risk prediction plays an increasing role in financial risk management. this study aims to investigate existence of asymmetry and long memory volatility in tehran stock exchange index daily data over period of 1998-2006. 1467 daily index returns are used for volatility modeling via garch (long & short memory) processes for both normal and t-student innovations. the specification and forecasting ...

How persistent is volatility? In other words, how quickly do financial markets forget large volatility shocks? Figure 1.1, Shephard (attached) shows that daily squared returns on exchange rates and stock indices can have autocorrelations which are significant for many lags. In any stationary ARCH or GARCH model, memory decays exponentially fast. For example, if {εt } are ARCH (1), the {εt} have...

This article establishes a family of models for pricing interest rate sensitive claims when the underlying interest rate is driven by a two state variable GARCH process. Analytical solutions are established for the case when the innovations in the short rate are combinations of a normal and chi-squared random variables and the volatility of rates takes on a special GARCH form. GARCH models that...

This research aims to analyze and to compare the ability of different mathematical models, such as artificial neural networks (ANN) and ARCH and GARCH models, to forecast the daily exchange rates Euro/U.S. dollar (USD), identifying which, among all the models applied, produces more accurate forecasts. By empirically comparing the different mathematical models developed in this research, the tra...

The unstable and uncertain nature of natural rubber prices makes them highly volatile prone to outliers, which can have a significant impact on both modeling forecasting. To tackle this issue, the author recommends hybrid model that combines autoregressive (AR) Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models. utilizes Huber weighting function ensure forecast value remai...

This paper studies volatility forecasting in the financial stock market. In general, stock market volatility is time-varying and exhibits clustering properties. Thus, this paper presents the results of using a fuzzy system method to analyze clustering in generalized autoregressive conditional heteroskedasticity (GARCH) models. It also uses the adaptive method of recursive least-squares (RLS) to...

We conduct empirical analyses to model the volatility of stock market in Malaysia. The GARCH type models (symmetric and asymmetric GARCH) are used to model the volatility of stock market in Malaysia. Their performances are compared based on three statistical error measures tools, i.e. mean squared error, root means squared error and mean absolute percentage error for in sample and out sample an...

After the so-called Asia crisis in the summer of 1997 the nancial markets were shaken by increased volatility transmission around the world. Therefore, in this paper we will analyse the daily exchange rates in New York, Germany, and Japan for the period of 2 years (June 21, 1996 to June 22, 1998). We estimate a VAR-GARCH in mean model and estimate the multivariate volatility e ects between the ...

This paper proposes a new kind of asymmetric GARCH where the conditional variance obeys two diierent regimes with a smooth transition function. In one formulation, the conditional variance reacts diierently to negative and positive shocks while in a second formulation, small and big shocks have separate eeects. The introduction of a threshold allows for a mixed eeect. A Bayesian strategy, based...

In this paper we present two robust estimates for GARCH(p,q) models. The first is defined by the minimization of a conveniently modified likelihood and the second is similarly defined, but includes an additional mechanism for restricting the propagation of the effect of one outlier on the next estimated conditional variances. We study the asymptotic properties of our estimates proving consisten...