Bayesian vector auto-regression model with Laplace errors applied to financial market data

0346969 - ÚTIA 2011 RIV CZ eng K - Conference Paper (Czech conference)
Šindelář, Jan
Bayesian vector auto-regression model with Laplace errors applied to financial market data.
Proceedings of Mathematical Methods in Economics 2010. České Budějovice: University of South Bohemia, Faculty of Economics, 2010 - (Houda, M.; Friebelová, J.), s. 602-608. ISBN 978-80-7394-218-2.
[Mathematical Methods in Economics 2010. České Budějovice (CZ), 08.09.2010-10.09.2010]
R&D Projects: GA MŠMT 1M0572; GA ČR GA102/08/0567
Institutional research plan: CEZ:AV0Z10750506
Keywords : auto-regression * robust * parameter estimation
Subject RIV: BB - Applied Statistics, Operational Research
http://library.utia.cas.cz/separaty/2010/AS/sindelar-bayesian vector auto-regression model with laplace errors applied to financial market data.pdf

The article presents alternative version of Bayesian vector auto-regression model with Laplace distributed innovations. Bayesian estimation in such model is more computationally demanding than estimation in a model with normally distributed innovations, but because of the heavier tails of Laplace distribution, it is more robust. In the article I try to present the way of proceeding with the estimation, obtaining a full posterior distribution of the parameters as a result. At the end an efficient algorithm is sketched, but this part of the research is still unfinished.
Permanent Link: http://hdl.handle.net/11104/0187854