Modeling and forecasting volatility in a bayesian approach

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.

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Year of publication:	2010
Authors:	Amiri, Esmail
Subject:	Volatilität \| Volatility \| Prognoseverfahren \| Forecasting model \| Bayes-Statistik \| Bayesian inference \| Theorie \| Theory \| ARCH-Modell \| ARCH model \| Schätzung \| Estimation

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Type of publication:	Article
Language:	English
Other identifiers:	10.1108/S0731-9053(2010)0000026014 [DOI]
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015381422