Fossil energy markets have always been known as strategic and important markets. They have a significant impact on the macro economy and financial markets of the world. The nature of these markets are accompanied by sudden shocks and volatility in the prices. Therefore, they must be controlled and forecasted by using appropriate tools. This paper adopts the Generalized Auto Regressive Condition...

This study explains how stochastic beta models and their parametrizations based on Bayesian inference work using Korean stock market data. In this study, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, is newly adopted to the original stochastic beta model. The ability of estimating parameters between original stochastic beta model and stochastic beta model based on GAR...

In this paper, we introduce a Markov-switching generalized autoregressive conditional heteroscedasticity (GARCH) model in the short-time Fourier transform (STFT) domain. A GARCH model is utilized with Markov switching regimes, where the parameters are assumed to be frequency variant. The model parameters are evaluated in each frequency subband and a special state (regime) is de ned for the case...

In myoelectric prostheses design, it is normally assumed that the necessary control information can be extracted from the surface myoelectric signals. In the pattern classification paradigm for controlling myoelectric prosthesis, the autoregressive (AR) model coefficients are generally considered an efficient and robust feature set. However, no formal statistical methodologies or tests are repo...

In this paper, we derive a new application of fuzzy systems designed for a generalized autoregression conditional heteroscedasticity (GARCH) model. In general, stock market performance is time-varying and nonlinear, and exhibits properties of clustering. The latter means simply that certain large changes tend to follow other large changes, and in general small changes tend to follow other small...

We propose a new method for practical non-Gaussian and nonstationary underwater noise modeling. This model is very useful for passive sonar in shallow waters. In this application, measurement of additive noise in natural environment and exhibits shows that noise can sometimes be significantly non-Gaussian and a time-varying feature especially in the variance. Therefore, signal processing algori...

Spatio-temporal forecasting has various applications in climate, transportation, geo-statistics, sociology, economics and many other fields of study. The modelling temperature its is a challenging task due to spatial dependency time series data nonlinear nature. To address these challenges, this study we proposed hybrid Space–Time Autoregressive Moving Average-Generalized Conditional Heterosced...

In this article, we analyze the time series of minute price returns on Bitcoin market through statistical models generalized autoregressive conditional heteroscedasticity (GARCH) family. We combine an approach that uses historical values and their volatilities—GARCH family models, with a so-called Mixture Distribution Hypothesis, which states dynamics are governed by information flow about mark...