期刊名称：CORE Discussion Papers / Center for Operations Research and Econometrics (UCL), Louvain
出版年度：2010
卷号：2010
期号：1
出版社：Center for Operations Research and Econometrics (UCL), Louvain
摘要：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. We consider 10
assets from NYSE and NASDAQ and compare 125 model based one-step-ahead conditional variance
forecasts over a period of 10 years using the model confidence set (MCS) and the Superior Predicitive
Ability (SPA) tests. Model per- formances are evaluated using four statistical loss functions which
account for different types and degrees of asymmetry with respect to over/under predictions. When
consid- ering the full sample, MCS results are strongly driven by short periods of high market instability
during which multivariate GARCH models appear to be inaccurate. Over rel- atively unstable periods, i.e.
dot-com bubble, the set of superior models is composed of more sophisticated specifications such as
orthogonal and dynamic conditional correlation (DCC), both with leverage effect in the conditional
variances. However, unlike the DCC models, our results show that the orthogonal specifications tend to
underestimate the conditional variance. Over calm periods, a simple assumption like constant conditional
correlation and symmetry in the conditional variances cannot be rejected. Finally, during the 2007-2008
financial crisis, accounting for non-stationarity in the conditional variance process generates superior
forecasts. The SPA test suggests that, independently from the period, the best models do not provide
significantly better forecasts than the DCC model of Engle (2002) with leverage in the conditional
variances of the returns.
关键词：Keywords: variance matrix, forecasting, multivariate GARCH, loss function, model confidence set,
superior predictive ability.
