  We consider the well-studied problem of predicting the time-varying
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 over all, 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.
