The HARX-GJR-GARCH skewed-t multipower realized volatility modelling for S&P 500

The heterogeneous autoregressive (HAR) models are used in modeling high frequency multipower realized volatility of the S&P 500 index. Extended from the standard realized volatility, the multipower realized volatility representations have the advantage of handling the possible abrupt jumps by smoothing the consecutive volatility. In order to accommodate clustering volatility and asymmetric of multipower realized volatility, the HAR model is extended by the threshold autoregressive conditional heteroscedastic (GJR-GARCH) component. In addition, the innovations of the multipower realized volatility are characterized by the skewed student-t distributions. The extended model provides the best performing in-sample and out-of-sample forecast evaluations

A hybrid group method of data handling with discrete wavelet transform for river flow forecasting

River flow forecasting is important because it can assist an organization to make better plans and decision makings. One of the major goals in river flow forecasting is to improve the planning, design, operation and management of hydrology and water resources system. This study proposes designing a hybridization model using Group Method of Data Handling (GMDH) and Discrete Wavelet Transform (DWT) for forecasting monthly river flow in three catchment areas in Malaysia. The monthly data of river flow in the form of monthly means are collected from the Department of Irrigation and Drainage, Malaysia. The hybrid model is a GMDH model that uses sub-time series components obtained using DWT on original data. The original data is represented by its features, which term the wavelet coefficients that are then iterated into GMDH model. The individual GMDH is used to forecast the river flow for each single catchment area. The experiments compare the performances of a hybrid model and a single model of Wavelet-Linear Regression (WR), ANN, and conventional GMDH. The results show that the hybrid model performs better than other models for river flow forecasting. It is shown that the proposed model can provide a promising alternative technique in river flow forecasting

A hybrid group method of data handling with discrete wavelet transform for GDP forecasting

This study is proposed the application of hybridization model using Group Method of Data Handling (GMDH) and Discrete Wavelet Transform (DWT) in time series forecasting. The objective of this paper is to examine the flexibility of the hybridization GMDH in time series forecasting by using Gross Domestic Product (GDP). A time series data set is used in this study to demonstrate the effectiveness of the forecasting model. This data are utilized to forecast through an application aimed to handle real life time series. This experiment compares the performances of a hybrid model and a single model of Wavelet-Linear Regression (WR), Artificial Neural Network (ANN), and conventional GMDH. It is shown that the proposed model can provide a promising alternative technique in GDP forecasting

3-dB Branch-line coupler using coupled line radial stub with no restriction on coupling power

Investigation on the design of coupled line radial stub towards 3-dB branch-line coupler (BLC) operating for fourth generation (4G) Long Term Evolution (LTE) at 3.5 GHz has been presented in this paper. The investigation involves different parameter value of the radius of radial stub and coupled line length at the series and shunt arm of 3-dB BLC designs specifically without restriction on the coupling power performance. The designed BLC was simulated using Rogers RO4003C substrate with thickness of 0.508 mm and dielectric constant of 3.38. The results for proposed radial stub BLC were being compared in terms of S-parameter and phase difference. The comparison shows that 3-dB BLC with radial shaped stub optimized to 79% reduction compared to conventional design without having to compromise the performance result especially with no restriction on the coupling power

The HARX-GJR-GARCH skewed-t multipower realized volatility modelling for S&P 500

The heterogeneous autoregressive (HAR) models are used in modeling high frequency multipower realized volatility of the S&P 500 index. Extended from the standard realized volatility, the multipower realized volatility representations have the advantage of handling the possible abrupt jumps by smoothing the consecutive volatility. In order to accommodate clustering volatility and asymmetric of multipower realized volatility, the HAR model is extended by the threshold autoregressive conditional heteroscedastic (GJR-GARCH) component. In addition, the innovations of the multipower realized volatility are characterized by the skewed student-t distributions. The extended model provides the best performing in-sample and out-of-sample forecast evaluations