Hall & Yao (2003) showed that, for ARCH/GARCH, i.e. autoregressive conditional heteroscedastic …

We investigate a new separable nonparametric model for time series, which includes many ARCH models and AR models …

We investigate a new separable nonparametric model for time series, which includes many ARCH models and AR models …

We investigate a class of semiparametric ARCH(∞) models that includes as a special case the partially nonparametric …

We investigate a class of semiparametric ARCH(∞) models that includes as a special case the partially nonparametric …

least squares as elasticities can be highly misleading in the presence of heteroskedasticity. This paper explains why this …

stationary time series and for standardized innovations of GARCH models. A simulation study demonstrates the efficacy of both …

satisfy these properties. Under no conditions aggregation of GARCH induces long memory conditional heteroskedasticity. …We study the impact of large cross-sections of contemporaneous aggregation of GARCH processes and of dynamic GARCH … factor models. The results crucially depend on the shape of the cross-sectional distribution of the GARCH coefficients and on …

The class of generalized autoregressive conditional heteroscedastic (GARCH) models has proved particularly valuable in … modelling time series with time varying volatility. These include financial data, which can be particularly heavy tailed. It is … for GARCH models only, the basic idea may be applied to address the estimation procedure selection problem in a general …

GARCH model. We extend the univariate Markov-Switching GARCH of Haas, Mittnik and Paolella (2004) into a bivariate Markov …-switching GARCH model with Conditional Constant Correlation (CCC) speci…cation within each regime, though the correlation may change … across regimes. Our model allows separate state variable governing each of the three processes: bond volatility, stock …