Paper Info
Title
The impact of general non-parametric volatility functions in multivariate GARCH models
Abstract
Recent studies have revealed that financial volatilities and correlations move together over time across assets and markets. The main effort has been on improving the flexibility of conditional correlation dynamics, while maintaining computational feasibility for large estimation problems. However, since in such models conditional covariances are the product of conditional correlations and individual volatilities, it is plausible that improving the estimation of individual volatilities will lead to better covariance forecasts, too. Functional gradient descent (FGD) has already been shown to improve substantially in-sample and out-of-sample covariance accuracy in the very simple constant conditional correlation (CCC) setting. Following this direction, the impact of FGD volatility estimates is tested in several multivariate GARCH settings, both at the multivariate and at the univariate portfolio levels. In particular, improving conditional correlations and improving individual volatilities are compared, to establish which effect produces the best fits and predictions for conditional covariances.
Year
DOI
Venue
2006
10.1016/j.csda.2005.06.006
Computational Statistics & Data Analysis
Keywords
Field
DocType
functional gradient descent (fgd) estimation,conditional covariances,multivariate garch setting,models conditional covariances,individual volatility,conditional correlation,general non-parametric volatility function,large estimation problem,dynamic conditional correlations,multivariate garch models,simple constant conditional correlation,covariance forecast,asymmetric non-linear volatility,fgd volatility estimate,multivariate garch model,conditional correlation dynamic,gradient descent
Econometrics,Conditional variance,Conditional probability distribution,Multivariate statistics,Nonparametric statistics,Univariate,Statistics,Autoregressive conditional heteroskedasticity,Volatility (finance),Mathematics,Covariance
Journal
Volume
Issue
ISSN
50
11
Computational Statistics and Data Analysis
Citations 
PageRank 
References 
5
1.20
0
Authors
1
Name
Order
Citations
PageRank
Francesco Audrino1143.36