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...

This paper presents a CAPM-based threshold quantile regression model with GARCH specification to examine relations between stock excess returns and “abnormal trading volume.” By employing the Bayesian MCMC method with asymmetric Laplace distribution to six daily Dow Jones Industrial stocks,...

With the regulatory requirements for risk management, Value at Risk (VaR) has become an essential tool in determining capital reserves to protect the risk induced by adverse market movements. The fact that VaR is not coherent has motivated the industry to explore alternative risk measures like...

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...

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 this research, a new class of time series models capturing dynamic seasonality is introduced. Unlike traditional seasonal models which focus mainly on the mean process, our approach can accommodate dynamic seasonality in the mean and variance processes. This feature allows us to perform...

To capture mean and variance asymmetries and time-varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy-tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for...