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

Citations

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Cited by:

  1. 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.
  2. Da Fonseca, José & Ignatieva, Katja, 2019. "Jump activity analysis for affine jump-diffusion models: Evidence from the commodity market," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 45-62.
  3. Florian Ielpo & Benoît Sévi, 2014. "Forecasting the density of oil futures," Working Papers 2014-601, Department of Research, Ipag Business School.
  4. Magkonis, Georgios & Tsouknidis, Dimitris A., 2017. "Dynamic spillover effects across petroleum spot and futures volatilities, trading volume and open interest," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 104-118.
  5. Jena, Sangram Keshari & Lahiani, Amine & Tiwari, Aviral Kumar & Roubaud, David, 2021. "Uncovering the complex asymmetric relationship between trading activity and commodity futures price: Evidenced from QNARDL study," Resources Policy, Elsevier, vol. 74(C).
  6. Lee, Hwang Hee & Hyun, Jung-Soon, 2019. "The asymmetric effect of equity volatility on credit default swap spreads," Journal of Banking & Finance, Elsevier, vol. 98(C), pages 125-136.
  7. 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.
  8. 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.
  9. Julien, Chevallier & Sévi, Benoît, 2013. "A Fear Index to Predict Oil Futures Returns," Energy: Resources and Markets 156489, Fondazione Eni Enrico Mattei (FEEM).
  10. 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.
  11. Palomba, Giulio & Tedeschi, Marco, 2024. "Contagion among European financial indices, evidence from a quantile VAR approach," Economic Systems, Elsevier, vol. 48(2).
  12. Jing Ao & Jihui Chen, 2020. "Price Volatility, the Maturity Effect, and Global Oil Prices: Evidence from Chinese Commodity Futures Markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 44(4), pages 627-654, October.
  13. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
  14. Doojin RYU & Hyein SHIM, 2017. "Intraday Dynamics of Asset Returns, Trading Activities, and Implied Volatilities: A Trivariate GARCH Framework," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 45-61, June.
  15. Go, You-How & Lau, Wee-Yeap, 2020. "The impact of global financial crisis on informational efficiency: Evidence from price-volume relation in crude palm oil futures market," Journal of Commodity Markets, Elsevier, vol. 17(C).
  16. 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.
  17. repec:ipg:wpaper:2014-053 is not listed on IDEAS
  18. 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.
  19. Xunfa Lu & Huanhuan Yan & Pengchao He & Nicholas Apergis, 2025. "Multifractal relationship between decomposed oil price shocks and trading volume," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-16, December.
  20. 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.
  21. Ji, Qiang & Zhang, Dayong, 2019. "China’s crude oil futures: Introduction and some stylized facts," Finance Research Letters, Elsevier, vol. 28(C), pages 376-380.
  22. Rannou, Yves & Boutabba, Mohamed Amine & Barneto, Pascal, 2021. "Are Green Bond and Carbon Markets in Europe complements or substitutes? Insights from the activity of power firms," Energy Economics, Elsevier, vol. 104(C).
  23. Su, Jian & Wang, Wenya & Bai, Yang & Zhou, Peng, 2025. "Measuring the natural gas price features of the Asia-Pacific market from a complex network perspective," Energy, Elsevier, vol. 314(C).
  24. 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.
  25. You‐How Go & Wee‐Yeap Lau, 2023. "What do we know about informational efficiency? Three puzzles and the new direction forward," Journal of Economic Surveys, Wiley Blackwell, vol. 37(4), pages 1489-1525, September.
  26. Xu Gong & Boqiang Lin, 2021. "Effects of structural changes on the prediction of downside volatility in futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(7), pages 1124-1153, July.
  27. Niu, Zibo & Ma, Feng & Zhang, Hongwei, 2022. "The role of uncertainty measures in volatility forecasting of the crude oil futures market before and during the COVID-19 pandemic," Energy Economics, Elsevier, vol. 112(C).
  28. Zied Ftiti & Fredj Jawadi & Waël Louhichi, 2017. "Modelling the relationship between future energy intraday volatility and trading volume with wavelet," Applied Economics, Taylor & Francis Journals, vol. 49(20), pages 1981-1993, April.
  29. repec:dau:papers:123456789/11714 is not listed on IDEAS
  30. Da Fonseca, José & Xu, Yahua, 2017. "Higher moment risk premiums for the crude oil market: A downside and upside conditional decomposition," Energy Economics, Elsevier, vol. 67(C), pages 410-422.
  31. Tian, Xiao & Duong, Huu Nhan & Kalev, Petko S., 2019. "Information content of the limit order book for crude oil futures price volatility," Energy Economics, Elsevier, vol. 81(C), pages 584-597.
  32. Todorova, Neda & Clements, Adam E., 2018. "The volatility-volume relationship in the LME futures market for industrial metals," Resources Policy, Elsevier, vol. 58(C), pages 111-124.
  33. Todorova, Neda, 2017. "The asymmetric volatility in the gold market revisited," Economics Letters, Elsevier, vol. 150(C), pages 138-141.
  34. Gao, Wang & He, Miao & Zhang, Hongwei, 2025. "Risk connectedness and portfolios between fossil energy, new energy and environmental governance markets," International Review of Financial Analysis, Elsevier, vol. 104(PA).
  35. 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.
  36. Ignatieva, Katja & Wong, Patrick, 2022. "Modelling high frequency crude oil dynamics using affine and non-affine jump–diffusion models," Energy Economics, Elsevier, vol. 108(C).
  37. Fatih Çemrek & Hakkı Polat, 2014. "Modeling Natural Gas Prices Volatility," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 2(1), pages 1-12, June.
  38. Ma, Feng & Wahab, M.I.M. & Huang, Dengshi & Xu, Weiju, 2017. "Forecasting the realized volatility of the oil futures market: A regime switching approach," Energy Economics, Elsevier, vol. 67(C), pages 136-145.
  39. Małgorzata Błażejowska & Anna Czarny & Iwona Kowalska & Andrzej Michalczewski & Paweł Stępień, 2025. "The Hedging Strategies of Enterprises in the European Union Allowances Market—Implementation Actions for Sustainable Development," Sustainability, MDPI, vol. 17(5), pages 1-27, February.
  40. Zhenwei Li & Jing Han & Yuping Song, 2020. "On the forecasting of high‐frequency financial time series based on ARIMA model improved by deep learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1081-1097, November.
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