DocumentCode
2855208
Title
Bayesian volatility forecasting in the Tehran stock market
Author
Amiri, Esmail
Author_Institution
Dept. of Stat., Imam Khomeini Int. Univ., Ghazvin, Iran
fYear
2010
fDate
18-20 June 2010
Firstpage
80
Lastpage
84
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/ICFTE.2010.5499420
Filename
5499420
Link To Document