperforms MGARCH in terms of regret. We also test covariance predictors on downstream applications such as portfolio optimization methods that depend on the covariance matrix. For these applications our simple covariance predictor and MGARCH perform similarly.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1561/0800000047 (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:now:fnteco:0800000047

Access Statistics for this article

More articles in Foundations and Trends(R) in Econometrics from now publishers
Bibliographic data for series maintained by Lucy Wiseman ().