We reveal that in the estimation of univariate GARCH or multivariate generalized orthogonal GARCH (GO-GARCH) models, maximizing the likelihood is equivalent to making the standardized residuals as independent as possible. Based on that, we propose three factor GARCH models in the framework of GO-GARCH: independent-factor GARCH exploits factors that are statistically as independent as possible; ...

This paper is mainly talking about several volatility models and its ability to predict and capture the distinctive characteristics of conditional variance about the empirical financial data. In my paper, I choose basic GARCH model and two important models of the GARCH family which are E-GARCH model and GJR-GARCH model to estimate. At the same time, in order to acquire the forecasting performan...

With the increase of wind power as a renewable energy source in many countries, wind speed forecasting has become more and more important to the planning of wind speed plants, the scheduling of dispatchable generation and tariffs in the day-ahead electricity market, and the operation of power systems. However, the uncertainty of wind speed makes troubles in them. For this reason, a wind speed f...

Abstract We assess the differential impact of geopolitical risk on Islamic and conventional gold backed cryptocurrencies using a multivariate Generalized Autoregressive Conditional Heteroscedasticity (M-GARCH) modeling. unveil that gold-backed behave differently from their counterparts. Sharia compliant are positively correlated to yellow metal, while ones weakly negatively associated gold. fin...

In this article, an original data-driven approach is proposed to detect both linear and nonlinear damage in structures using output-only responses. The method deploys variational mode decomposition (VMD) generalized autoregressive conditional heteroscedasticity (GARCH) model for signal processing feature extraction. To end, VMD decomposes the response signals that are first decomposed intrinsic...

The present study suggests a model for predicting liquidity gap, based on source and cost of funds approach concerning the daily time series data (25 March 2009 to 19 March 2018), in order to control and manage the liquidity risk. Using the family of autoregressive conditional heteroscedasticity models, the behavior of bank liquidity gap is modeled and predicted. The results show that the APGAR...

A new variant of the ARCH class of models for forecasting conditional variance, to be called the Generalized AutoRegressive Conditional Heteroskedasticity Parkinson Range (GARCH-PARK-R) Model, is proposed. The GARCH-PARK-R model, utilizing the extreme values, is a good alternative to the Realized Volatility that requires a large amount of intra-daily data, which remain relatively costly and are...

Introduction/main objectives: The aim of this research is to study the impact macroeconomic variables on Indonesian Islamic stock market’s volatility. Background issues: To predict volatility, daily or high-frequency data has been applied model’s explanatory with same frequency. However, when it comes as volatility drivers, low-frequency, such weekly, monthly, quarterly. current uses a model wh...

This paper examines the robustness of control schemes to data conditional heteroscedasticity. Overall, the results show that the control schemes which do not account for heteroscedasticity fail in providing reliable information on the status of the process. Consequently, incorrect conclusions will be drawn by applying these procedures in the presence of data conditional heteroscedasticity. Cont...

We propose an extension of standard asymmetric volatility models in the generalized autoregressive conditional heteroskedasticity (GARCH) class that admits conditional nonGaussianities in a tractable fashion. Our “bad environment-good environment" (BEGE) model utilizes two gamma-distributed shocks and generates a conditional shock distribution with time-varying heteroskedasticity, skewness, and...