Multivariate GARCH models are in principle able to accommodate the features of the dynamic conditional correlations processes, although with the drawback, when the number of financial returns series considered increases, that the parameterizations entail too many parameters.In general, the interaction between model parametrization of the second conditional moment and the conditional density of asset returns adopted in the estimation determines the fitting of such models to the observed dynamics of the data. This paper aims to evaluate the interactions between conditional second moment specifications and probability distributions adopted in the likelihood computation, in forecasting volatilities and covolatilities. We measure the relative performances of alternative conditional second moment and probability distributions specifications by means of Monte Carlo simulations, using both statistical and financial forecasting loss functions.
|Titolo:||Model and distribution uncertainty in Multivariate GARCH estimation : a Monte Carlo analysis|
SPAZZINI, FILIPPO (Ultimo)
|Data di pubblicazione:||2008|
|Parole Chiave:||Multivariate GARCH models ; Model uncertainty ; Quasi-maximum likelihood ; Monte Carlo methods|
|Citazione:||Model and distribution uncertainty in Multivariate GARCH estimation : a Monte Carlo analysis / E. Rossi, F. Spazzini. ((Intervento presentato al 2. convegno International Workshop on Computational and Financial Econometrics (CFE) tenutosi a Neuchatel (Switzerland) nel 2008.|
|Appare nelle tipologie:||14 - Intervento a convegno non pubblicato|