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Analyzing Oil Futures with a Dynamic Nelson‐Siegel Model

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  • Niels S. GrØnborg
  • Asger Lunde

Abstract

The dynamic Nelson–Siegel model is used to model the term structure of futures contracts on oil and obtain forecasts of prices of these contracts. Three factors are extracted and modelled in a very flexible framework. The outcome of this exercise is a class of models which describes the observed prices of futures contracts well and performs better than conventional benchmarks in realistic real‐time out‐of‐sample exercises. © 2015 Wiley Periodicals, Inc. Jrl Fut Mark 36:153–173, 2016

Suggested Citation

  • Niels S. GrØnborg & Asger Lunde, 2016. "Analyzing Oil Futures with a Dynamic Nelson‐Siegel Model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(2), pages 153-173, February.
    • Handle: RePEc:wly:jfutmk:v:36:y:2016:i:2:p:153-173
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    Cited by:

    1. Anders Merrild Posselt, 2022. "Dynamics in the VIX complex," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(9), pages 1665-1687, September.
    2. Horváth, Lajos & Liu, Zhenya & Rice, Gregory & Wang, Shixuan, 2020. "A functional time series analysis of forward curves derived from commodity futures," International Journal of Forecasting, Elsevier, vol. 36(2), pages 646-665.
    3. Lajos Horváth & Zhenya Liu & Curtis Miller & Weiqing Tang, 2024. "Breaks in term structures: Evidence from the oil futures markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2317-2341, April.
    4. Mario Figueiredo & Yuri F. Saporito, 2023. "Forecasting the term structure of commodities future prices using machine learning," Digital Finance, Springer, vol. 5(1), pages 57-90, March.
    5. Won Joong Kim & Gunho Jung & Sun-Yong Choi, 2020. "Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning," Complexity, Hindawi, vol. 2020, pages 1-23, July.
    6. Sudarshan Kumar & Sobhesh Kumar Agarwalla & Jayanth R. Varma & Vineet Virmani, 2023. "Harvesting the volatility smile in a large emerging market: A Dynamic Nelson–Siegel approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(11), pages 1615-1644, November.
    7. Bianchi, Robert J. & Fan, John Hua & Miffre, Joëlle & Zhang, Tingxi, 2023. "Exploiting the dynamics of commodity futures curves," Journal of Banking & Finance, Elsevier, vol. 154(C).
    8. Vahidin Jeleskovic & Anastasios Demertzidis, 2018. "Comparing different methods for the estimation of interbank intraday yield curves," MAGKS Papers on Economics 201839, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    9. Kleppe, Tore Selland & Liesenfeld, Roman & Moura, Guilherme Valle & Oglend, Atle, 2022. "Analyzing Commodity Futures Using Factor State-Space Models with Wishart Stochastic Volatility," Econometrics and Statistics, Elsevier, vol. 23(C), pages 105-127.
    10. Oguzhan Cepni, Duc Khuong Nguyen, and Ahmet Sensoy, 2022. "News Media and Attention Spillover across Energy Markets: A Powerful Predictor of Crude Oil Futures Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Special I).
    11. 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).
    12. Spencer, Simon & Bredin, Don, 2019. "Agreement matters: OPEC announcement effects on WTI term structure," Energy Economics, Elsevier, vol. 80(C), pages 589-609.
    13. Gao, Xin & Li, Bingxin & Liu, Rui, 2023. "The relative pricing of WTI and Brent crude oil futures: Expectations or risk premia?," Journal of Commodity Markets, Elsevier, vol. 30(C).
    14. Guo, Zi-Yi, 2022. "Risk management of Bitcoin futures with GARCH models," Finance Research Letters, Elsevier, vol. 45(C).

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