Which Risk Factors Drive Oil Futures Price Curves?

45 Pages Posted: 20 Sep 2016 Last revised: 16 Jan 2019

See all articles by Matthew Ames

Matthew Ames

The Institute of Statistical Mathematics

Guillaume Bagnarosa

ESC Rennes School of Business

Tomoko Matsui

Independent

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

Pavel V. Shevchenko

Macquarie University; Macquarie University, Macquarie Business School

Date Written: September 18, 2016

Abstract

Supplementary material available at: https://ssrn.com/abstract=3312707

We develop a consistent estimation framework that builds on the familiar two-factor model of Schwartz and Smith (2000), to allow for an investigation of the influence of observable factors, such as inventories, production or hedging pressure, on the term structure of crude oil futures prices. We develop a novel Hybrid Multi-Factor (HMF) state-space regression model, in such a way that we can obtain closed form futures prices under standard risk neutral pricing formulations, and importantly we can incorporate state-space model estimation techniques to consistently and efficiently estimate the models developed. In particular, under the developed class of HMF models we can estimate both the structural features related to the convenience yield and spot price dynamics (or equivalently the long and short term stochastic dynamics) and also the structural parameters that relate to the influence on the spot price of the observed exogenous factors. We can utilise such models to gain significant insight into the futures and spot price dynamics in terms of interpretable observable factors that influence speculators and hedgers heterogeneously, which is not attainable with existing modelling approaches.

Keywords: Crude oil, Short-term factor, Long-term factor, Hybrid Multi-Factor model, Macroeconomical factors, Term structure

JEL Classification: C01, C1, C5, G13, Q02

Suggested Citation

Ames, Matthew and Bagnarosa, Guillaume and Matsui, Tomoko and Peters, Gareth and Shevchenko, Pavel V., Which Risk Factors Drive Oil Futures Price Curves? (September 18, 2016). Available at SSRN: https://ssrn.com/abstract=2840730 or http://dx.doi.org/10.2139/ssrn.2840730

Matthew Ames (Contact Author)

The Institute of Statistical Mathematics ( email )

Tokyo
Japan

Guillaume Bagnarosa

ESC Rennes School of Business ( email )

2, RUE ROBERT D'ARBRISSEL
Rennes, 35065
France

Tomoko Matsui

Independent ( email )

No Address Available

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

Pavel V. Shevchenko

Macquarie University ( email )

North Ryde
Sydney, New South Wales 2109
Australia

HOME PAGE: http://www.businessandeconomics.mq.edu.au/contact_the_faculty/all_fbe_staff/pavel_shevchenko

Macquarie University, Macquarie Business School ( email )

New South Wales 2109
Australia

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