Rank estimation in cointegrated vector auto-regression models via automated Trans-dimensional Markov chain Monte Carlo

Gareth W. Peters, Ben Lasscock, Kannan Balakrishnan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publication2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
PublisherIEEE
Pages41-44
Number of pages4
ISBN (Electronic)9781457721052
ISBN (Print)9781457721045
DOIs
Publication statusPublished - 23 Jan 2012
Event4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing 2011 - San Juan, Puerto Rico
Duration: 13 Dec 201116 Dec 2011

Conference

Conference4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing 2011
Abbreviated titleCAMSAP 2011
CountryPuerto Rico
CitySan Juan
Period13/12/1116/12/11

ASJC Scopus subject areas

  • Computer Networks and Communications

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    Peters, G. W., Lasscock, B., & Balakrishnan, K. (2012). Rank estimation in cointegrated vector auto-regression models via automated Trans-dimensional Markov chain Monte Carlo. In 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) (pp. 41-44). IEEE. https://doi.org/10.1109/CAMSAP.2011.6136040