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On the volatility-volume relationship in energy futures markets using intraday data

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  • Julien Chevallier
  • Benoît Sévi

Abstract

This paper investigates the relationship between trading volume and price volatility in the crude oil and natural gas futures markets when using high-frequency data. By regressing various realized volatility measures (with/without jumps) on trading volume and trading frequency, our results feature a contemporaneous and largely positive relationship. Furthermore, we test whether the volatility-volume relationship is symmetric for energy futures by considering positive and negative realized semivariance. We show that (i) an asymmetric volatility-volume relationship indeed exists, (ii) trading volume and trading frequency significantly affect negative and positive realized semivariance, and (iii) the information content of negative realized semivariance is higher than for positive realized semivariance.

Suggested Citation

  • Julien Chevallier & Benoît Sévi, 2011. "On the volatility-volume relationship in energy futures markets using intraday data," EconomiX Working Papers 2011-16, University of Paris Nanterre, EconomiX.
  • Handle: RePEc:drm:wpaper:2011-16
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    References listed on IDEAS

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    Cited by:

    1. repec:rjr:romjef:v::y:2017:i:2:p:45-61 is not listed on IDEAS
    2. Fredj Jawadi & Waël Louhichi & Abdoulkarim Idi Cheffou & Rivo Randrianarivony, 2016. "Intraday jumps and trading volume: a nonlinear Tobit specification," Review of Quantitative Finance and Accounting, Springer, vol. 47(4), pages 1167-1186, November.
    3. repec:ipg:wpaper:2014-053 is not listed on IDEAS
    4. Sévi, Benoît, 2015. "Explaining the convenience yield in the WTI crude oil market using realized volatility and jumps," Economic Modelling, Elsevier, vol. 44(C), pages 243-251.
    5. Slim, Skander & Dahmene, Meriam, 2016. "Asymmetric information, volatility components and the volume–volatility relationship for the CAC40 stocks," Global Finance Journal, Elsevier, vol. 29(C), pages 70-84.
    6. Florian Ielpo & Benoît Sévi, 2014. "Forecasting the density of oil futures," Working Papers 2014-601, Department of Research, Ipag Business School.
    7. repec:eee:finana:v:52:y:2017:i:c:p:104-118 is not listed on IDEAS
    8. Todorova, Neda, 2017. "The asymmetric volatility in the gold market revisited," Economics Letters, Elsevier, vol. 150(C), pages 138-141.
    9. Julien Chevallier & Benoît Sévi, 2013. "A Fear Index to Predict Oil Futures Returns," Working Papers 2013.62, Fondazione Eni Enrico Mattei.
    10. Da Fonseca, José & Ignatieva, Katja & Ziveyi, Jonathan, 2016. "Explaining credit default swap spreads by means of realized jumps and volatilities in the energy market," Energy Economics, Elsevier, vol. 56(C), pages 215-228.
    11. Geng, Jiang-Bo & Ji, Qiang & Fan, Ying, 2016. "The behaviour mechanism analysis of regional natural gas prices: A multi-scale perspective," Energy, Elsevier, vol. 101(C), pages 266-277.
    12. Jawadi, Fredj & Louhichi, Waël & Ameur, Hachmi Ben & Cheffou, Abdoulkarim Idi, 2016. "On oil-US exchange rate volatility relationships: An intraday analysis," Economic Modelling, Elsevier, vol. 59(C), pages 329-334.
    13. Sévi, Benoît, 2014. "Forecasting the volatility of crude oil futures using intraday data," European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.
    14. Alizadeh, Amir H. & Tamvakis, Michael, 2016. "Market conditions, trader types and price–volume relation in energy futures markets," Energy Economics, Elsevier, vol. 56(C), pages 134-149.
    15. Stephanie-Carolin Grosche, 2014. "What Does Granger Causality Prove? A Critical Examination of the Interpretation of Granger Causality Results on Price Effects of Index Trading in Agricultural Commodity Markets," Journal of Agricultural Economics, Wiley Blackwell, vol. 65(2), pages 279-302, June.
    16. repec:dau:papers:123456789/11714 is not listed on IDEAS
    17. repec:eee:eneeco:v:67:y:2017:i:c:p:410-422 is not listed on IDEAS
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    More about this item

    Keywords

    Trading Volume; Price Volatility; Crude Oil Futures; Natural Gas Futures; High-Frequency Data; Realized Volatility; Bipower Variation; Median Realized Volatility; Realised Semivariance; Jump;

    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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