Abstract
Most equity risk models applied in practice assume stable return correlations over time. However, there is considerable evidence suggesting that correlations among stock returns and hence, variance–covariance matrices (VCMs) become unstable over time. In this paper, we account for correlation instabilities in US stock returns and derive VCMs from time-varying factor model estimates. To do so, we use three different estimation approaches: (1) moving window least squares, (2) flexible least squares and (3) the random walk model. Our empirical results suggest that a time-varying estimation of return correlations fits the data considerably better than time-invariant estimation and thus, increases the efficiency of risk estimation and portfolio selection.
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