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Can the Dynamics of Petroleum Futures be Forecasted? Evidence from Major Markets

Can the Dynamics of Petroleum Futures be Forecasted? Evidence from Major Markets. Thalia Chantziara 1 & George Skiadopoulos 2 ¹ Independent ² Dept. of Banking and Financial Management, University of Piraeus & Financial Options Research Centre, University of Warwick Commodities 2007

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Can the Dynamics of Petroleum Futures be Forecasted? Evidence from Major Markets

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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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