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...

Over the past few years, interest has increased in models defined on positive and negative integers. Several application areas lead to data that are differences between Some important examples price changes measured discretely financial applications, pre- posttreatment measurements of discrete outcomes clinical trials, difference number goals sports events, differencing count-valued time series...

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 present a comprehensive modelling framework aimed at quantifying the response of agricultural commodity prices to changes in their potential determinants. The problem model uncertainty is assessed explicitly by concentrating on specification selection based quality short-term out-of-sample forecasts (1 12 months ahead) for price wheat, soybeans and corn. Univariate multivariate autoregressiv...

This paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a general nonlinear autoregression of order p (AR(p)) with the conditional variance specified as a general nonlinear first order generalized autoregressive conditional heteroskedasticity (GARCH(1,1)) model. We do not require the rescaled errors to be ind...

Methods: Using daily exchange rates for 7 years (January 1, 2008, to April 30, 2015), this study attempted to model dynamics following generalized autoregressive conditional heteroscedastic (GARCH), asymmetric power ARCH (APARCH), exponential generalized autoregressive conditional heteroscedstic (EGARCH), threshold generalized autoregressive conditional heteroscedstic (TGARCH), and integrated g...

In the study, we discussed the generalized autoregressive conditional heteroskedasticity model and enhanced it with wavelet transform to evaluate the daily returns for 1/4/2002-30/12/2011 period in Brent oil market. We proposed discrete wavelet transform generalized autoregressive conditional heteroskedasticity model to increase the forecasting performance of the generalized autoregressive cond...

This paper provides two main new results: the first shows theoretically that large biases and variances can arise when the quasi-maximum likelihood ~QML! estimation method is employed in a simple bivariate structure under the assumption of conditional heteroskedasticity; and the second demonstrates how these analytical theoretical results can be used to improve the finite-sample performance of ...

This paper studies asymptotic properties of the quasi-maximum likelihood estimator (QMLE) and of a suggested modified version for the parameters in the first order autoregressive (AR) model with autoregressive conditional heteroskedastic (ARCH) errors. The modified QMLE (MQMLE) is based on truncation of the likelihood function and is related to the recent so-called self-weighted QMLE in Ling (2...