NDLI: Rank estimation in Cointegrated Vector Auto-Regression models via automated Trans-dimensional Markov chain Monte Carlo

Content Provider	IEEE Xplore Digital Library
Author	Peters, G.W. Lasscock, B. Balakrishnan, K.
Copyright Year	2011
Description	Author affiliation: Department of Mathematics and Statistics, University of NSW, Sydney, Australia (Peters, G.W.) \|\| Boronia Capital Pty Ltd, Sydney, NSW, Australia (Lasscock, B.; Balakrishnan, K.)
Abstract	This paper develops a novel automated Transdimensional Markov chain Monte Carlo sampling methodology for Bayesian Cointegrated Vector Auto Regression (CVAR) models. In automating the rank and cointegration vector estimation in CVAR models we solve an important problem in algorithmic trading of cointegrated price series. The automation of both the within model sub-space sampling for the cointegration vectors directions and the between model rank estimation Markov chain proposal is achieved by developing a global matrix-variate proposal centered on the MLE and with covariance given by the observed Fisher Information matrix. To obtain this in the matrix-variate CVAR setting under an error correction formulation (ECM) involved a non-trivial derivation of the observed Fisher information matrix for each model subspaces unconstrained cointegration vector components, conditional on the components of the long run multiplier matrix which are constrained for identifiability. We study synthetic data and futures data on U.S. treasury notes, bonds and US equity indexes. In each analysis, we compare the estimated rank based on the estimated posterior model probabilities for the rank to simple Bayes Factor estimated posterior rank probabilities and the classical hypothesis test of the rank based on the trace statistic of the long-run multiplier matrix.
Starting Page	41
Ending Page	44
File Size	276961
Page Count	4
File Format	PDF
ISBN	9781457721045
e-ISBN	9781457721052
DOI	10.1109/CAMSAP.2011.6136040
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Biological system modeling Estimation Markov processes Vectors Covariance matrix Proposals
Content Type	Text
Resource Type	Article

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