We present a volatility forecasting comparative study within the autoregressive conditional heteroskedasticity (ARCH) class of models. Our goal is to identify successful predictive models over multiple horizons and to investigate how predictive ability is influenced by choices for estimation window length, innovation distribution, and frequency of parameter reestimation. Test assets include a r...

This paper investigates the forecasting ability of five different versions of GARCH models. The five GARCH models applied are bivariate GARCH, GARCH-ECM, BEKK GARCH, GARCH-X and GARCH-GJR. Forecast errors based on four emerging stock futures portfolio return (based on forecasted hedge ratio) forecasts are employed to evaluate out-ofsample forecasting ability of the five GARCH models. Daily data...

Prediction of peak loads in Iran up to year 2011 is discussed using the Radial Basis Function Networks (RBFNs). In this study, total system load forecast reflecting the current and future trends is carried out for global grid of Iran. Predictions were done for target years 2007 to 2011 respectively. Unlike short-term load forecasting, long-term load forecasting is mainly affected by economy...

Solar radiation is one of the principal energy sources for physical, biological and chemical processes, occupying the most important role in many engineering applications. The process of converting sunlight to electricity without combustion allows to create power without pollution. The major problem of such energy source is its intermittence and its stochastic character which make difficult the...

This paper develops a new ensemble-based approach to point and interval forecasting, focus on total electricity supply. The proposed combines Bootstrap Aggregation (Bagging), time series methods novel pruning routine that performs feature selection before the aggregation of forecasts. Monthly supplied between January 2000 September 2020 in 16 countries are considered. Forecasting performance di...

this paper investigates the forecasting performance of different time-varying bvar models for iranian inflation. forecast accuracy of a bvar model with litterman’s prior compared with a time-varying bvar model (a version introduced by doan et al., 1984); and a modified time-varying bvar model, where the autoregressive coefficients are held constant and only the deterministic components are allo...

Fuzzy Load forecasting plays a paramount role in the operation and management of power systems. Accurate estimation of future power demands for various lead times facilitates the task of generating power reliably and economically. The forecasting of future loads for a relatively large lead time (months to few years) is studied here (long term load forecasting). Among the various techniques used...

In this paper, we study the efficiency of the Chinese wheat and soybeans futures markets and assess the conditions in agricultural commodity futures and cash markets in China. Formal statistical tests are conducted through Johansen's cointegration approach to identify the long-term equilibrium relationship between futures and cash markets. Three different cash prices from Zhengzhou Grain Wholes...

• short-term inclement weather forecasting (for hurricanes, etc.), • contaminant plume forecasting in both urban environments (for coordinating emergency response) and battlefield environments (for coordinating troop movements), • long-term ocean current forecasting (for El Niño, climate change, etc.), and • MHD/plasma forecasting (for sunspot cycles, over terms of years, in order to plan space...

We examine recursive out-of-sample forecasting of monthly postwar U.S. core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading indicators associated with macroeconomic activity and monetary conditions for forecasting horizons up to two years....