This paper incorporates EGARCH modeling in a financial event study relating firm value to negative environmental news. News media provide informal information channels unlike formal government disclosure programs. This paper improves on previous studies by using a larger sample than most studies, treating heteroskedasticity in the disturbance term with a hybrid method that allows EGARCH, and co...

This paper proposes a new method to forecast S&P 500 return distribution by combining quantile regression models using macro-finance variables with volatility-based models including various standard EGARCH and stochastic volatility specifications. 30 density forecasting models are compared and combined in an out-of-sample forecasting exercise. Using macro-finance variables is found to help subs...

Three strains of facultatively anaerobic thermotolerant and moderately halophilic bacteria were isolated from two Jordanian hot spring locations. The novel Bacillus strains were short rods, Gram-positive, motile and spore formers. The strains were capable of anaerobic (but not aerobic) growth at 63C, moderately halophilic, able to grow at NaCl concentration range of 12 15 % and at temperatures ...

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...

In this paper, we estimate GARCH, EGARCH, and GJR-GARCH models assuming normal and heavy-tailed distribution (i.e., GED). Results suggest that when the heavy-tailed distribution is considered, the persistence has found to be reduced in all the cases. Findings also reveal that positive shocks are more common than the negative shocks in this market.

We provide a simple, yet highly effective framework for forecasting return volatility by combining exponential generalized autoregressive conditional heteroscedasticity models with data on the range. Using Standard and Poor’s 500 index data for 1983–2004, we demonstrate the importance of a long-memory specification, based on either a two-factor structure or fractional integration, that allows f...

Face recognition is important in human identification. The biological recognition technique acts as a good method and broad applications in security areas. This work presents a method to improve the face recognition accuracy using a combination of Principal Component Analysis (PCA), and Wavelet Transform. Wavelet Transform is used to decompose the input image with different levels and rearrange...