We consider quantile autoregression (QAR) models in which the autoregressive coefficients can be expressed as monotone functions of a single, scalar random variable. The models can capture systematic influences of conditioning variables on the location, scale and shape of the conditional distribution of the response, and therefore constitute a significant extension of classical constant coeffic...

Many financial time series have varying structures at different quantile levels, and also exhibit the phenomenon of conditional heteroskedasticity same time. However, there is presently no model that accommodates both these features. This paper fills gap by proposing a novel heteroskedastic called “quantile double autoregression”. The strict stationarity new derived, self-weighted estimation su...

A seasonal additive nonlinear vector autoregression (SANVAR) model is proposed for multivariate seasonal time series to explore the possible interaction among the various univariate series. Significant lagged variables are selected and additive autoregression functions estimated based on the selected variables using spline smoothing method. Conservative confidence bands are constructed for the ...

Recent developments in nonlinear time series modelling are reviewed. Three main types of nonlinear model are discussed: Markov Switching, Threshold Autoregression and Smooth Transition Autoregression. Classical and Bayesian estimation techniques are described for each model. Parametric tests for nonlinearity are reviewed with examples from the three types of model. Finally forecasting and impul...

Many financial time series processes appear subject to periodic structural changes in their dynamics. Regression relationships are often not robust to outliers nor stable over time, whilst the existence of changes in variance over time is well documented. This paper considers a vector autoregression subject to pseudocyclical structural changes. The parameters of a vector autoregression are mode...