In this paper, we develop and evaluate speech enhancement algorithms, which are based on supergaussian generalized autoregressive conditional heteroscedasticity (GARCH) models in the short-time Fourier transform (STFT) domain. We consider three different statistical models, two fidelity criteria, and two approaches for the estimation of the variances of the STFT coefficients. The statistical mo...

Aims: To model the concentration variation of PM2.5 and PM10 in selected locations Delhi.
 Study Design: ARFIMA-GARCH model.
 Place Duration Study: The study was conducted by using daily (24 hour interval) data from three air quality monitoring stations Delhi namely, Narela, Okhla Phase II Pusa.
 Methodology: ARFIMA is applied as mean GARCH variance Results: series are stationary...

To capture the missed information in the standardized errors by parametric multivariate generalized autoregressive conditional heteroskedasticity (MV-GARCH) model, we propose a new semiparametric MV-GARCH (SM-GARCH) model. This SM-GARCH model is a twostep model: firstly estimating parametric MV-GARCH model, then using nonparametric skills to model the conditional covariance matrix of the standa...

In this paper, a comparison is provided for volatility estimation in Bayesian and frequentist settings. We compare the predictive performance of these two approaches under generalized autoregressive conditional heteroscedasticity (GARCH) model. Our results indicate that provides better potential than approach. The finding contrary to some work line research. To illustrate our finding, we used s...

Exponential models of Autoregressive Conditional Heteroscedasticity (ARCH) are of special interest, since they enable richer dynamics (e.g. contrarian or cyclical), provide greater robustness to jumps and outliers, and guarantee the positivity of volatility. The latter is not guaranteed in ordinary ARCH models, in particular when additional exogenous and/or predetermined variables (“X”) are inc...

Great Salt Lake (GSL) is the largest salt lake in the western hemisphere, the fourthlargest terminal lake in the world. The elevation of GSL has critical effect on the people who live nearby and their properties. It is crucial to build an exact model of GSL elevation time series in order to predict the GSL elevation precisely. Although some models, such as ARIMA or FARIMA (fractional auto-regre...

Most of the food commodity prices on world market increased drastically in late 2006 to mid 2008. The increase 2008 was triggered by global crisis. In 2020, is facing a recession caused Covid-19 pandemic. This study aims examine impact several commodities market. Volatility analysis conducted determine movement during crisis and recession. Secondary data obtained from World Bank's Pink Sheet Da...

The Composite Stock Price Index (IHSG) is a value that describes the combined performance of all shares listed on Indonesia Exchange. JCI serves as benchmark for investors in investing. method used to predict future conditions based past data forecasting . Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) amodel time series can be forecasting. Financial has high volatil...

Purpose The purpose of the research is to assess risk financial market in digital economy through quantitative analysis model big data era. It a challenge for government carry out management Design/methodology/approach In this study, generalized autoregressive conditional heteroskedasticity-vector autoregression (GARCH-VaR) constructed analyze economy. Additionally, correlation test and station...

Generalized autoregressive conditional heteroscedasticity (GARCH) models have long been considered as one of the most successful families of approaches for volatility modeling in financial return series. In this paper, we propose an alternative approach based on methodologies widely used in the field of statistical machine learning. Specifically, we propose a novel nonparametric Bayesian mixtur...