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Analyzing Oil Futures with a Dynamic Nelson-Siegel Model

Author

Listed:
  • Niels S. Hansen

    (Aarhus University and CREATES)

  • Asger Lunde

    (Aarhus University and CREATES)

Abstract

In this paper we are interested in the term structure of futures contracts on oil. The objective is to specify a relatively parsimonious model which explains data well and performs well in a real time out of sample forecasting. The dynamic Nelson-Siegel model is normally used to analyze and forecast interest rates of different maturities. The structure of oil futures resembles the structure of interest rates and this motivates the use of this model for our purposes. The data set is vast and the dynamic Nelson-Siegel model allows for a significant dimension reduction by introducing three factors. By performing a series of cross-section regressions we obtain time series for these factors and we focus on modeling their joint distribution. Using copula decomposition we can set up a model for each factor individually along with a model for their dependence structure. When a reasonable model for the factors has been specified it can be used to forecast prices of futures contracts with different maturities. The outcome of this exercise is a class of models which describes the observed futures contracts well and forecasts better than conventional benchmarks. We carry out a real time value at risk analysis and show that our class of models performs well.

Suggested Citation

  • Niels S. Hansen & Asger Lunde, 2013. "Analyzing Oil Futures with a Dynamic Nelson-Siegel Model," CREATES Research Papers 2013-36, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2013-36
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    1. Emmanuel Farhi & Ricardo Caballero & Pierre-Olivier Gourinchas, "undated". "Financial Crash, Commodity Prices and Global Imbalances," Working Paper 20933, Harvard University OpenScholar.
    2. Koopman, Siem Jan & Mallee, Max I. P. & Van der Wel, Michel, 2010. "Analyzing the Term Structure of Interest Rates Using the Dynamic Nelson–Siegel Model With Time-Varying Parameters," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(3), pages 329-343.
    3. Giacomini, Raffaella & Komunjer, Ivana, 2005. "Evaluation and Combination of Conditional Quantile Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 416-431, October.
    4. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    5. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    6. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    7. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    8. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
    9. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    10. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    11. Jason West, 2011. "Long-Dated Agricultural Futures Price Estimates Using the Seasonal Nelson-Siegel Model," Discussion Papers in Finance finance:201107, Griffith University, Department of Accounting, Finance and Economics.
    12. Schwartz, Eduardo S, 1997. "The Stochastic Behavior of Commodity Prices: Implications for Valuation and Hedging," Journal of Finance, American Finance Association, vol. 52(3), pages 923-973, July.
    13. Helyette Geman, 2005. "Commodities and Commodity Derivatives. Modeling and Pricing for Agriculturals, Metals and Energy," Post-Print halshs-00144182, HAL.
    14. Morten B. Jensen & Asger Lunde, 2001. "The NIG-S&ARCH model: a fat-tailed, stochastic, and autoregressive conditional heteroskedastic volatility model," Econometrics Journal, Royal Economic Society, vol. 4(2), pages 1-10.
    15. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    16. repec:dau:papers:123456789/607 is not listed on IDEAS
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    Cited by:

    1. van Huellen, Sophie, 2020. "Too much of a good thing? Speculative effects on commodity futures curves," Journal of Financial Markets, Elsevier, vol. 47(C).
    2. Bredin, Don & O'Sullivan, Conall & Spencer, Simon, 2021. "Forecasting WTI crude oil futures returns: Does the term structure help?," Energy Economics, Elsevier, vol. 100(C).

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    More about this item

    Keywords

    Oil futures; Nelson-Siegel; Normal Inverse Gaussian; GARCH; Copula.;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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