Abstract Purpose The Group Method of Data Handling (GMDH) neural network has demonstrated good performance in data mining, prediction, and optimization. Scholars have used it to forecast stock real estate investment trust (REIT) returns some countries region, but not the United States (US) REIT market. primary goal this study is predict US market using GMDH then compare its accuracy with that d...

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...

Today's precipitation is growing increasingly variable, making forecasting difficult. The Indian Meteorological Department (IMD) currently employs Composite and Stochastic approaches to forecast spring storm in Asia. As a corollary, planners are unlikely predict the macroeconomic effects of disasters (due excessive precipitation) or famine (less precipitation). amount that drops dependent on va...

GARCH-type models have been highly developed since Engle [1982] presented ARCH process 30 years ago. Different kinds of GARCH-type models are applicable to different kinds of research purposes. As documented by many literatures that short-memory processes with level shifts will exhibit properties that make standard tools conclude long-memory is present. Therefore, in this paper, we want to fore...

The aim of this paper is to analyse the out-of-sample performance of SETAR models relative to a linear AR and a GARCH model using daily data for the Euro effective exchange rate. The evaluation is conducted on point, interval and density forecasts, unconditionally, over the whole forecast period, and conditional on specific regimes. The results show that overall the GARCH model is better able t...

This paper focus on the problems faced in the empirical investigation of the relation between the level and volatility of inflation. Monthly inflation series seem to be affected by both the presence of outliers and conditional heteroscedasticity. First, the paper illustrates the implications that the presence of outliers and conditional heteroscedasticity have on the usual residual diagnostics....

We consider the parameter restrictions that need to be imposed in order to ensure that the conditional variance process of a GARCH(p, q) model remains non-negative. Previously, Nelson and Cao (1992) provided a set of necessary and sufficient conditions for the aforementioned non-negativity property for GARCH(p, q) models with p ≤ 2, and derived a sufficient condition for the general case of GAR...

Tourism forecasting has garnered considerable interest. However, integrating tourism with volatility is significantly less typical. This study investigates the performance of both single models and their combinations for demand. The seasonal autoregressive integrated moving average (SARIMA) model used to construct mean equation, three models, namely generalized conditional heteroscedasticity (G...

We revisit the risk-return relation using the component GARCH model and international daily MSCI stock market data. In contrast with the previous evidence obtained from weekly and monthly data, daily data show that the relation is positive in almost all markets and often statistically significant. Likelihood ratio tests reject the standard GARCH model in favor of the component GARCH model, whic...