intermediate regimes is considered and modeled with the introduction of a smooth-transition mechanism in a GARCH specification. One … power. A smooth-transition GARCH specification is tested and estimated with stock returns and exchange-rate data. While a …

This paper describes a GAUSS program of a Markov-chain sampling algorithm for GARCH models proposed by Nakatsuma (1998 …). This algorithm allows us to generate Monte Carlo samples of parameters in a GARCH model from their joint posterior … distribution. The samples obtained by this algorithm are used for Bayesian analysis of the GARCH model. As numerical examples …

different GARCH models. …

principal index of the London Stock Exchange supports our model when compared to other frequently used GARCH-type models, which …

GARCH (1,1) model. As a result, the GARCH estimate tends to have too small a standard error relative to the true one when … the ARCH parameter is small, even when sample size becomes very large. In combination with an upward bias in the GARCH …

In this paper we test for (Generalized) AutoRegressive Conditional Heteroskedasticity [(G)ARCH] in daily and weekly … data on 22 exchange rates and 13 stock market indices using the standard Lagrange Multiplier [LM] test for GARCH and a new … result is that we find spurious GARCH in over 50% of the cases. Using Monte Carlo simulations, in which we evaluate our …

- Maximum Likelihood Estimator for a general nonlinear conditional mean model with first-order GARCH errors. …

ARMA(1,1)-GARCH(1,1) and ARMA(3,2)-GJR(1,1) models are suitable for modelling ENSO volatility. Moreover, 1998 is a turning …

Carlo methods. The effects of several model characteristics (unit roots, GARCH, stochastic volatility, heavy tailed …