This paper studies the statistical properties of a two-step conditional quantile estimator in nonlinear time series models with unspecified error distribution. The asymptotic distribution of the quasi-maximum likelihood estimators and the filtered empirical percentiles is derived. Three...

A new class of stochastic covariance models based on Wishart distribution is proposed. Three categories of dynamic correlation models are introduced depending on how the time-varying covariance matrix is formulated and whether or not it is a latent variable. A stochastic covariance filter is...

We develop an efficient way to select the best subset autoregressive model with exogenous variables and generalized autoregressive conditional heteroscedasticity errors.One main feature of our method is to select important autoregressive and exogenous variables, and at the same time to estimate...

In high dimensional data, relevant interactions can be difficult to identify due to the extremely large number of possible interactions among variables. Conventional methods use a screening stage to vastly reduce the dimension of the variable space before examining the interaction effect....

This paper proposes a new clustered correlation multivariate GARCH model (CC-MGARCH) that allows conditional correlations to form clusters. This model can generalize the time-varying correlation structure in Tse and Tsui (2002) by determining a natural grouping of the correlations among the...

This paper proposes a threshold multivariate GARCH model (Threshold MGARCH) which integrates threshold nonlinearity, mean and volatility asymmetries and time-varying correlation in financial markets. The main feature of this model is that the threshold variables are formulated as average or...

It is well known that volatility asymmetry exists in financial markets. This paper reviews and investigates recently developed techniques for Bayesian estimation and model selection applied to a large group of modern asymmetric heteroskedastic models. These include the GJR-GARCH, threshold...