This study extends the dynamic conditional correlation model to allow day-specific correlations of shocks across international stock markets. The properties of the resulting periodic dynamic conditional correlation (PDCC) model are examined, with the model then applied to study the intra-week interactions between six developed European stock markets and the US over the period 1993 2005. We find...

The main purpose of this paper is to evaluate the effect of crude oil price on global fertilizer prices in both the mean and volatility. The endogenous structural breakpoint unit root test, ARDL model, and alternative volatility models, including GARCH, EGARCH, and GJR models, are used to investigate the relationship between crude oil price and six global fertilizer prices. The empirical result...

This paper examines the price volatility in the silver spot (cash) market. A host of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models are used to analyze and gain a better understanding of the volatility of silver prices. We find the TGARCH (1,1) model indicates that both positive and negative shocks do not have a significant effect on volatility in the silver spot marke...

In this chapter, the authors use an EGARCH-ECM to estimate the pass-through effects of Foreign Exchange (FX) rate changes and changes in producers’ prices for 20 U.K. export sectors. The long-run adjustments of export prices to FX rate changes and changes in producers’ prices are within the range of –1.02% (for the Textiles sector) and –17.22% (for the Meat sector). The contemporaneous PricingT...

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 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...

in this paper various arch models and relevant news impact curves including a partially nonparametric (pnp) one are compared and estimated with daily iran stock return data. diagnostic tests imply the asymmetry of the volatility response to news. the egarch model, which passes all the tests and appears relatively matching with the asymmetry in the data, seems to be the most adequate characteriz...

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...

Financial time series forecast has been classified as standard problem in forecasting due to its high non-linearity and high volatility in data. Statistical methods such as GARCH, GJR, EGARCH and Artificial Neural Networks (ANNs) based on standard learning algorithms such as backpropagation have been widely used for forecasting time series volatility of various fields. In this paper, we propose...