The GARCH model is modified to capture the effect on volatilities of the consecutive number of days of positive or negative shocks. The new model is applied to the Shanghai Shcomp and Nikkei225 indices and found particularly useful in analyzing the Shcomp index. Similarly, the EGARCH model is extended along the same line as the GARCH model and is applied to the same sets of data. Stationarity of the new GARCH(1,1) model is proved, and also derived is the asymptotic distribution of the quasimaximum likelihood estimator
This article develops the dynamic asymmetric GARCH (or DAGARCH) model that generalizes asymmetric GA...
DAMGARCH is a new model that extends the VARMA-GARCH model of Ling and McAleer (2003) by introducing...
This article develops the dynamic asymmetric GARCH (or DAGARCH) model that generalizes asymmetric GA...
This paper studies the performance of the GARCH model and two of its non-linear modifications to for...
textabstractIn this paper we study the performance of the GARCH model and two of its non-linear modi...
The GARCH (p, q) model is a very interesting stochastic process with widespread applications and a c...
This paper studies the performance of GARCH model and its modifications, using the rate of returns f...
The Kuwait stock exchange index is examined for evidence of a day-of-the-week effect. A nonlinear GA...
Forecasting volatility with precision in financial market is very important. This paper examines the...
The volatility clustering often seen in financial data has increased the interest of researchers in ...
The volatility clustering often seen in financial data has increased the interest of researchers in ...
In this paper, we extend the concept of the news impact curve of volatility developed by Engle and N...
Estimation of the parameters of Garch models for financial data is typically based on daily close-to...
After the so-called Asia crisis in the summer of 1997 the stock markets were shaken by an increased ...
This paper proposes a new kind of asymmetric GARCH where the conditional variance obeys two differen...
This article develops the dynamic asymmetric GARCH (or DAGARCH) model that generalizes asymmetric GA...
DAMGARCH is a new model that extends the VARMA-GARCH model of Ling and McAleer (2003) by introducing...
This article develops the dynamic asymmetric GARCH (or DAGARCH) model that generalizes asymmetric GA...
This paper studies the performance of the GARCH model and two of its non-linear modifications to for...
textabstractIn this paper we study the performance of the GARCH model and two of its non-linear modi...
The GARCH (p, q) model is a very interesting stochastic process with widespread applications and a c...
This paper studies the performance of GARCH model and its modifications, using the rate of returns f...
The Kuwait stock exchange index is examined for evidence of a day-of-the-week effect. A nonlinear GA...
Forecasting volatility with precision in financial market is very important. This paper examines the...
The volatility clustering often seen in financial data has increased the interest of researchers in ...
The volatility clustering often seen in financial data has increased the interest of researchers in ...
In this paper, we extend the concept of the news impact curve of volatility developed by Engle and N...
Estimation of the parameters of Garch models for financial data is typically based on daily close-to...
After the so-called Asia crisis in the summer of 1997 the stock markets were shaken by an increased ...
This paper proposes a new kind of asymmetric GARCH where the conditional variance obeys two differen...
This article develops the dynamic asymmetric GARCH (or DAGARCH) model that generalizes asymmetric GA...
DAMGARCH is a new model that extends the VARMA-GARCH model of Ling and McAleer (2003) by introducing...
This article develops the dynamic asymmetric GARCH (or DAGARCH) model that generalizes asymmetric GA...