Title: Bayesian inference for dynamic cointegration models with application to soybean crush spread
Authors: Maciej Marowka - Imperial College London (United Kingdom) [presenting]
Gareth Peters - University College London (United Kingdom)
Nikolas Kantas - Imperial College (United Kingdom)
Guillaume Bagnarosa - Rennes School of Business (France)
Abstract: In crush spread commodity trading strategies, it is a common practice to select portfolio positions based on physical refinery conditions and efficiency in extracting byproducts from crushing raw soybeans to get soyoil and soymeal. The selected portfolio positions 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. We take a statistical approach instead based on forming portfolio positions following from the cointegration vector relationships in the price series. 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 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.