CONDITIONALLY HETEROSKEDASTIC FACTOR MODELS WITH SKEWNESS AND LEVERAGE EFFECTS

EGARCH models with fat tails, skewness and leverage

An EGARCH model in which the conditional distribution is heavytailed and skewed is proposed. The properties of the model, including unconditional moments, autocorrelations and the asymptotic distribution of the maximum likelihood estimator, are obtained. Evidence for skewness in conditional t-distribution is found for a range of returns series and the model is shown to give a better fit than th...

Skewness and Leverage

A Conditionally Heteroskedastic Global Inflation Model

This article proposes a multivariate model of inflation with conditionally heteroskedastic common and country-specific components. The model is estimated in one-step via Quasi-Maximum Likelihood for the G7 countries for the period Q1-1960 to Q4-2009. It is found that various model specifications considered fit well the first and second order dynamics of inflation in the G7. The estimated volati...

USING SUBSPACE METHODS FOR ESTIMATING ARMA MODELS FOR MULTIVARIATE TIME SERIES WITH CONDITIONALLY HETEROSKEDASTIC INNOVATIONS By

This paper deals with the estimation of linear dynamic models of the ARMA type for the conditional mean for time series with conditionally heteroskedastic innovation process widely used in modelling financial time series. Estimation is performed using subspace methods which are known to have computational advantages as compared to prediction error methods based on criterion minimization. These ...

Forecasting large datasets with conditionally heteroskedastic dynamic common factors

We propose a new method for multivariate forecasting which combines Dynamic Factor and multivariate GARCH models. We call the model Dynamic Factor GARCH, as the information contained in large macroeconomic or financial datasets is captured by a few dynamic common factors, which we assume being conditionally heteroskedastic. After describing the estimation of the model, we present simulation res...