Asymmetric multivariate normal mixture GARCH

Autor: Marc S. Paolella, Markus Haas, Stefan Mittnik
Přispěvatelé: University of Zurich, Haas, Markus
Jazyk: angličtina
Rok vydání: 2008
Předmět:
Statistics and Probability
Multivariate statistics
Autoregressive conditional heteroskedasticity
ARCH-Modell
Conditional Volatility
Multivariate normal distribution
Finite Normal Mixtures
C51
Kapitalertrag
2604 Applied Mathematics
Econometrics
ddc:330
Value at Risk
G10
Multivariate Analyse
G11
2613 Statistics and Probability
Conditional Volatility
Finite Normal Mixtures
Multivariate GARCH
Leverage Effect

C32
Leverage Effect
USA
Mathematics
Analysis of covariance
Applied Mathematics
jel:C51
Conditional probability distribution
jel:C32
Covariance
Börsenkurs
Volatilität
10003 Department of Banking and Finance
jel:G10
330 Economics
jel:G11
Computational Mathematics
GARCH-Prozess
Computational Theory and Mathematics
Multivariate GARCH
Parametrization
Conditional variance
2605 Computational Mathematics
Theorie
1703 Computational Theory and Mathematics
Schätzung
Popis: An asymmetric multivariate generalization of the recently proposed class of normal mixture GARCH models is developed. Issues of parametrization and estimation are discussed. Conditions for covariance stationarity and the existence of the fourth moment are derived, and expressions for the dynamic correlation structure of the process are provided. In an application to stock market returns, it is shown that the disaggregation of the conditional (co)variance process generated by the model provides substantial intuition. Moreover, the model exhibits a strong performance in calculating out-of-sample Value-at-Risk measures.
Databáze: OpenAIRE