It is shown empirically that mixed autoregressive moving average regression models with generalized autoregressive conditional heteroskedasticity (Reg-ARMA-GARCH models) can have multimodality in the likelihood that is caused by a dummy variable in the conditional mean. Maximum likelihood estimates at the local and global modes are investigated and turn out to be qualitatively different, leadin...

Some recent specifications for GARCH error processes explicitly assume a conditional variance that is generated by a mixture of normal components, albeit with some parameter restrictions. This paper analyses the general normal mixture GARCH(1,1) model which can capture time variation in both conditional skewness and kurtosis. A main focus of the paper is to provide evidence that, for modelling ...

The aftermath of the COVID-19 pandemic is not limited to human lives and health sectors. It has also changed social economic aspects world. This study investigated Islamic stock market’s reaction changes in volatility before during this pandemic. market model event methodology was employed analyze reactions nine different markets around globe. To examine persistence risk, generalized autoregres...

Abstract We examine stock return autocorrelation at various quantiles of the returns' distribution and use it to forecast volatility. Our empirical results show that strength autoregression varies across in terms both magnitude persistence. Specifically, order coefficients is lower left tail comparison with right tail. Additionally, we quantile autoregressive (QAR) framework proposed this study...

Various e m p i r i d studies have shown that the time-varying volatility of asset returns can be described by GARCH (generalized autoregressive conditional heteroskedasticity) models. The corresponding GARCH option pricing model of Duan (1995) is capable of depicting the "smile-effect" which often can be found in option prices. In some derivative markets, however, the slope of the smile is not...

In this paper we propose a consistent Integrated Conditional Moment (ICM) test of the functional form of a conditional heteroskedasticity model, for example a GARCH specification, which is asymptotically independent of the ICM test of the specification of the underlying conditional expectation model, under the null hypothesis that both models are correctly specified.

In this paper, we evaluate the downside risk of six major agricultural commodities – corn, wheat, soybeans, soybean meal, oil and oats. For research purposes, first use an optimal generalised autoregressive conditional heteroscedasticity (GARCH) model to create residuals, which later for measuring risks via parametric semiparametric approaches. Modified value-at-risk (mVaR) modified (mCVaR) pro...