Title :
Bayesian volatility forecasting in the Tehran stock market
Author_Institution :
Dept. of Stat., Imam Khomeini Int. Univ., Ghazvin, Iran
Abstract :
In a Bayesian approach, we compare the volatility forecasting ability of ARCH, GARCH and stochastic volatility(SV) models, using daily Tehran stock market exchange data(TSE). To estimate the parameters of the models, Markov chain Monte Carlo(MCMC) methods is applied. The results show that the SV models perform better than the ARCH and GARCH family.
Keywords :
Bayes methods; Markov processes; Monte Carlo methods; autoregressive processes; stock markets; ARCH model; Bayesian volatility forecasting; GARCH model; Markov chain Monte Carlo method; Tehran stock market; stochastic volatility model; Bayesian methods; Data engineering; Economic forecasting; Investments; Parameter estimation; Predictive models; Security; Statistics; Stochastic processes; Stock markets; ARCH; Bayesian; GARCH; Markov chain Monte Carlo methods; Smooth transition autoregressive; Stochastic volatility;
Conference_Titel :
Financial Theory and Engineering (ICFTE), 2010 International Conference on
Conference_Location :
Dubai
Print_ISBN :
978-1-4244-7757-9
Electronic_ISBN :
978-1-4244-7759-3
DOI :
10.1109/ICFTE.2010.5499420
