Calibration and filtering for multi factor commodity models with seasonality

incorporating panel data from futures contracts

Gareth William Peters, Mark Briers, Pavel Shevchenko, Arnaud Doucet

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

We construct a general multi-factor model for estimation and calibration of commodity spot prices and futures valuation. This extends the multi-factor long-short model in Schwartz and Smith (Manag Sci 893-911, 2000) and Yan (Review of Derivatives Research 5(3):251-271, 2002) in two important aspects: firstly we allow for both the long and short term dynamic factors to be mean reverting incorporating stochastic volatility factors and secondly we develop an additive structural seasonality model. In developing this non-linear continuous time stochastic model we maintain desirable model properties such as being arbitrage free and exponentially affine, thereby allowing us to derive closed form futures prices. In addition the models provide an improved capability to capture dynamics of the futures curve calibration in different commodities market conditions such as backwardation and contango. A Milstein scheme is used to provide an accurate discretized representation of the s.d.e. model. This results in a challenging non-linear non-Gaussian state-space model. To carry out inference, we develop an adaptive particle Markov chain Monte Carlo method. This methodology allows us to jointly calibrate and filter the latent processes for the long-short and volatility dynamics. This methodology is general and can be applied to the estimation and calibration of many of the other multi-factor stochastic commodity models proposed in the literature. We demonstrate the performance of our model and algorithm on both synthetic data and real data for futures contracts on crude oil.

Original languageEnglish
Pages (from-to)841-874
Number of pages34
JournalMethodology and Computing in Applied Probability
Volume15
Issue number4
DOIs
Publication statusPublished - Dec 2013
Externally publishedYes

    Fingerprint

Cite this