We demonstrate a reliable and computationally feasible method for determining whether a given threshold autoregression autoregressive conditional heteroscedastic (AR-ARCH) model is ergodic, and for determining which moments exist when it is ergodic. This method may be used to delineate the parameter space of the model. We show (for an order 2 model) that the parameter space is much less constra...

We perform a comprehensive examination of the recursive, comparative predictive performance of a number of linear and non-linear models for UK stock and bond returns. We estimate Markov switching, threshold autoregressive (TAR), and smooth transition autoregressive (STR) regime switching models, and a range of linear specifications in addition to univariate models in which conditional heteroske...

In this paper we propose a nonparametric test for conditional heteroskedasticity based on a new measure of nonparametric goodness-of-fit (R2). In analogy with the ANOVA tools for classical linear regression models, the nonparametric R2 is obtained for the local polynomial regression of the residuals from a parametric regression on some covariates. It is close to 0 under the null hypothesis of c...

The purpose of this paper is to investigate the relationship between the inflation and inflation uncertainty in Iran. Using mixed models of self-exciting threshold autoregressive (SETAR) and generalized autoregressive conditional heteroskedasticity (GARCH), the inflation behaviors are examined for the period 1990M05-2013M10. This approach allows testing the hypotheses of Friedman-Ball, Pourgera...

In this article, we study conditional heteroskedasticity in a market index on the Bombay Stock Exchange, from April 1979 to March 1995. We nd strong evidence of heteroskedasticity in daily, weekly and monthly returns. The conditional variance of all three data series seem best approximated by a garch(1,1) model. The garch parameter estimates at all data frequencies exhibit strong persistence in...

Image anomaly detection is the process of extracting a small number of clustered pixels which are different from the background. The type of image, its characteristics and the type of anomalies depend on the application at hand. In this paper, we introduce a new statistical model called noncausal autoregressive–autoregressive conditional heteroscedasticity (AR-ARCH) model for background in sona...