Results 1 
5 of
5
On the forecasting accuracy of multivariate GARCH models
"... Abstract This paper addresses the selection of multivariate GARCH models in terms of variance matrix forecasting accuracy with a focus on relatively large scale problems. We consider 10 assets from NYSE and NASDAQ and we compare 125 model based onestepahead conditional variance forecasts using th ..."
Abstract

Cited by 22 (3 self)
 Add to MetaCart
(Show Context)
Abstract This paper addresses the selection of multivariate GARCH models in terms of variance matrix forecasting accuracy with a focus on relatively large scale problems. We consider 10 assets from NYSE and NASDAQ and we compare 125 model based onestepahead conditional variance forecasts using the model confidence set (MCS) and the Superior Predicitive Ability (SPA) tests over a period of 10 years. 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. Over relatively unstable periods, i.e. dotcom 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 20072008 financial crisis, accounting for nonstationarity 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
The value of multivariate model sophistication: an application to pricing Dow Jones Industrial Average options
, 2012
"... We assess the predictive accuracy of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set 248 multivariate models that differ in their specificatio ..."
Abstract
 Add to MetaCart
We assess the predictive accuracy of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set 248 multivariate models that differ in their specification of the conditional variance, conditional correlation, and innovation distribution. All models belong to the dynamic conditional correlation class which is particularly suited because it allows to consistently estimate the risk neutral dynamics with a manageable computational effort in relatively large scale problems. It turns out that the most important gain in pricing accuracy comes from increasing the sophistication in the marginal variance processes (i.e. nonlinearity, asymmetry and component structure). Enriching the model with more complex correlation models, and relaxing a Gaussian innovation for a Laplace innovation assumption improves the pricing in a smaller way. Apart from investigating directly the value of model sophistication in terms of dollar losses, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performance.
Robust Ranking of Multivariate GARCH Models by Problem Dimension
, 2012
"... During the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Recent research has begun to examine MGARCH specifications in terms of their outofsample forecasting performance. We provide an empirical comparison of alternative MGARCH models, namely BEKK, DCC, ..."
Abstract
 Add to MetaCart
During the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Recent research has begun to examine MGARCH specifications in terms of their outofsample forecasting performance. We provide an empirical comparison of alternative MGARCH models, namely BEKK, DCC, Corrected DCC (cDCC), CCC, OGARCH Exponentially Weighted Moving Average, and covariance shrinking, using historical data for 89 US equities. We contribute to the literature in several directions. First, we consider a wide range of models, including the recent cDCC and covariance shrinking models. Second, we use a range of tests and approaches for direct and indirect model comparison, including the Model Confidence Set. Third, we examine how the robust model rankings are influenced by the crosssectional dimension of the problem.