A first-order autoregressive process with inverse gaussian marginals is introduced. The innovation distributions are obtained under certain special cases. The unknown parameters are estimated using different methods and these estimators are shown to be consistent and asymptotically normal. The behavior of the estimators for small samples is studied through simulation experiments. On Sums of Tri...

in this article using autoregressive (ar), autoregressive conditional heteroskedasticity (arch), generalized autoregressive conditional heteroskedasticity (garch) models we assess the weekend effect and also compare the trading patterns of individual and legal investors during 1381-1385 in tehran stock exchange. our findings suggest that weekend effect exists in tehran stock exchanges which are...

We study in this article threshold quantile autoregressive processes. In particular we propose estimation and inference of the parameters in nonlinear quantile processes when the threshold parameter defining nonlinearities is known for each quantile, and also when the parameter vector is estimated consistently. We derive the asymptotic properties of the nonlinear threshold quantile autoregressi...