Title: Boosting MCMC estimation of stochastic volatility models
Language: English
Authors: Albrecht, Dorisz 
Qualification level: Diploma
Advisor: Sögner, Leopold 
Issue Date: 2017
Number of Pages: 39
Qualification level: Diploma
This paper analyzes the arising problems of using MCMC sampling methods under different model parameterizations in a stochastic volatility model. It turns out that the performance of Bayesian inference is dependent on the true parameter values. The standard centered parameterization has shortcomings when the variability of its volatility is relatively small, while the non-centered parameterization presents with complications when the persistence parameter is close to one. This paper uses the recently presented ancillarity-sufficiency interweaving strategy which overcomes the pitfalls of the parameterizations by using both of them in order to update the latent states and the parameters of interest jointly, this way maintaining the dependence between them.
Keywords: Stochastic Volatility; Bayesian inference; Ancillarity-Sufficiency; Boosting MCMC
URI: https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:1-100760
Library ID: AC13738081
Organisation: E017 - Continuing Education Center 
Publication Type: Thesis
Appears in Collections:Thesis

Files in this item:

Page view(s)

checked on Aug 21, 2021


checked on Aug 21, 2021

Google ScholarTM


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