Factor-augmented Bayesian cointegration models: a case-study on the soybean crush spread

Maciej Marowka, Gareth W. Peters, Nikolas Kantas, Guillaume Bagnarosa

Research output: Contribution to journalArticle

Abstract

We investigate how vector auto-regressive models can be used to study the soybean crush spread. By crush spread we mean a time series marking the difference between a weighted combination of the value of soymeal and soyoil to the value of the original soybeans. Commodity industry practitioners often use fixed prescribed values for these weights, which do not take into account any time-varying effects or any financial-market-based dynamic features that can be discerned from futures price data. We address this issue by proposing an appropriate time series model with cointegration. Our model consists of an extension of a particular vector auto-regressive model that is used widely in econometrics. Our extensions are inspired by the problem at hand and allow for a time-varying covariance structure and a time-varying intercept to account for seasonality. To perform Bayesian inference we design an efficient Markov chain Monte Carlo algorithm, which is based on the approach of Koop and his co-workerss. Our investigations on prices obtained from futures contracts data confirmed that the added features in our model are useful in reliable statistical determination of the crush spread. Although the interest here is on the soybean crush spread, our approach is applicable also to other tradable spreads such as oil and energy-based crack and spark spreads.

Original languageEnglish
Pages (from-to)483-500
Number of pages18
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume69
Issue number2
Early online date22 Jan 2020
DOIs
Publication statusPublished - Apr 2020

Keywords

  • Bayesian inference
  • Markov chain Monte Carlo methods
  • Soybean crush spread
  • State space models with cointegration

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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