On The Forecasting Accuracy Of Multivariate Garch Models

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This paper addresses the question of the selection of multivariate GARCH models in terms of variance matrix forecasting accuracy with a particular focus on relatively large scale problems. The authors consider 10 assets from NYSE and NASDAQ and compare 125 models based one-step-ahead conditional variance forecasts over a period of 10 years using the Model Confidence Set (MCS) and the Superior Predictive Ability (SPA) tests. Model performances are evaluated using four statistical loss functions which account for different types and degrees of asymmetry with respect to over/under predictions. When considering the full sample, MCS results are strongly driven by short periods of high market instability during which multivariate GARCH models appear to be inaccurate.