The environmental literature lacks the use of volatility based models for environmental stochastic processes. To overcome this deficiency, we use EGARCH, IGARCH, TGARCH, GJR-GARCH, NGARCH, AVGARCH and APARCH models for functional relationships of the pathogen indicators time series for recreational activates at beaches. We use generalized error, Student’s t, exponential, normal and normal inver...

In this paper we estimate the skewness of unconditional distribution energy returns and test its statistical significance. We compare performance traditional robust tests for with those based on implied in a TGARCH model Gram-Charlier (TGARCH-GC) innovations. also analyze implications TGARCH-GC tail risk through evaluation Value-at-Risk (VaR) expected shortfall (ES) accuracy. Our results show t...

Smart grid is the main development goal of future power grid while the short-term load forecasting is the significant premise of making management, power supply and trading plan in market circumstance. The forecasting accuracy directly determined the safety and economy of electric system. Support Vector Machines (SVM), as the new machine learning method, has applied successfully to short-termed...

The Chinese stock market has been established for more than 20 years. Although it is not as mature as the highly developed western securities markets, it has a huge influence on the global economy. It is significant to study the risks of the Chinese stock market, especially the risk of stock indexes. Affected by the economic globalization today, more and more financial derivatives and financial...

Methods: Using daily exchange rates for 7 years (January 1, 2008, to April 30, 2015), this study attempted to model dynamics following generalized autoregressive conditional heteroscedastic (GARCH), asymmetric power ARCH (APARCH), exponential generalized autoregressive conditional heteroscedstic (EGARCH), threshold generalized autoregressive conditional heteroscedstic (TGARCH), and integrated g...

This paper provides a theoretical explanation for the heteroscedasticity of asset returns. In line with existing empirical results, our model yields an asymmetric relationship between stock return and volatility. Based on the simple assumptions that investors behave according to Prospect Theory and are subject to mental accounting in a dynamic setting, we analytically derive the unit-root versi...

Data finansial yang mengikuti deret waktu memiliki keragaman atau volatilitas setiap waktunya tidak konstan. Keadaan ini disebut sebagai heteroskedastisitas. Metode dapat menyelesaikan masalah tersebut adalah Autoregressive Conditional Heteroscedasticity (ARCH)/Generalized (GARCH). Namun, ARCH/GARCH mengatasi beberapa kasus seperti perbedaan dalam nilai leverage effect. Sehingga dilakukan pemod...

Although quasi maximum likelihood estimator based on Gaussian density (G-QMLE) is widely used to estimate GARCH-type models, it does not perform successfully when error distribution is either skewed or leptokurtic. This paper proposes normal mixture quasi-maximum likelihood estimator (NMQMLE) for non-stationary TGARCH(1, 1) models. We show that, under mild regular conditions, there is no consis...

One of the main tasks to analyze and design a mining system is predicting the behavior exhibited by prices in the future. In this paper, the applications of different prediction methods are evaluated in econometrics and financial management fields, such as ARIMA, TGARCH, and stochastic differential equations, for the time-series of monthly copper prices. Moreover, the performance of these metho...