This article applied GARCH model instead AR or ARMA model to compare with the standard BP and SVM in forecasting of the four international including two Asian stock markets indices.These models were evaluated on five performance metrics or criteria. Our experimental results showed the superiority of SVM and GARCH models, compared to the standard BP in forecasting of the four international stock...

We compare three methods of constructing confidence intervals for sample autocorrelations of squared returns modeled by models from the GARCH family. We compare the residual bootstrap, block bootstrap and subsampling methods. The residual bootstrap based on the standard GARCH(1,1) model is seen to perform best.

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...

In this paper we introduce an exponential continuous time GARCH(p, q) process. It is defined in such a way that it is a continuous time extension of the discrete time EGARCH(p, q) process. We investigate stationarity, mixing and moment properties of the new model. An instantaneous leverage effect can be shown for the exponential continuous time GARCH(p, p) model.

This paper collects some of the well known probabilistic properties of GARCH(p, q) processes. In particular, we address the question of strictly and of weakly stationary solutions. We further investigate moment conditions as well as the strong mixing property of GARCH processes. Some distributional properties such as the tail behaviour and continuity properties of the stationary distribution ar...

The asymptotic theory for the sample autocorrelations and extremes of a GARCH(1; 1) process is provided. Special attention is given to the case when the sum of the ARCH and GARCH parameters is close to one, i.e. when one is close to an innnite variance marginal distribution. This situation has been observed for various nancial log{return series and led to the introduction of the IGARCH model. I...

1. Methods and application Several studies in empirical finance literature have highlighted the importance of allowing for skewness, tail-fatness, non normality of returns for asset allocation and pricing models. Moreover, the dependence between returns, that can impact portfolio decisions, often exhibits nonlinear structures and asymmetric extremal behavior that the usual correlation coefficie...

We study the asymptotic tail behavior of the conditional probability distributions of rt+k and rt+1 + · · · + rt+k when (rt )t∈N is a GARCH(1, 1) process. As an application, we examine the relation between the extreme lower quantiles of these random variables.

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.

This paper establishes a selection of stylized facts for high-frequency cointegrated processes, based on one-minute-binned transaction data. A methodology is introduced to simulate cointegrated stock pairs, following none, some or all of these stylized facts. AR(1)-GARCH(1,1) and MR(3)-STAR(1)-GARCH(1,1) processes contaminated with reversible and non-reversible jumps are used to model the coint...