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Explaining the convenience yield in the WTI crude oil market using realized volatility and jumps

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  • Benoît Sévi

Abstract

In this paper, we first provide empirical evidence of the existence of intraday jumps in the crude oil price series. We then show that these jumps, in conjunction with realized volatility measures, are important in modeling the convenience yield over the 2001-2010 period. Our empirical results indicate that lagged jumpmean only explains around 16% of the weekly convenience yield. Our best specification, including variation in inventories, eight-week realized variance and the 250-day jumpmean is able to explain around 61% of the weekly convenience yield. Importantly, our results are not driven by the simultaneous determination of the various variables at work as we only use lagged variables in all regressions.

Suggested Citation

  • Benoît Sévi, 2014. "Explaining the convenience yield in the WTI crude oil market using realized volatility and jumps," Working Papers 2014-602, Department of Research, Ipag Business School.
  • Handle: RePEc:ipg:wpaper:2014-602
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    Cited by:

    1. Nguyen, Duc Binh Benno & Prokopczuk, Marcel, 2019. "Jumps in commodity markets," Journal of Commodity Markets, Elsevier, vol. 13(C), pages 55-70.
    2. Vortelinos, Dimitrios I. & Lakshmi, Geeta, 2015. "Market risk of BRIC Eurobonds in the financial crisis period," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 295-310.
    3. Shi, Wendong & Sun, Jingwei, 2016. "Aggregation and long-memory: An analysis based on the discrete Fourier transform," Economic Modelling, Elsevier, vol. 53(C), pages 470-476.

    More about this item

    Keywords

    convenience yield; realized volatility; jump; inventory.;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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
    • G1 - Financial Economics - - General Financial Markets
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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