Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/25306 
Year of Publication: 
2008
Series/Report no.: 
SFB 649 Discussion Paper No. 2008,063
Publisher: 
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, Berlin
Abstract: 
In this paper, we review the most common specifications of discrete-time stochastic volatility (SV) models and illustrate the major principles of corresponding Markov Chain Monte Carlo (MCMC) based statistical inference. We provide a hands-on ap proach which is easily implemented in empirical applications and financial practice and can be straightforwardly extended in various directions. We illustrate empirical results based on different SV specifications using returns on stock indices and foreign exchange rates.
Subjects: 
Stochastic volatility
Markov chain Monte Carlo
Metropolis-Hastings algorithm Jump Processes
JEL: 
C15
C22
G12
Document Type: 
Working Paper

Files in This Item:
File
Size
736.66 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.