Modeling and forecasting volatility in a bayesian approach

Abstract

In a Bayesian approach, we compare the forecasting performance of five classes of models: ARCH, GARCH, SV, SV-STAR, and MSSV using daily Tehran Stock Exchange (TSE) market data. To estimate the parameters of the models, Markov chain Monte Carlo (MCMC) methods is applied. The results show that the models in the fourth and the fifth class perform better than the models in the other classes.

Citation

Amiri, E. (2010), "Modeling and forecasting volatility in a bayesian approach", Greene, W. and Carter Hill, R. (Ed.) Maximum Simulated Likelihood Methods and Applications (Advances in Econometrics, Vol. 26), Emerald Group Publishing Limited, Leeds, pp. 323-356. https://doi.org/10.1108/S0731-9053(2010)0000026014