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.

Financial data are as a rule asymmetric, although most econometric models are symmetric. This applies also to continuous-time models for high-frequency and irregularly spaced data. We discuss some asymmetric versions of the continuous-time GARCH model, concentrating then on the GJR-COGARCH. We calculate higher order moments and extend the first jump approximation. These results are prerequisite...

The financial market is the core of national economic development, and stocks play an important role in market. Analyzing stock prices has become focus investors, analysts, people related fields. This paper evaluates volatility Apple Inc. (AAPL) returns using five generalized autoregressive conditional heteroskedasticity (GARCH) models: sGARCH with constant mean, GARCH sstd, GJR-GARCH, AR(1) GJ...

Background: In light of the latest global financial crisis and the ongoing sovereign debt crisis, accurate measuring of market losses has become a very current issue. One of the most popular risk measures is Value-at-Risk (VaR). Objectives: Our paper has two main purposes. The first is to test the relative performance of selected GARCH-type models in terms of their ability of delivering volatil...

In the class of univariate conditional volatility models, the three most popular are the generalized autoregressive conditional heteroskedasticity (GARCH) model of Engle (1982) and Bollerslev (1986), the GJR (or threshold GARCH) model of Glosten, Jagannathan and Runkle (1992), and the exponential GARCH (or EGARCH) model of Nelson (1990, 1991). For purposes of deriving the mathematical regularit...

In this paper, we consider a general family of asymmetric volatility models with stationary and ergodic coefficients. This family can nest several non-linear asymmetric GARCH models with stochastic parameters into its ambit. It also generalizes Markovswitching GARCH and GJR models. The geometric ergodicity of the proposed process is established. Sufficient conditions for stationarity and existe...

This study investigates the time series beaviour of daily stock returns of four firms listed in the Nigerian StockMarket from 2nd January, 2002 to 31st December, 2006, using three different models of heteroscedastic processes, namely: GARCH (1,1), EGARCH (1,1) and GJR-GARCHmodels respectively. The four firms whose share prices were used in this analysis are UBA, Unilever, Guiness and Mobil. All...

This paper investigates the forecasting ability of five different versions of GARCH models. The five GARCH models applied are bivariate GARCH, GARCH-ECM, BEKK GARCH, GARCH-X and GARCH-GJR. Forecast errors based on four emerging stock futures portfolio return (based on forecasted hedge ratio) forecasts are employed to evaluate out-ofsample forecasting ability of the five GARCH models. Daily data...

Gold investment is considered safer and has less risk than other types of investment. One the important knowledge in investing gold predicting price future through modeling past. The purpose this study to model past so that it can be used predict prices future. world data a time series heteroscedasticity properties, solve problem GARCH. This an asymmetric effect, GARCH used, namely Glosten-Jaga...

The paper develops two Dynamic Conditional Correlation (DCC) models, namely the Wishart DCC (WDCC) model and the Matrix-Exponential Conditional Correlation (MECC) model. The paper applies the WDCC approach to the exponential GARCH (EGARCH) and GJR models to propose asymmetric DCC models. We use the standardized multivariate t-distribution to accommodate heavy-tailed errors. The paper presents a...