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Forecasting Petroleum Futures Dynamics Using Principal Components Analysis

This research paper investigates the forecasting potential of principal components analysis (PCA) on the term structure of petroleum futures prices. The study uses a rich dataset of WTI NYMEX Crude oil, IPE Brent Crude Oil, Heating Oil, and Gasoline futures prices from 1993 to 2003. The results suggest that while PCA reveals significant factors governing price dynamics, forecasting power remains limited. The paper also explores spillover effects among these commodity markets and discusses implications for future research on alternative PCA models and GARCH-type effects.

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Forecasting Petroleum Futures Dynamics Using Principal Components Analysis

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  1. Can the Dynamics of Petroleum Futures be Forecasted? Evidence from Major Markets Thalia Chantziara1 & George Skiadopoulos2 ¹ Independent ² Dept. of Banking and Financial Management, University of Piraeus & Financial Options Research Centre, University of Warwick Commodities 2007 17January, 2007 – Birkbeck College

  2. Background - Motivation • Futures on various petroleum products have become very popular. • The whole term structure of futures prices is of interest. • The term structure evolves stochastically. • The trading and hedging of petroleum futures is challenging. • Can we forecast the daily evolution of the petroleum term structure per se?

  3. This paper - Contributions • What will be the forecasting variables? • Principal Components Analysis (PCA) is used to this end (Stock & Watson, 2002a, JASA/ 2002b, JBES, Artis et al., 2005, JF). • Let the data speak themselves. • The PCs subsume all the available information. • Spillover effects may also be detected. • Rich data set of petroleum futures.

  4. Related Literature • PCA & Petroleum Markets. • Cortazar & Schwartz (JoD, 1994), Tolmasky & Hindanov (JFM, 2002). • Clewlow and Strickland (1999). • Järvinen (2003). • Forecasting the prices of petroleum futures. • Sadorsky (EE, 2002). • Cabbibo & Fiorenzani (Energy Risk, 2004).

  5. Outline • Background – Motivation. • This paper – Contributions – Related Literature. • The Data. • Principal Components Analysis (PCA): Results. • PCA & Forecasting Power. • Autoregressions. • Conclusions – Implications – Future research.

  6. The Data Set • Daily settlement futures prices on the: • WTI NYMEX Crude oil (CL). • IPE Brent Crude Oil (CO). • Heating Oil (HO). • Gasoline (HU). • The Bloomberg generic series are used. • Filtering constraints. • CL1-CL9, CO1-CO7, HO1-HO7, HU1-HU7. • The sample is chosen over 1/1/1993 – 31/12/2003.

  7. PCA: Results • Separate PCA & Joint PCA. • PCA has been applied to the daily changes. • Three principal components (PCs) are retained. • Stability of the results has been checked.

  8. Joint PCA: PCs

  9. PCA and Forecasting Power: Setting • Let be the j-maturity series measured at time t, j=CL1,…, CL9, CO1,…, CO7, HO1,…, HO9, HU1,…, HU7. • Separate PCA: • Joint PCA: • The regressors are stationary, non-normal though. • General to specific approach is followed.

  10. PCA and Forecasting Power: Results

  11. Separate PCA: NYMEX Crude Oil

  12. Separate PCA: IPE Crude Oil

  13. Separate PCA: Heating Oil

  14. Separate PCA: Gasoline

  15. Joint PCA: Results • The joint PCs have no predictive power in the case of NYMEX & IPE crude oil.

  16. Autoregressions • Univariate and Vector autoregressions are also run. j = CL1,…, CL9, CO1,…, CO7, HO1,…, HO9, HU1,…, HU7. ΔFtl is the (J*1) vector that consists of the changes of the j=1,…,J maturity for each commodity l=CL, CO, HO, HU, Φlis the (J*J) matrix of coefficients of the l-commodity, cl, utl are the l-commodity (J*1) vectors of constants and error terms respectively. • No forecasting power is detected either.

  17. Conclusions • Can we forecast the term structure of petroleum futures? • PCA has been used (separately & jointly). • A rich data set has been employed. • Three factors govern the dynamics of the petroleum futures prices. • Some of the factors are significant but the R2’s are very small. • Results are corroborated by univariate and vector autoregressions.

  18. Implications – Future Research • The dynamics of petroleum futures can not be forecasted. • The dynamics of petroleum futures prices are stable over time. • Spillover effects are detected between the four markets (also Lin & Tamvakis, 2001, EE; Girma and Paulson,1999, JFM). • Future research: Alternative variants of the PCA model may be useful. • GARCH-type effects. • Non-linear PCA models.

  19. Thank you for listening! gskiado@unipi.gr http://iweb.xrh.unipi.gr/~gskiado/index.htm

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