We compare small-sample properties of Bayes estimation and maximum likelihood estimation (MLE) of ARMA-GARCH models. Our Monte Carlo experiments indicate that in small sample, the Bayes estimator beats the MLE. We also develop a Bayes method of testing strict stationarity and ergodicity of the...

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...

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...

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...

Three Bayesian methods (Markov chain Monte Carlo, Laplace approximation and quadrature formula) are developed to estimate the parameters of the ARMA-GARCH model. The ARMA-GARCH model is applied to weekly foreign exchange rate data of five major currencies, and their stochastic volatilities are...

We compare small-sample properties of Bayes estimation and maximum likelihood estimation (MLE) of ARMA-GARCH models. Our Monte Carlo experiments indicate that in small sample, the Bayes estimator beats the MLE. We also develop a Bayes method of testing strict stationarity and ergodicity of the...