Bayesian Inference for Dynamic Cointegration Models with Application to Soybean Crush Spread

30 Pages Posted: 1 May 2017

See all articles by Maciej Marówka

Maciej Marówka

Imperial College London - Department of Mathematics

Gareth Peters

Department of Actuarial Mathematics and Statistics, Heriot-Watt University; University College London - Department of Statistical Science; University of Oxford - Oxford-Man Institute of Quantitative Finance; London School of Economics & Political Science (LSE) - Systemic Risk Centre; University of New South Wales (UNSW) - Faculty of Science

Nikolas Kantas

Imperial College London

Guillaume Bagnarosa

ESC Rennes School of Business

Date Written: May 1, 2016

Abstract

Abstract In crush spread commodity trading strategies it is a common practice to select portfolio positions not based on statistical properties, but instead based on physical refinery conditions and efficiency in extracting byproducts from crushing raw soybeans to get soymeal and soyoil. The selected portfolio positions based on knowledge of refinery efficiency are then used to provide a basis for constructing the so called spread series, which is investigated separately using a model with a linear Gaussian structure. In this paper we take a statistical approach instead based on forming portfolio positions following from the cointegration vector relationships in the price series, which we argue endogenously take into consideration the respective demand and supply equilibrium dynamic associated to each component of the soybean complex spread. We propose an extension of the standard Cointegrated Vector Autoregressive Model that allows for a hidden linear trend under an error correction representation. The aim of this paper is to perform Bayesian estimation of the optimal cointegration vectors jointly with latent trends and to this end we develop an efficient Markov Chain Monte Carlo (MCMC) algorithm. The performance of this method is illustrated using numerical examples with simulated observations. Finally, we use the proposed model and MCMC sampler to perform analysis for soybean crush data. We will find the evidence in favour of the model structure proposed and present empirical justification that cointegration portfolio selection based on physical features of soybean market is sensitive to different roll adjustment methods used in the industry.

Keywords: Bayesian Cointegration, Crush Trades, Rao-Blackwellized MCMC

Suggested Citation

Marówka, Maciej and Peters, Gareth and Kantas, Nikolas and Bagnarosa, Guillaume, Bayesian Inference for Dynamic Cointegration Models with Application to Soybean Crush Spread (May 1, 2016). Available at SSRN: https://ssrn.com/abstract=2960638 or http://dx.doi.org/10.2139/ssrn.2960638

Maciej Marówka (Contact Author)

Imperial College London - Department of Mathematics ( email )

South Kensington Campus
Imperial College
LONDON, SW7 2AZ
United Kingdom

Gareth Peters

Department of Actuarial Mathematics and Statistics, Heriot-Watt University ( email )

Edinburgh Campus
Edinburgh, EH14 4AS
United Kingdom

HOME PAGE: http://garethpeters78.wixsite.com/garethwpeters

University College London - Department of Statistical Science ( email )

1-19 Torrington Place
London, WC1 7HB
United Kingdom

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

University of Oxford Eagle House
Walton Well Road
Oxford, OX2 6ED
United Kingdom

London School of Economics & Political Science (LSE) - Systemic Risk Centre ( email )

Houghton St
London
United Kingdom

University of New South Wales (UNSW) - Faculty of Science ( email )

Australia

Nikolas Kantas

Imperial College London ( email )

South Kensington Campus
Exhibition Road
London, Greater London SW7 2AZ
United Kingdom

Guillaume Bagnarosa

ESC Rennes School of Business ( email )

2, RUE ROBERT D'ARBRISSEL
Rennes, 35065
France

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