Realized covariance matrices (RCs) are an important input to asses the risks involved in different investment allocations and it is thus useful to model and forecast them. To this end generalized autoregressive score (GAS) models are employed in this paper. These models are ideal for comparing...

We introduce a novel covariance estimator that exploits the heteroskedastic nature of financial time series by employing exponential weighted moving averages and shrinking the in-sample eigenvalues through cross-validation. Our estimator is model-agnostic in that we make no assumptions on the...

This paper constructs a new estimator for large covariance matrices by drawing a bridge between the classic Stein (1975) estimator in finite samples and recent progress under large-dimensional asymptotics. The estimator keeps the eigenvectors of the sample covariance matrix and applies shrinkage...