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Benoît Sévi
(Benoit Sevi)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Olivier Rousse & Benoît Sévi, 2019. "Informed Trading in the WTI Oil Futures Market," Post-Print hal-02024317, HAL.

    Cited by:

    1. Sultan Alturki & Alexander Kurov, 2022. "Market inefficiencies surrounding energy announcements," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(1), pages 172-188, January.

  2. Derek Bunn & Julien Chevallier & Yannick Le Pen & Benoît Sévi, 2017. "Fundamental and Financial Influences on the Co-movement of Oil and Gas prices," Post-Print hal-01619890, HAL.

    Cited by:

    1. Wu, Nan & Wen, Fenghua & Gong, Xu, 2022. "Marionettes behind co-movement of commodity prices: Roles of speculative and hedging activities," Energy Economics, Elsevier, vol. 115(C).
    2. Jozef Baruník & Evžen Kocenda, 2019. "Total, Asymmetric and Frequency Connectedness Between Oil and Forex Markets," CESifo Working Paper Series 7756, CESifo.
    3. Lu-Tao Zhao & Li-Na Liu & Zi-Jie Wang & Ling-Yun He, 2019. "Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach," Sustainability, MDPI, vol. 11(14), pages 1-20, July.
    4. Derek W. Bunn, 2021. "Observations on “Risk Transmission Across Supply Chains”," Production and Operations Management, Production and Operations Management Society, vol. 30(12), pages 4588-4589, December.
    5. Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2021. "Common factors and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 74(C).
    6. Okhrin, Yarema & Uddin, Gazi Salah & Yahya, Muhammad, 2023. "Nonlinear and asymmetric interconnectedness of crude oil with financial and commodity markets," Energy Economics, Elsevier, vol. 125(C).
    7. Rousse, O. & Sévi, B., 2016. "Informed trading in oil-futures market," Working Papers 2016-07, Grenoble Applied Economics Laboratory (GAEL).
    8. Lyócsa, Štefan & Molnár, Peter, 2018. "Exploiting dependence: Day-ahead volatility forecasting for crude oil and natural gas exchange-traded funds," Energy, Elsevier, vol. 155(C), pages 462-473.
    9. Marek Kwas & Alessia Paccagnini & Michal Rubaszek, 2020. "Common factors and the dynamics of cereal prices. A forecasting perspective," CAMA Working Papers 2020-47, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    10. Guan, Qing & An, Haizhong, 2017. "The exploration on the trade preferences of cooperation partners in four energy commodities’ international trade: Crude oil, coal, natural gas and photovoltaic," Applied Energy, Elsevier, vol. 203(C), pages 154-163.
    11. Li, Fengyun & Li, Xingmei & Zheng, Haofeng & Yang, Fei & Dang, Ruinan, 2021. "How alternative energy competition shocks natural gas development in China: A novel time series analysis approach," Resources Policy, Elsevier, vol. 74(C).
    12. Kim, Jong-Min & Tabacu, Lucia & Jung, Hojin, 2020. "A quantile-copula approach to dependence between financial assets," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    13. Halser, Christoph & Paraschiv, Florentina & Russo, Marianna, 2023. "Oil–gas price relationships on three continents: Disruptions and equilibria," Journal of Commodity Markets, Elsevier, vol. 31(C).
    14. Yao, Ting & Zhang, Yue-Jun & Ma, Chao-Qun, 2017. "How does investor attention affect international crude oil prices?," Applied Energy, Elsevier, vol. 205(C), pages 336-344.
    15. Jonathan Doh & Pawan Budhwar & Geoffrey Wood, 2021. "Long-term energy transitions and international business: Concepts, theory, methods, and a research agenda," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 52(5), pages 951-970, July.

  3. Nguyen, Duc Khuong & Sévi, Benoît & Sjö, Bo & Salah Uddin, Gazi, 2015. "The role of trade openness and investment in examining the energy-growth-pollution nexus: Empirical evidence for China and India," MPRA Paper 75769, University Library of Munich, Germany, revised Dec 2016.

    Cited by:

    1. Oruj Gasimli & Ihtisham ul Haq & Sisira Kumara Naradda Gamage & Fadi Shihadeh & Prasanna Sisira Kumara Rajapakshe & Muhammad Shafiq, 2019. "Energy, Trade, Urbanization and Environmental Degradation Nexus in Sri Lanka: Bounds Testing Approach," Energies, MDPI, vol. 12(9), pages 1-16, April.
    2. Cheng-Feng Wu & Shian-Chang Huang & Chei-Chang Chiou & Tsangyao Chang & Yung-Chih Chen, 2022. "The Relationship Between Economic Growth and Electricity Consumption: Bootstrap ARDL Test with a Fourier Function and Machine Learning Approach," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1197-1220, December.
    3. Hazwan Haini, 2021. "Examining the impact of ICT, human capital and carbon emissions: Evidence from the ASEAN economies," International Economics, CEPII research center, issue 166, pages 116-125.
    4. Feng Wang & Yijie Jiang & Wulin Zhang & Fang Yang, 2019. "Elasticity of factor substitution and driving factors of energy intensity in China’s industry," Energy & Environment, , vol. 30(3), pages 385-407, May.
    5. Hongzhong Fan & Md Ismail Hossain, 2018. "Technological Innovation, Trade Openness, CO2 Emission and Economic Growth: Comparative Analysis between China and India," International Journal of Energy Economics and Policy, Econjournals, vol. 8(6), pages 240-257.
    6. Guglielmo Maria Caporale & Gloria Claudio-Quiroga & Luis A. Gil-Alana, 2019. "CO2 Emissions and GDP: Evidence from China," CESifo Working Paper Series 7881, CESifo.
    7. Siddhartha Pradeep, 2022. "Role of monetary policy on CO2 emissions in India," SN Business & Economics, Springer, vol. 2(1), pages 1-33, January.
    8. Mahalik, Mantu Kumar & Villanthenkodath, Muhammed Ashiq & Mallick, Hrushikesh & Gupta, Monika, 2021. "Assessing the effectiveness of total foreign aid and foreign energy aid inflows on environmental quality in India," Energy Policy, Elsevier, vol. 149(C).
    9. Sharma, Rajesh & Sinha, Avik & Kautish, Pradeep, 2020. "Examining the impacts of economic and demographic aspects on the ecological footprint in South and Southeast Asian countries," MPRA Paper 104245, University Library of Munich, Germany, revised 2020.

  4. Benoît Sévi, 2014. "Forecasting the volatility of crude oil futures using intraday data," Post-Print hal-01463921, HAL.

    Cited by:

    1. Gong, Xu & Lin, Boqiang, 2018. "The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market," Energy Economics, Elsevier, vol. 74(C), pages 370-386.
    2. Degiannakis, Stavros & Filis, George & Klein, Tony & Walther, Thomas, 2019. "Forecasting Realized Volatility of Agricultural Commodities," MPRA Paper 96267, University Library of Munich, Germany.
    3. Matteo Bonato & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Investor Happiness and Predictability of the Realized Volatility of Oil Price," Working Papers 202009, University of Pretoria, Department of Economics.
    4. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting Realized Oil-Price Volatility: The Role of Financial Stress and Asymmetric Loss," Working Papers 201903, University of Pretoria, Department of Economics.
    5. Chao Liang & Yin Liao & Feng Ma & Bo Zhu, 2022. "United States Oil Fund volatility prediction: the roles of leverage effect and jumps," Empirical Economics, Springer, vol. 62(5), pages 2239-2262, May.
    6. Asai, Manabu & Gupta, Rangan & McAleer, Michael, 2020. "Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 933-948.
    7. 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.
    8. Manabu Asai & Rangan Gupta & Michael McAleer, 2019. "The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures," Working Papers 201925, University of Pretoria, Department of Economics.
    9. 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.
    10. 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.
    11. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Narayan, Paresh Kumar, 2016. "Intraday volatility interaction between the crude oil and equity markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 40(C), pages 1-13.
    12. Barunik, Jozef & Krehlik, Tomas & Vacha, Lukas, 2016. "Modeling and forecasting exchange rate volatility in time-frequency domain," European Journal of Operational Research, Elsevier, vol. 251(1), pages 329-340.
    13. Xu, Weiju & Ma, Feng & Chen, Wang & Zhang, Bing, 2019. "Asymmetric volatility spillovers between oil and stock markets: Evidence from China and the United States," Energy Economics, Elsevier, vol. 80(C), pages 310-320.
    14. Liu, Jing & Ma, Feng & Zhang, Yaojie, 2019. "Forecasting the Chinese stock volatility across global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 466-477.
    15. 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.
    16. Dehua Shen & Andrew Urquhart & Pengfei Wang, 2020. "Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks," European Financial Management, European Financial Management Association, vol. 26(5), pages 1294-1323, November.
    17. Zhang, Yue-Jun & Yao, Ting & He, Ling-Yun & Ripple, Ronald, 2019. "Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models?," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 302-317.
    18. Zied Ftiti & Aviral Tiwari & Amél Belanès & Khaled Guesmi, 2014. "Tests of Financial Market Contagion: Evolutionary Cospectral Analysis V.S. Wavelet Analysis," Working Papers 2014-577, Department of Research, Ipag Business School.
    19. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2015. "Forecasting implied volatility indices worldwide: A new approach," MPRA Paper 72084, University Library of Munich, Germany.
    20. Chatziantoniou, Ioannis & Degiannakis, Stavros & Filis, George, 2019. "Futures-based forecasts: How useful are they for oil price volatility forecasting?," Energy Economics, Elsevier, vol. 81(C), pages 639-649.
    21. Degiannakis, Stavros & Filis, George & Arora, Vipin, 2018. "Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence," MPRA Paper 96270, University Library of Munich, Germany.
    22. Xiufeng Xing & Yingjia Cong & Yu Wang & Xueqing Wang, 2023. "The Impact of COVID-19 and War in Ukraine on Energy Prices of Oil and Natural Gas," Sustainability, MDPI, vol. 15(19), pages 1-16, September.
    23. Štefan Lyócsa & Peter Molnár, 2016. "Volatility forecasting of strategically linked commodity ETFs: gold-silver," Quantitative Finance, Taylor & Francis Journals, vol. 16(12), pages 1809-1822, December.
    24. Xu, Yanyan & Huang, Dengshi & Ma, Feng & Qiao, Gaoxiu, 2019. "Liquidity and realized range-based volatility forecasting: Evidence from China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1102-1113.
    25. Dimitrios I. Vortelinos & Konstantinos Gkillas, 2018. "Intraday realised volatility forecasting and announcements," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 9(1), pages 88-118.
    26. Lv, Wendai, 2018. "Does the OVX matter for volatility forecasting? Evidence from the crude oil market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 916-922.
    27. Zhang, Yaojie & Wahab, M.I.M. & Wang, Yudong, 2023. "Forecasting crude oil market volatility using variable selection and common factor," International Journal of Forecasting, Elsevier, vol. 39(1), pages 486-502.
    28. Lyócsa, Štefan & Molnár, Peter & Todorova, Neda, 2017. "Volatility forecasting of non-ferrous metal futures: Covariances, covariates or combinations?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 51(C), pages 228-247.
    29. Qiao, Gaoxiu & Teng, Yuxin & Li, Weiping & Liu, Wenwen, 2019. "Improving volatility forecasting based on Chinese volatility index information: Evidence from CSI 300 index and futures markets," The North American Journal of Economics and Finance, Elsevier, vol. 49(C), pages 133-151.
    30. Liu, Jing & Wei, Yu & Ma, Feng & Wahab, M.I.M., 2017. "Forecasting the realized range-based volatility using dynamic model averaging approach," Economic Modelling, Elsevier, vol. 61(C), pages 12-26.
    31. Ma, Feng & Liu, Jing & Wahab, M.I.M. & Zhang, Yaojie, 2018. "Forecasting the aggregate oil price volatility in a data-rich environment," Economic Modelling, Elsevier, vol. 72(C), pages 320-332.
    32. Chiu, Mei Choi & Wong, Hoi Ying & Zhao, Jing, 2015. "Commodity derivatives pricing with cointegration and stochastic covariances," European Journal of Operational Research, Elsevier, vol. 246(2), pages 476-486.
    33. Davide De Gaetano, 2018. "Forecast Combinations in the Presence of Structural Breaks: Evidence from U.S. Equity Markets," Mathematics, MDPI, vol. 6(3), pages 1-19, March.
    34. Liu, Jing & Ma, Feng & Yang, Ke & Zhang, Yaojie, 2018. "Forecasting the oil futures price volatility: Large jumps and small jumps," Energy Economics, Elsevier, vol. 72(C), pages 321-330.
    35. Riza Demirer & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2022. "Risk aversion and the predictability of crude oil market volatility: A forecasting experiment with random forests," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(8), pages 1755-1767, August.
    36. Mei, Dexiang & Ma, Feng & Liao, Yin & Wang, Lu, 2020. "Geopolitical risk uncertainty and oil future volatility: Evidence from MIDAS models," Energy Economics, Elsevier, vol. 86(C).
    37. Marfatia, Hardik A. & Gupta, Rangan & Cakan, Esin, 2021. "Dynamic impact of the U.S. monetary policy on oil market returns and volatility," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 159-169.
    38. Luo, Jiawen & Ji, Qiang & Klein, Tony & Todorova, Neda & Zhang, Dayong, 2020. "On realized volatility of crude oil futures markets: Forecasting with exogenous predictors under structural breaks," Energy Economics, Elsevier, vol. 89(C).
    39. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
    40. Dimos Kambouroudis & David McMillan & Katerina Tsakou, 2019. "Forecasting Realized Volatility: The role of implied volatility, leverage effect, overnight returns and volatility of realized volatility," Working Papers 2019-03, Swansea University, School of Management.
    41. Ellington, Michael, 2022. "Fat tails, serial dependence, and implied volatility index connections," European Journal of Operational Research, Elsevier, vol. 299(2), pages 768-779.
    42. Luo, Jiawen & Klein, Tony & Ji, Qiang & Hou, Chenghan, 2022. "Forecasting realized volatility of agricultural commodity futures with infinite Hidden Markov HAR models," International Journal of Forecasting, Elsevier, vol. 38(1), pages 51-73.
    43. Dudley Gilder & Leonidas Tsiaras, 2020. "Volatility forecasts embedded in the prices of crude‐oil options," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(7), pages 1127-1159, July.
    44. Degiannakis, Stavros, 2017. "The one-trading-day-ahead forecast errors of intra-day realized volatility," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1298-1314.
    45. Sabri Boubaker & Zhenya Liu & Yaosong Zhan, 2022. "Risk management for crude oil futures: an optimal stopping-timing approach," Annals of Operations Research, Springer, vol. 313(1), pages 9-27, June.
    46. Xu Gong & Boqiang Lin, 2018. "Structural breaks and volatility forecasting in the copper futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 290-339, March.
    47. Demirer, Riza & Gupta, Rangan & Pierdzioch, Christian & Shahzad, Syed Jawad Hussain, 2020. "The predictive power of oil price shocks on realized volatility of oil: A note," Resources Policy, Elsevier, vol. 69(C).
    48. Li, Wenlan & Cheng, Yuxiang & Fang, Qiang, 2020. "Forecast on silver futures linked with structural breaks and day-of-the-week effect," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    49. Mohammad Al-Shboul & Aktham Maghyereh, 2023. "Did real economic uncertainty drive risk connectedness in the oil–stock nexus during the COVID-19 outbreak? A partial wavelet coherence analysis," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 12(1), pages 1-23, December.
    50. Jing Hao & Feng He & Feng Ma & Tong Fu, 2023. "Trading around the clock: Revisit volatility spillover between crude oil and equity markets in different trading sessions," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(6), pages 771-791, June.
    51. Dimos S. Kambouroudis & David G. McMillan & Katerina Tsakou, 2021. "Forecasting realized volatility: The role of implied volatility, leverage effect, overnight returns, and volatility of realized volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(10), pages 1618-1639, October.
    52. Zhang, Yaojie & Wei, Yu & Zhang, Yi & Jin, Daxiang, 2019. "Forecasting oil price volatility: Forecast combination versus shrinkage method," Energy Economics, Elsevier, vol. 80(C), pages 423-433.
    53. Tang, Yusui & Ma, Feng, 2023. "The volatility of natural resources implications for sustainable development: Crude oil volatility prediction based on the multivariate structural regime switching," Resources Policy, Elsevier, vol. 83(C).
    54. Yaojie Zhang & Mengxi He & Danyan Wen & Yudong Wang, 2022. "Forecasting Bitcoin volatility: A new insight from the threshold regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 633-652, April.
    55. Katsiampa, Paraskevi & Corbet, Shaen & Lucey, Brian, 2019. "High frequency volatility co-movements in cryptocurrency markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 35-52.
    56. Tao, Qizhi & Wei, Yu & Liu, Jiapeng & Zhang, Ting, 2018. "Modeling and forecasting multifractal volatility established upon the heterogeneous market hypothesis," International Review of Economics & Finance, Elsevier, vol. 54(C), pages 143-153.
    57. Feng Ma & Chao Liang & Yuanhui Ma & M.I.M. Wahab, 2020. "Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1277-1290, December.
    58. Bian, Siyu & Serra, Teresa & Garcia, Philip & Irwin, Scott, 2022. "New evidence on market response to public announcements in the presence of microstructure noise," European Journal of Operational Research, Elsevier, vol. 298(2), pages 785-800.
    59. Rice, Gregory & Wirjanto, Tony & Zhao, Yuqian, 2023. "Exploring volatility of crude oil intraday return curves: A functional GARCH-X model," Journal of Commodity Markets, Elsevier, vol. 32(C).
    60. Zhang, Yue-Jun & Zhang, Lu, 2015. "Interpreting the crude oil price movements: Evidence from the Markov regime switching model," Applied Energy, Elsevier, vol. 143(C), pages 96-109.
    61. Wen, Fenghua & Gong, Xu & Cai, Shenghua, 2016. "Forecasting the volatility of crude oil futures using HAR-type models with structural breaks," Energy Economics, Elsevier, vol. 59(C), pages 400-413.
    62. Delis, Panagiotis & Degiannakis, Stavros & Giannopoulos, Kostantinos, 2021. "What should be taken into consideration when forecasting oil implied volatility index?," MPRA Paper 110831, University Library of Munich, Germany.
    63. Zhang, Yaojie & He, Mengxi & Wang, Yudong & Liang, Chao, 2023. "Global economic policy uncertainty aligned: An informative predictor for crude oil market volatility," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1318-1332.
    64. Chen, Zhonglu & Ye, Yong & Li, Xiafei, 2022. "Forecasting China's crude oil futures volatility: New evidence from the MIDAS-RV model and COVID-19 pandemic," Resources Policy, Elsevier, vol. 75(C).
    65. Bu, Ruijun & Hizmeri, Rodrigo & Izzeldin, Marwan & Murphy, Anthony & Tsionas, Mike, 2023. "The contribution of jump signs and activity to forecasting stock price volatility," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 144-164.
    66. Tarek Bouazizi & Mongi Lassoued & Zouhaier Hadhek, 2021. "Oil Price Volatility Models during Coronavirus Crisis: Testing with Appropriate Models Using Further Univariate GARCH and Monte Carlo Simulation Models," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 281-292.
    67. Wang, Jiqian & He, Xiaofeng & Ma, Feng & Li, Pan, 2022. "Uncertainty and oil volatility: Evidence from shrinkage method," Resources Policy, Elsevier, vol. 75(C).
    68. Roman Horváth & Štefan Lyócsa & Eduard Baumöhl, 2018. "Stock market contagion in Central and Eastern Europe: unexpected volatility and extreme co-exceedance," The European Journal of Finance, Taylor & Francis Journals, vol. 24(5), pages 391-412, March.
    69. Gong, Xue & Ye, Xin & Zhang, Weiguo & Zhang, Yue, 2023. "Predicting energy futures high-frequency volatility using technical indicators: The role of interaction," Energy Economics, Elsevier, vol. 119(C).
    70. 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.
    71. Akyildirim, Erdinc & Corbet, Shaen & Lucey, Brian & Sensoy, Ahmet & Yarovaya, Larisa, 2020. "The relationship between implied volatility and cryptocurrency returns," Finance Research Letters, Elsevier, vol. 33(C).
    72. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," MPRA Paper 96276, University Library of Munich, Germany.
    73. Michael Frömmel & Eyup Kadioglu, 2023. "Impact of trading hours extensions on foreign exchange volatility: intraday evidence from the Moscow exchange," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    74. Hui Qu & Tianyang Wang & Peng Shangguan & Mengying He, 2024. "Revisiting the puzzle of jumps in volatility forecasting: The new insights of high‐frequency jump intensity," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(2), pages 218-251, February.
    75. Lyócsa, Štefan & Todorova, Neda, 2021. "What drives volatility of the U.S. oil and gas firms?," Energy Economics, Elsevier, vol. 100(C).
    76. Fan, Lina & Yang, Hao & Zhai, Jia & Zhang, Xiaotao, 2023. "Forecasting stock volatility during the stock market crash period: The role of Hawkes process," Finance Research Letters, Elsevier, vol. 55(PA).
    77. Klein, Tony & Todorova, Neda, 2021. "Night trading with futures in China: The case of Aluminum and Copper," Resources Policy, Elsevier, vol. 73(C).
    78. Yaxiong Zeng & Diego Klabjan, 2017. "Online Adaptive Machine Learning Based Algorithm for Implied Volatility Surface Modeling," Papers 1706.01833, arXiv.org, revised Jun 2018.
    79. Ma, Feng & Wang, Jiqian & Wahab, M.I.M. & Ma, Yuanhui, 2023. "Stock market volatility predictability in a data-rich world: A new insight," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1804-1819.
    80. Yaojie Zhang & Yudong Wang & Feng Ma & Yu Wei, 2022. "To jump or not to jump: momentum of jumps in crude oil price volatility prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-31, December.
    81. Alizadeh, Amir H. & Huang, Chih-Yueh & Marsh, Ian W., 2021. "Modelling the volatility of TOCOM energy futures: A regime switching realised volatility approach," Energy Economics, Elsevier, vol. 93(C).
    82. Matteo Bonato & Rangan Gupta & Chi Keung Marco Lau & Shixuan Wang, 2019. "Moments-Based Spillovers across Gold and Oil Markets," Working Papers 201966, University of Pretoria, Department of Economics.
    83. Pan, Zhiyuan & Wang, Yudong & Wu, Chongfeng & Yin, Libo, 2017. "Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 130-142.
    84. 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.
    85. Qianjie Geng & Xianfeng Hao & Yudong Wang, 2024. "Forecasting the volatility of crude oil futures: A time‐dependent weighted least squares with regularization constraint," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 309-325, March.
    86. Toan Luu Duc Huynh & Muhammad Shahbaz & Muhammad Ali Nasir & Subhan Ullah, 2022. "Financial modelling, risk management of energy instruments and the role of cryptocurrencies," Annals of Operations Research, Springer, vol. 313(1), pages 47-75, June.
    87. Kristjanpoller, Werner D. & Concha, Diego, 2016. "Impact of fuel price fluctuations on airline stock returns," Applied Energy, Elsevier, vol. 178(C), pages 496-504.
    88. Stefan Lyocsa & Peter Molnar & Igor Fedorko, 2016. "Forecasting Exchange Rate Volatility: The Case of the Czech Republic, Hungary and Poland," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(5), pages 453-475, October.
    89. Yongmei Fang & Bo Guan & Shangjuan Wu & Saeed Heravi, 2020. "Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 877-886, September.
    90. Yang, Cai & Gong, Xu & Zhang, Hongwei, 2019. "Volatility forecasting of crude oil futures: The role of investor sentiment and leverage effect," Resources Policy, Elsevier, vol. 61(C), pages 548-563.
    91. Wei Zhang & Kai Yan & Dehua Shen, 2021. "Can the Baidu Index predict realized volatility in the Chinese stock market?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
    92. Meng, Fanyi & Liu, Li, 2019. "Analyzing the economic sources of oil price volatility: An out-of-sample perspective," Energy, Elsevier, vol. 177(C), pages 476-486.
    93. Gong, Xu & Lin, Boqiang, 2017. "Forecasting the good and bad uncertainties of crude oil prices using a HAR framework," Energy Economics, Elsevier, vol. 67(C), pages 315-327.
    94. Degiannakis, Stavros & Filis, George, 2016. "Forecasting oil price realized volatility: A new approach," MPRA Paper 69105, University Library of Munich, Germany.
    95. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2018. "Forecasting global stock market implied volatility indices," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 111-129.
    96. Yang, Ke & Tian, Fengping & Chen, Langnan & Li, Steven, 2017. "Realized volatility forecast of agricultural futures using the HAR models with bagging and combination approaches," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 276-291.
    97. 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.
    98. Libo Yin, 2022. "The role of intermediary capital risk in predicting oil volatility," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 401-416, January.
    99. Liu, Jing & Ma, Feng & Tang, Yingkai & Zhang, Yaojie, 2019. "Geopolitical risk and oil volatility: A new insight," Energy Economics, Elsevier, vol. 84(C).
    100. 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.
    101. Chen, Zhonglu & Zhang, Li & Weng, Chen, 2023. "Does climate policy uncertainty affect Chinese stock market volatility?," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 369-381.
    102. Ma, Feng & Wahab, M.I.M. & Zhang, Yaojie, 2019. "Forecasting the U.S. stock volatility: An aligned jump index from G7 stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 54(C), pages 132-146.
    103. Konstantinos Gkillas & Paraskevi Katsiampa & Dimitrios I. Vortelinos & Mark E. Wohar, 2023. "Greek government‐debt crisis events and European financial markets: News surprises on Greek bond yields and inter‐relations of European financial markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 4037-4054, October.
    104. Feng Ma & Yu Wei & Wang Chen & Feng He, 2018. "Forecasting the volatility of crude oil futures using high-frequency data: further evidence," Empirical Economics, Springer, vol. 55(2), pages 653-678, September.
    105. Degiannakis, Stavros, 2018. "Multiple Days Ahead Realized Volatility Forecasting: Single, Combined and Average Forecasts," MPRA Paper 96272, University Library of Munich, Germany.
    106. Jiqian Wang & Feng Ma & M.I.M. Wahab & Dengshi Huang, 2021. "Forecasting China's Crude Oil Futures Volatility: The Role of the Jump, Jumps Intensity, and Leverage Effect," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 921-941, August.
    107. Zhang, Yaojie & Ma, Feng & Wei, Yu, 2019. "Out-of-sample prediction of the oil futures market volatility: A comparison of new and traditional combination approaches," Energy Economics, Elsevier, vol. 81(C), pages 1109-1120.
    108. Chen, Wang & Ma, Feng & Wei, Yu & Liu, Jing, 2020. "Forecasting oil price volatility using high-frequency data: New evidence," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 1-12.
    109. Luo, Jiawen & Marfatia, Hardik A. & Ji, Qiang & Klein, Tony, 2023. "Co-volatility and asymmetric transmission of risks between the global oil and China's futures markets," Energy Economics, Elsevier, vol. 117(C).
    110. 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.
    111. Panagiotis Delis & Stavros Degiannakis & George Filis, 2022. "What matters when developing oil price volatility forecasting frameworks?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 361-382, March.
    112. Jiawen Luo & Qun Zhang, 2024. "Air pollution, weather factors, and realized volatility forecasts of agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(2), pages 151-217, February.
    113. Degiannakis, Stavros & Filis, George, 2022. "Oil price volatility forecasts: What do investors need to know?," Journal of International Money and Finance, Elsevier, vol. 123(C).
    114. Qu, Hui & Li, Guo, 2023. "Multi-perspective investor attention and oil futures volatility forecasting," Energy Economics, Elsevier, vol. 119(C).
    115. Todorova, Neda, 2015. "The course of realized volatility in the LME non-ferrous metal market," Economic Modelling, Elsevier, vol. 51(C), pages 1-12.
    116. Jiang, Ping & Liu, Zhenkun & Wang, Jianzhou & Zhang, Lifang, 2021. "Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm," Resources Policy, Elsevier, vol. 73(C).
    117. Leong, Soon Heng & Urga, Giovanni, 2023. "A practical multivariate approach to testing volatility spillover," Journal of Economic Dynamics and Control, Elsevier, vol. 153(C).
    118. Xu Gong & Boqiang Lin, 2022. "Predicting the volatility of crude oil futures: The roles of leverage effects and structural changes," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 610-640, January.
    119. Pablo Cansado-Bravo & Carlos Rodríguez-Monroy, 2018. "Persistence of Oil Prices in Gas Import Prices and the Resilience of the Oil-Indexation Mechanism. The Case of Spanish Gas Import Prices," Energies, MDPI, vol. 11(12), pages 1-17, December.
    120. Niu, Zibo & Liu, Yuanyuan & Gao, Wang & Zhang, Hongwei, 2021. "The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China," Resources Policy, Elsevier, vol. 73(C).
    121. 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.
    122. Konstantinos Gkillas & Christoforos Konstantatos & Costas Siriopoulos, 2021. "Uncertainty Due to Infectious Diseases and Stock–Bond Correlation," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
    123. Elie Bouri, 2019. "The Effect of Jumps in the Crude Oil Market on the Sovereign Risks of Major Oil Exporters," Risks, MDPI, vol. 7(4), pages 1-15, December.
    124. Danyan Wen & Mengxi He & Yaojie Zhang & Yudong Wang, 2022. "Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 230-251, March.
    125. Ma, Feng & Zhang, Yaojie & Huang, Dengshi & Lai, Xiaodong, 2018. "Forecasting oil futures price volatility: New evidence from realized range-based volatility," Energy Economics, Elsevier, vol. 75(C), pages 400-409.
    126. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
    127. Chen, Yixiang & Ma, Feng & Zhang, Yaojie, 2019. "Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets," Energy Economics, Elsevier, vol. 81(C), pages 52-62.

  5. 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.

    Cited by:

    1. Moreno, Manuel & Novales, Alfonso & Platania, Federico, 2019. "Long-term swings and seasonality in energy markets," European Journal of Operational Research, Elsevier, vol. 279(3), pages 1011-1023.
    2. Nguyen, Duc Binh Benno & Prokopczuk, Marcel, 2017. "Jumps in Commodity Markets," Hannover Economic Papers (HEP) dp-615, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    3. Akihiro Omura & Neda Todorova, 2019. "The quantile dependence of commodity futures markets on news sentiment," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(7), pages 818-837, July.
    4. Sarwar, Suleman & Tiwari, Aviral Kumar & Tingqiu, Cao, 2020. "Analyzing volatility spillovers between oil market and Asian stock markets," Resources Policy, Elsevier, vol. 66(C).
    5. Zhang, Hanxiong & Auer, Benjamin R. & Vortelinos, Dimitrios I., 2018. "Performance ranking (dis)similarities in commodity markets," Global Finance Journal, Elsevier, vol. 35(C), pages 115-137.
    6. Laurini, Márcio Poletti & Mauad, Roberto Baltieri & Aiube, Fernando Antônio Lucena, 2020. "The impact of co-jumps in the oil sector," Research in International Business and Finance, Elsevier, vol. 52(C).
    7. Omura, Akihiro & Li, Bin & Chung, Richard & Todorova, Neda, 2018. "Convenience yield, realised volatility and jumps: Evidence from non-ferrous metals," Economic Modelling, Elsevier, vol. 70(C), pages 496-510.
    8. 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.
    9. 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.
    10. 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.

  6. Julien Chevallier & Benoît Sévi, 2014. "On the Stochastic Properties of Carbon Futures Prices," Post-Print hal-01474249, HAL.

    Cited by:

    1. Zhang, Lixia & Luo, Qin & Guo, Xiaozhu & Umar, Muhammad, 2022. "Medium-term and long-term volatility forecasts for EUA futures with country-specific economic policy uncertainty indices," Resources Policy, Elsevier, vol. 77(C).
    2. Yuanfeng Hu & Yixiang Tian & Luping Zhang, 2023. "Green Bond Pricing and Optimization Based on Carbon Emission Trading and Subsidies: From the Perspective of Externalities," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    3. Zied Ftiti & Aviral Tiwari & Amél Belanès & Khaled Guesmi, 2014. "Tests of Financial Market Contagion: Evolutionary Cospectral Analysis V.S. Wavelet Analysis," Working Papers 2014-577, Department of Research, Ipag Business School.
    4. Mason, Charles F. & A. Wilmot, Neil, 2014. "Jump processes in natural gas markets," Energy Economics, Elsevier, vol. 46(S1), pages 69-79.
    5. Bai, Yiyi & Okullo, Samuel J., 2023. "Drivers and pass-through of the EU ETS price: Evidence from the power sector," Energy Economics, Elsevier, vol. 123(C).
    6. Mehmet Balcilar & Riza Demirer & Shawkat Hammoudeh & Duc Khuong Nguyen, 2014. "Risk Spillovers across the Energy and Carbon Markets and Hedging Strategies for Carbon Risk," Working Papers 15-10, Eastern Mediterranean University, Department of Economics.
    7. Andreas Karpf & Antoine Mandel & Stefano Battiston, 2018. "Price and network dynamics in the European carbon market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01905985, HAL.
    8. Pan, Di & Zhang, Chen & Zhu, Dandan & Ji, Yuanpu & Cao, Wei, 2022. "A novel method of detecting carbon asset price jump characteristics based on significant information shocks," Finance Research Letters, Elsevier, vol. 47(PA).
    9. Charles F. Mason & Neil A. Wilmot, 2023. "On Climate Fat Tails and Politics," CESifo Working Paper Series 10815, CESifo.
    10. Julien Chevallier & Stéphane Goutte, 2014. "The goodness-of-fit of the fuel-switching price using the mean-reverting Lévy jump process," Working Papers 2014-285, Department of Research, Ipag Business School.
    11. Coleman, Andrew, 2018. "Forest-based carbon sequestration, and the role of forward, futures, and carbon-lending markets: A comparative institutions approach," Journal of Forest Economics, Elsevier, vol. 33(C), pages 95-104.
    12. Julien Chevallier & Stéphane Goutte, 2017. "Estimation of Lévy-driven Ornstein–Uhlenbeck processes: application to modeling of $$\hbox {CO}_2$$ CO 2 and fuel-switching," Annals of Operations Research, Springer, vol. 255(1), pages 169-197, August.
    13. Balietti, Anca Claudia, 2016. "Trader types and volatility of emission allowance prices. Evidence from EU ETS Phase I," Energy Policy, Elsevier, vol. 98(C), pages 607-620.

  7. Olivier Rousse & Benoît Sévi, 2013. "Citizen's participation in permit markets and social welfare under uncertainty," Post-Print halshs-00814000, HAL.

    Cited by:

    1. Nasim Gholami & Mojtaba ANSARI & Mohammadjavad MAHDAVINEJAD, 2018. "A Scientometric Review Of Citizen Participation Research: World Trend," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 13(3), pages 37-53, August.
    2. Huiru Zhao & Sen Guo & Qi Zhang & Chunjie Li, 2014. "Social Welfare Evaluation of Electric Universal Service in China: From the Perspective of Sustainability," Sustainability, MDPI, vol. 6(8), pages 1-17, August.

  8. Yannick Le Pen & Benoît Sévi, 2013. "Futures Trading and the Excess Comovement of Commodity Prices," AMSE Working Papers 1301, Aix-Marseille School of Economics, France, revised Jan 2013.

    Cited by:

    1. Liu, Chang & Sun, Xiaolei & Wang, Jun & Li, Jianping & Chen, Jianming, 2021. "Multiscale information transmission between commodity markets: An EMD-Based transfer entropy network," Research in International Business and Finance, Elsevier, vol. 55(C).
    2. Ohashi, Kazuhiko & Okimoto, Tatsuyoshi, 2016. "Increasing trends in the excess comovement of commodity prices," Journal of Commodity Markets, Elsevier, vol. 1(1), pages 48-64.
    3. Julien Chevallier & Florian Ielpo & Ling-Ni Boon, 2013. "Common risk factors in commodities," Economics Bulletin, AccessEcon, vol. 33(4), pages 2801-2816.
    4. Julien Chevallier & Benoît Sévi, 2013. "A Fear Index to Predict Oil Futures Returns," Working Papers 2013.62, Fondazione Eni Enrico Mattei.
    5. Fan, John Hua & Mo, Di & Zhang, Tingxi, 2022. "The “necessary evil” in Chinese commodity markets," Journal of Commodity Markets, Elsevier, vol. 25(C).
    6. Fernandez-Diaz, Jose M. & Morley, Bruce, 2019. "Interdependence among agricultural commodity markets, macroeconomic factors, crude oil and commodity index," Research in International Business and Finance, Elsevier, vol. 47(C), pages 174-194.
    7. Fretheim, Torun, 2019. "An empirical analysis of the correlation between large daily changes in grain and oil futures prices," Journal of Commodity Markets, Elsevier, vol. 14(C), pages 66-75.
    8. Rehman, Mobeen Ur & Vo, Xuan Vinh, 2021. "Energy commodities, precious metals and industrial metal markets: A nexus across different investment horizons and market conditions," Resources Policy, Elsevier, vol. 70(C).
    9. Dudda, Tom L. & Klein, Tony & Nguyen, Duc Khuong & Walther, Thomas, 2022. "Common Drivers of Commodity Futures?," QBS Working Paper Series 2022/05, Queen's University Belfast, Queen's Business School.
    10. Liu, Lu & Zhang, Xiang, 2019. "Financialization and commodity excess spillovers," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 195-216.
    11. Bohl, Martin T. & Irwin, Scott H. & Pütz, Alexander & Sulewski, Christoph, 2023. "The impact of financialization on the efficiency of commodity futures markets," Journal of Commodity Markets, Elsevier, vol. 31(C).
    12. Zelazny Jan, 2016. "Financialization and Commodity Market Stability," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 12(4), pages 33-42, December.
    13. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," MPRA Paper 96276, University Library of Munich, Germany.
    14. Adhikari, Ramesh & Putnam, Kyle J., 2020. "Comovement in the commodity futures markets: An analysis of the energy, grains, and livestock sectors," Journal of Commodity Markets, Elsevier, vol. 18(C).
    15. Degiannakis, Stavros & Filis, George, 2023. "Oil price assumptions for macroeconomic policy," Energy Economics, Elsevier, vol. 117(C).
    16. Mohammad Isleimeyyeh & Amine Ben Amar & Stéphane Goutte & Ramzi Benkraiem, 2022. "Commodity markets dynamics: What do cross-commodities over different nearest-to-maturities tell us?," Post-Print hal-03674806, HAL.
    17. Jan Żelazny, 2016. "Zmiany na rynkach towarowych a regulacje nadzorcze w Unii Europejskiej / Changes on Commodity Markets and Regulation in the European Union," International Economics, University of Lodz, Faculty of Economics and Sociology, issue 15, pages 199-210, September.
    18. Ben Amar, Amine & Goutte, Stéphane & Isleimeyyeh, Mohammad, 2022. "Asymmetric cyclical connectedness on the commodity markets: Further insights from bull and bear markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 386-400.
    19. Bohl, Martin T. & Pütz, Alexander & Sulewski, Christoph, 2021. "Speculation and the informational efficiency of commodity futures markets," Journal of Commodity Markets, Elsevier, vol. 23(C).
    20. Martin Bohl & Alexander Pütz & Christoph Sulewski, 2019. "Speculation and the Informational Efficiency of Commodity Futures Markets," CQE Working Papers 8919, Center for Quantitative Economics (CQE), University of Muenster.
    21. Hu, Min & Zhang, Dayong & Ji, Qiang & Wei, Lijian, 2020. "Macro factors and the realized volatility of commodities: A dynamic network analysis," Resources Policy, Elsevier, vol. 68(C).

  9. Benoît Sévi, 2013. "An empirical analysis of the downside risk-return trade-off at daily frequency," Post-Print hal-01500860, HAL.

    Cited by:

    1. Suzanne G. M. Fifield & David G. McMillan & Fiona J. McMillan, 2020. "Is there a risk and return relation?," The European Journal of Finance, Taylor & Francis Journals, vol. 26(11), pages 1075-1101, July.
    2. Gong, Xu & Wen, Fenghua & Xia, X.H. & Huang, Jianbai & Pan, Bin, 2017. "Investigating the risk-return trade-off for crude oil futures using high-frequency data," Applied Energy, Elsevier, vol. 196(C), pages 152-161.
    3. Vortelinos, Dimitrios I., 2016. "Incremental information of stock indicators," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 79-97.
    4. Xie, Nan & Wang, Zongrun & Chen, Sicen & Gong, Xu, 2019. "Forecasting downside risk in China’s stock market based on high-frequency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 530-541.
    5. Ayub, Usman & Shah, Syed Zulfiqar Ali & Abbas, Qaisar, 2015. "Robust analysis for downside risk in portfolio management for a volatile stock market," Economic Modelling, Elsevier, vol. 44(C), pages 86-96.
    6. Frazier, David T. & Liu, Xiaochun, 2016. "A new approach to risk-return trade-off dynamics via decomposition," Journal of Economic Dynamics and Control, Elsevier, vol. 62(C), pages 43-55.
    7. Ahmed, Walid M.A., 2020. "Is there a risk-return trade-off in cryptocurrency markets? The case of Bitcoin," Journal of Economics and Business, Elsevier, vol. 108(C).

  10. Julien, Chevallier & Sévi, Benoît, 2013. "A Fear Index to Predict Oil Futures Returns," Energy: Resources and Markets 156489, Fondazione Eni Enrico Mattei (FEEM).

    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. Sun, Hang & Bos, Jaap W.B. & Li, Zhuo, 2017. "In the Nick of Time: A Heteroskedastic SVAR Model and Its Application to the Crude Oil Futures Market," Research Memorandum 019, Maastricht University, Graduate School of Business and Economics (GSBE).
    4. Zied Ftiti & Aviral Tiwari & Amél Belanès & Khaled Guesmi, 2014. "Tests of Financial Market Contagion: Evolutionary Cospectral Analysis V.S. Wavelet Analysis," Working Papers 2014-577, Department of Research, Ipag Business School.
    5. Cortazar, Gonzalo & Ortega, Hector & Rojas, Maximiliano & Schwartz, Eduardo S., 2021. "Commodity index risk premium," Journal of Commodity Markets, Elsevier, vol. 22(C).
    6. José Renato Haas Ornelas & Roberto Baltieri Mauad, 2017. "Volatility Risk Premia and Future Commodity Returns," Working Papers Series 455, Central Bank of Brazil, Research Department.
    7. Lycheva, Maria & Mironenkov, Alexey & Kurbatskii, Alexey & Fantazzini, Dean, 2022. "Forecasting oil prices with penalized regressions, variance risk premia and Google data," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 68, pages 28-49.
    8. 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.
    9. Marinela Adriana Finta & José Renato Haas Ornelas, 2018. "Commodity Return Predictability: evidence from implied variance, skewness and their risk premia and their risk premia," Working Papers Series 479, Central Bank of Brazil, Research Department.
    10. Finta, Marinela Adriana & Ornelas, José Renato Haas, 2022. "Commodity return predictability: Evidence from implied variance, skewness, and their risk premia☆☆," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    11. 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. 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.

    Cited by:

    1. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting Realized Oil-Price Volatility: The Role of Financial Stress and Asymmetric Loss," Working Papers 201903, University of Pretoria, Department of Economics.
    2. 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.
    3. 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.
    4. Florian Ielpo & Benoît Sévi, 2014. "Forecasting the density of oil futures," Working Papers 2014-601, Department of Research, Ipag Business School.
    5. 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.
    6. 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).
    7. 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.
    8. 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.
    9. 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.
    10. Julien Chevallier & Benoît Sévi, 2013. "A Fear Index to Predict Oil Futures Returns," Working Papers 2013.62, Fondazione Eni Enrico Mattei.
    11. 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.
    12. Yves Rannou & Mohamed Amine Boutabba & Pascal Barneto, 2021. "Are Green Bond and Carbon Markets in Europe complements or substitutes? Insights from the activity of power firms," Post-Print hal-03435879, HAL.
    13. 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.
    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. 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.
    18. 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.
    19. 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.
    20. Fredj Jawadi & Wael Louhichi & Hachmi Ben Ameur & Abdoulkarim Idi Cheffou, 2017. "On Oil-US Exchange Rate Volatility Relationships: an Intradaily Analysis," Working Papers hal-04141662, HAL.
    21. 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.
    22. Fredj Jawadi & Waël Louhichi & Hachmi Ben Ameur & Abdoulkarim Idi Cheffou, 2017. "On Oil-US Exchange Rate Volatility Relationships: an Intradaily Analysis," EconomiX Working Papers 2017-11, University of Paris Nanterre, EconomiX.
    23. 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).
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. Todorova, Neda, 2017. "The asymmetric volatility in the gold market revisited," Economics Letters, Elsevier, vol. 150(C), pages 138-141.
    29. 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.
    30. 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).
    31. Fatih Çemrek & Hakkı Polat, 2014. "Modeling Natural Gas Prices Volatility," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 2(1), pages 1-12, June.
    32. 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.
    33. 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.

  12. Julien Chevallier & Yannick Le Pen & Benoît Sévi, 2011. "Options introduction and volatility in the EU ETS," Working Papers 1107, Chaire Economie du climat.

    Cited by:

    1. Panagiotis G. Papaioannou & George P. Papaioannou & Kostas Siettos & Akylas Stratigakos & Christos Dikaiakos, 2017. "Dynamic Conditional Correlation between Electricity and Stock markets during the Financial Crisis in Greece," Papers 1708.07063, arXiv.org.
    2. Federico Galán-Valdivieso & Elena Villar-Rubio & María-Dolores Huete-Morales, 2018. "The erratic behaviour of the EU ETS on the path towards consolidation and price stability," International Environmental Agreements: Politics, Law and Economics, Springer, vol. 18(5), pages 689-706, October.
    3. Zhao, Xin-gang & Jiang, Gui-wu & Nie, Dan & Chen, Hao, 2016. "How to improve the market efficiency of carbon trading: A perspective of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1229-1245.
    4. Xu, Li & Deng, Shi-Jie & Thomas, Valerie M., 2016. "Carbon emission permit price volatility reduction through financial options," Energy Economics, Elsevier, vol. 53(C), pages 248-260.
    5. Yinpeng Zhang & Zhixin Liu & Yingying Xu, 2018. "Carbon price volatility: The case of China," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-15, October.
    6. Eugenia Sanin, María & Violante, Francesco & Mansanet-Bataller, María, 2015. "Understanding volatility dynamics in the EU-ETS market," Energy Policy, Elsevier, vol. 82(C), pages 321-331.
    7. Julien Chevallier, 2013. "Carbon trading: past, present and future," Chapters, in: Roger Fouquet (ed.), Handbook on Energy and Climate Change, chapter 21, pages 471-489, Edward Elgar Publishing.
    8. Marc Gronwald & Janina Ketterer & Stefan Trück, 2011. "The Dependence Structure between Carbon Emission Allowances and Financial Markets - A Copula Analysis," CESifo Working Paper Series 3418, CESifo.
    9. Viteva, Svetlana & Veld-Merkoulova, Yulia V. & Campbell, Kevin, 2014. "The forecasting accuracy of implied volatility from ECX carbon options," Energy Economics, Elsevier, vol. 45(C), pages 475-484.
    10. Yan, Kai & Zhang, Wei & Shen, Dehua, 2020. "Stylized facts of the carbon emission market in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
    11. Compernolle, Tine & Kort, Peter M. & Thijssen, Jacco J.J., 2022. "The effectiveness of carbon pricing: The role of diversification in a firm’s investment decision," Energy Economics, Elsevier, vol. 112(C).
    12. Andrea Petrella & Sandro Sapio, 2010. "No PUN intended: A time series analysis of the Italian day-ahead electricity prices," RSCAS Working Papers 2010/03, European University Institute.
    13. Wen, Fenghua & Wu, Nan & Gong, Xu, 2020. "China's carbon emissions trading and stock returns," Energy Economics, Elsevier, vol. 86(C).
    14. Joao Leitao & Joaquim Ferreira & Ernesto Santibanez‐Gonzalez, 2021. "Green bonds, sustainable development and environmental policy in the European Union carbon market," Business Strategy and the Environment, Wiley Blackwell, vol. 30(4), pages 2077-2090, May.
    15. Janina Ketterer, 2012. "The Impact of Wind Power Generation on the Electricity Price in Germany," ifo Working Paper Series 143, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    16. Fagiani, Riccardo & Hakvoort, Rudi, 2014. "The role of regulatory uncertainty in certificate markets: A case study of the Swedish/Norwegian market," Energy Policy, Elsevier, vol. 65(C), pages 608-618.
    17. Jiemin Huang & Jiaoju Ge & Kai Chang & Yixiang Tian, 2020. "Dynamic hedging analysis of carbon emission trading yield in Shenzhen," Energy & Environment, , vol. 31(5), pages 870-885, August.
    18. Zhou, P. & Zhang, L. & Zhou, D.Q. & Xia, W.J., 2013. "Modeling economic performance of interprovincial CO2 emission reduction quota trading in China," Applied Energy, Elsevier, vol. 112(C), pages 1518-1528.
    19. Shuyi Wang & Zhenhua Wu & Baochen Yang, 2018. "Decision and Performance Analysis of a Price-Setting Manufacturer with Options under a Flexible-Cap Emission Trading Scheme (ETS)," Sustainability, MDPI, vol. 10(10), pages 1-22, October.
    20. Zhang, Yue-Jun & Peng, Yu-Lu & Ma, Chao-Qun & Shen, Bo, 2017. "Can environmental innovation facilitate carbon emissions reduction? Evidence from China," Energy Policy, Elsevier, vol. 100(C), pages 18-28.
    21. Chevallier, Julien, 2013. "Variance risk-premia in CO2 markets," Economic Modelling, Elsevier, vol. 31(C), pages 598-605.
    22. Yanxin Liu & Huajiao Li & Jianhe Guan & Xueyong Liu & Yajie Qi, 2019. "The role of the world’s major steel markets in price spillover networks: an analysis based on complex network motifs," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(4), pages 697-720, December.
    23. Tan, Xue-Ping & Wang, Xin-Yu, 2017. "Dependence changes between the carbon price and its fundamentals: A quantile regression approach," Applied Energy, Elsevier, vol. 190(C), pages 306-325.

  13. Chevallier, Julien & Benoit, Sevi, 2009. "On the Realized Volatility of the ECX CO2 Emissions 2008 Futures Contract: Distribution, Dynamics and Forecasting," Sustainable Development Papers 55834, Fondazione Eni Enrico Mattei (FEEM).

    Cited by:

    1. Julien Chevallier & Benoît Sévi, 2014. "On the Stochastic Properties of Carbon Futures Prices," Post-Print hal-01474249, HAL.
    2. Julien Chevallier, 2010. "Modelling the convenience yield in carbon prices using daily and realized measures," Working Papers halshs-00463921, HAL.
    3. Song, Yazhi & Liu, Tiansen & Liang, Dapeng & Li, Yin & Song, Xiaoqiu, 2019. "A Fuzzy Stochastic Model for Carbon Price Prediction Under the Effect of Demand-related Policy in China's Carbon Market," Ecological Economics, Elsevier, vol. 157(C), pages 253-265.
    4. Segnon, Mawuli & Lux, Thomas & Gupta, Rangan, 2017. "Modeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 692-704.
    5. Rittler, Daniel, 2012. "Price discovery and volatility spillovers in the European Union emissions trading scheme: A high-frequency analysis," Journal of Banking & Finance, Elsevier, vol. 36(3), pages 774-785.
    6. Wen, Fenghua & Gong, Xu & Cai, Shenghua, 2016. "Forecasting the volatility of crude oil futures using HAR-type models with structural breaks," Energy Economics, Elsevier, vol. 59(C), pages 400-413.
    7. Viteva, Svetlana & Veld-Merkoulova, Yulia V. & Campbell, Kevin, 2014. "The forecasting accuracy of implied volatility from ECX carbon options," Energy Economics, Elsevier, vol. 45(C), pages 475-484.
    8. Rittler, Daniel, 2009. "Price Discovery, Causality and Volatility Spillovers in European Union Allowances Phase II: A High Frequency Analysis," Working Papers 0492, University of Heidelberg, Department of Economics.
    9. Chevallier, Julien, 2011. "Evaluating the carbon-macroeconomy relationship: Evidence from threshold vector error-correction and Markov-switching VAR models," Economic Modelling, Elsevier, vol. 28(6), pages 2634-2656.
    10. Yves Rannou, 2017. "Liquidity, information, strategic trading in an electronic order book: New insights from the European carbon markets," Post-Print hal-01650533, HAL.
    11. Bredin, Don & Hyde, Stuart & Muckley, Cal, 2014. "A microstructure analysis of the carbon finance market," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 222-234.
    12. Oscar Carchano & Vicente Medina Martínez & Ángel Pardo Tornero, 2012. "Rolling over EUAs and CERs," Working Papers. Serie AD 2012-15, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    13. Reckling, Dennis, 2016. "Variance risk premia in CO2 markets: A political perspective," Energy Policy, Elsevier, vol. 94(C), pages 345-354.
    14. Chevallier, Julien, 2011. "A model of carbon price interactions with macroeconomic and energy dynamics," Energy Economics, Elsevier, vol. 33(6), pages 1295-1312.
    15. Shenghua Xiong & Chunfeng Wang & Zhenming Fang & Dan Ma, 2019. "Multi-Step-Ahead Carbon Price Forecasting Based on Variational Mode Decomposition and Fast Multi-Output Relevance Vector Regression Optimized by the Multi-Objective Whale Optimization Algorithm," Energies, MDPI, vol. 12(1), pages 1-21, January.
    16. Huang, Yumeng & Dai, Xingyu & Wang, Qunwei & Zhou, Dequn, 2021. "A hybrid model for carbon price forecastingusing GARCH and long short-term memory network," Applied Energy, Elsevier, vol. 285(C).
    17. Yue-Jun Zhang, 2016. "Research on carbon emission trading mechanisms: current status and future possibilities," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 39(1/2), pages 89-107.
    18. Didit Budi Nugroho & Takayuki Morimoto, 2019. "Incorporating Realized Quarticity into a Realized Stochastic Volatility Model," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 26(4), pages 495-528, December.
    19. Todorova, Neda, 2015. "The course of realized volatility in the LME non-ferrous metal market," Economic Modelling, Elsevier, vol. 51(C), pages 1-12.
    20. Chevallier, Julien, 2013. "Variance risk-premia in CO2 markets," Economic Modelling, Elsevier, vol. 31(C), pages 598-605.
    21. Chevallier, Julien, 2011. "Nonparametric modeling of carbon prices," Energy Economics, Elsevier, vol. 33(6), pages 1267-1282.
    22. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2022. "Forecasting European carbon returns using dimension reduction techniques: Commodity versus financial fundamentals," International Journal of Forecasting, Elsevier, vol. 38(3), pages 944-969.
    23. Zhu, Bangzhu & Han, Dong & Wang, Ping & Wu, Zhanchi & Zhang, Tao & Wei, Yi-Ming, 2017. "Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression," Applied Energy, Elsevier, vol. 191(C), pages 521-530.

  14. Yannick LE PEN & Benoît SEVI, 2008. "Volatility transmission and volatility impulse response functions in European electricity forward markets," Cahiers du CREDEN (CREDEN Working Papers) 08.09.77, CREDEN (Centre de Recherche en Economie et Droit de l'Energie), Faculty of Economics, University of Montpellier 1.

    Cited by:

    1. Hasan, Mudassar & Arif, Muhammad & Naeem, Muhammad Abubakr & Ngo, Quang-Thanh & Taghizadeh–Hesary, Farhad, 2021. "Time-frequency connectedness between Asian electricity sectors," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 208-224.
    2. Hung Do & Rabindra Nepal & Tooraj Jamasb, 2020. "Electricity market integration, decarbonisation and security of supply: Dynamic volatility connectedness in the Irish and Great Britain markets," CAMA Working Papers 2020-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Balcilar, Mehmet & Hammoudeh, Shawkat & Toparli, Elif Akay, 2018. "On the risk spillover across the oil market, stock market, and the oil related CDS sectors: A volatility impulse response approach," Energy Economics, Elsevier, vol. 74(C), pages 813-827.
    4. Peri, Massimo, 2015. "Cliamte Variability and Agricultural Price volatility: the case of corn and soybeans," 2015 Conference, August 9-14, 2015, Milan, Italy 212623, International Association of Agricultural Economists.
    5. Jin, Xiaoye, 2015. "Volatility transmission and volatility impulse response functions among the Greater China stock markets," Journal of Asian Economics, Elsevier, vol. 39(C), pages 43-58.
    6. Han, Lin & Kordzakhia, Nino & Trück, Stefan, 2020. "Volatility spillovers in Australian electricity markets," Energy Economics, Elsevier, vol. 90(C).
    7. David E. Allen & Michael McAleer & Robert Powell & Abhay K. Singh, 2017. "Volatility spillover and multivariate volatility impulse response analysis of GFC news events," Applied Economics, Taylor & Francis Journals, vol. 49(33), pages 3246-3262, July.
    8. Yan, Guan & Trück, Stefan, 2020. "A dynamic network analysis of spot electricity prices in the Australian national electricity market," Energy Economics, Elsevier, vol. 92(C).
    9. Green, Rikard & Larsson, Karl & Lunina, Veronika & Nilsson, Birger, 2018. "Cross-commodity news transmission and volatility spillovers in the German energy markets," Journal of Banking & Finance, Elsevier, vol. 95(C), pages 231-243.
    10. Christina E. Bannier, 2016. "Bewertungsmethoden in der Projektfinanzierung Erneuerbarer Energien [Valuation Methods for Renewable Energy Projects]," Schmalenbach Journal of Business Research, Springer, vol. 68(1), pages 75-110, April.
    11. Baldi, Lucia & Peri, Massimo & Vandone, Daniela, 2016. "Stock markets’ bubbles burst and volatility spillovers in agricultural commodity markets," Research in International Business and Finance, Elsevier, vol. 38(C), pages 277-285.
    12. Erdogdu, Erkan, 2016. "Asymmetric volatility in European day-ahead power markets: A comparative microeconomic analysis," Energy Economics, Elsevier, vol. 56(C), pages 398-409.
    13. de Menezes, Lilian M. & Houllier, Melanie A., 2015. "Germany's nuclear power plant closures and the integration of electricity markets in Europe," Energy Policy, Elsevier, vol. 85(C), pages 357-368.
    14. I-Chun Tsai & Shu-Hen Chiang, 2018. "Risk Transfer among Housing Markets in Major Cities in China," Sustainability, MDPI, vol. 10(7), pages 1-20, July.
    15. Jan Horst Keppler & Sébastien Phan & Yannick Le Pen & Charlotte Boureau, 2017. "The Impact of Intermittent Renewable Production and Market Coupling on the Convergence of French and German Electricity Prices," Working Papers hal-01599700, HAL.
    16. Daglish, Toby & de Bragança, Gabriel Godofredo Fiuza & Owen, Sally & Romano, Teresa, 2021. "Pricing effects of the electricity market reform in Brazil," Energy Economics, Elsevier, vol. 97(C).
    17. Lin Han & Ivor Cribben & Stefan Trueck, 2022. "Extremal Dependence in Australian Electricity Markets," Papers 2202.09970, arXiv.org.
    18. Massimo Peri, 2017. "Climate variability and the volatility of global maize and soybean prices," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 9(4), pages 673-683, August.
    19. David Gabauer, 2020. "Volatility impulse response analysis for DCC‐GARCH models: The role of volatility transmission mechanisms," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 788-796, August.
    20. Funke, Michael & Loermann, Julius & Tsang, Andrew, 2020. "Volatility transmission and volatility impulse response functions in the main and the satellite Renminbi exchange rate markets," BOFIT Discussion Papers 22/2020, Bank of Finland Institute for Emerging Economies (BOFIT).
    21. Hung Do & Rabindra Nepal & Russell Smyth, 2020. "Interconnectedness in the Australian national electricity market: A higher moment analysis," CAMA Working Papers 2020-49, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    22. Nikos Nomikos & Enrique Salvador, 2014. "The role of volatility regimes on volatility transmission patterns," Quantitative Finance, Taylor & Francis Journals, vol. 14(1), pages 1-13, January.
    23. Jin, Xiaoye & An, Ximeng, 2016. "Global financial crisis and emerging stock market contagion: A volatility impulse response function approach," Research in International Business and Finance, Elsevier, vol. 36(C), pages 179-195.
    24. Assefa, Tsion & Meuwissen, Miranda & Lansink, Alfons G.J.M., 2015. "Food scares and price volatility: the case of German and Spanish pig chains," 2015 Conference, August 9-14, 2015, Milan, Italy 210966, International Association of Agricultural Economists.
    25. Lilian de Menezes & Melanie A. Houllier, 2013. "Modelling Germany´s Energy Transition and its Potential Effect on European Electricity Spot Markets," EcoMod2013 5395, EcoMod.
    26. Lin, Wen-Yuan & Tsai, I-Chun, 2019. "Trader differences in Shanghai’s A-share and B-share markets: Effects on interaction with the Shanghai housing market," Journal of Asian Economics, Elsevier, vol. 64(C), pages 1-1.
    27. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2022. "Long-memory and volatility spillovers across petroleum futures," Energy, Elsevier, vol. 243(C).
    28. Manner, Hans & Türk, Dennis & Eichler, Michael, 2016. "Modeling and forecasting multivariate electricity price spikes," Energy Economics, Elsevier, vol. 60(C), pages 255-265.
    29. Ama Agyeiwaa Abrokwah, 2018. "Price and Volatility Spillovers in the Electricity Reliability Council of Texas Day-Ahead Electricity Market," International Journal of Energy Economics and Policy, Econjournals, vol. 8(6), pages 322-330.
    30. Lindström, Erik & Regland, Fredrik, 2012. "Modeling extreme dependence between European electricity markets," Energy Economics, Elsevier, vol. 34(4), pages 899-904.
    31. Baldi, Lucia & Peri, Massimo & Vandone, Daniela, 2016. "Financial Markets and Agricultural Commodities: Volatility Impulse Response Analysis," 2016 International European Forum (151st EAAE Seminar), February 15-19, 2016, Innsbruck-Igls, Austria 244461, International European Forum on System Dynamics and Innovation in Food Networks.
    32. Haugom, Erik & Westgaard, Sjur & Solibakke, Per Bjarte & Lien, Gudbrand, 2011. "Realized volatility and the influence of market measures on predictability: Analysis of Nord Pool forward electricity data," Energy Economics, Elsevier, vol. 33(6), pages 1206-1215.

  15. Yannick LE PEN & Benoît SEVI, 2008. "On the non-convergence of energy intensities: evidence from a pair-wise econometric approach," Cahiers du CREDEN (CREDEN Working Papers) 08.12.79, CREDEN (Centre de Recherche en Economie et Droit de l'Energie), Faculty of Economics, University of Montpellier 1.

    Cited by:

    1. Shi, Xunpeng & Yu, Jian & Cheong, Tsun Se, 2020. "Convergence and distribution dynamics of energy consumption among China's households," Energy Policy, Elsevier, vol. 142(C).
    2. Hao, Yu & Liao, Hua & Wei, Yi-Ming, 2015. "Is China’s carbon reduction target allocation reasonable? An analysis based on carbon intensity convergence," Applied Energy, Elsevier, vol. 142(C), pages 229-239.
    3. Payne, James E. & Vizek, Maruška & Lee, Junsoo, 2017. "Stochastic convergence in per capita fossil fuel consumption in U.S. states," Energy Economics, Elsevier, vol. 62(C), pages 382-395.
    4. De Cian, Enrica & Dasgupta, Shouro & Hof, Andries F. & van Sluisveld, Mariësse A.E. & Köhler, Jonathan & Pfluger, Benjamin & van Vuuren, Detlef P., 2017. "Actors, Decision-making, and Institutions in Quantitative System Modelling," MITP: Mitigation, Innovation and Transformation Pathways 263485, Fondazione Eni Enrico Mattei (FEEM).
    5. Mehmet Balcilar & Firat Emir, 2018. "The Dynamics of Energy Intensity Convergence in the EU-28 Countries," Working Papers 15-37, Eastern Mediterranean University, Department of Economics.
    6. Honma, Satoshi & Hu, Jin-Li, 2014. "Panel Data Parametric Frontier Technique for Measuring Total-factor Energy Efficiency: Application to Japanese Regions," MPRA Paper 54304, University Library of Munich, Germany.
    7. Michał Gostkowski & Tomasz Rokicki & Luiza Ochnio & Grzegorz Koszela & Kamil Wojtczuk & Marcin Ratajczak & Hubert Szczepaniuk & Piotr Bórawski & Aneta Bełdycka-Bórawska, 2021. "Clustering Analysis of Energy Consumption in the Countries of the Visegrad Group," Energies, MDPI, vol. 14(18), pages 1-25, September.
    8. Csereklyei, Zszsanna & Varas, Mar Rubio & Stern, David I., 2014. "Energy and Economic Growth: The Stylized Facts," Working Papers 249502, Australian National University, Centre for Climate Economics & Policy.
    9. Payne, James E. & Vizek, Maruška & Lee, Junsoo, 2017. "Is there convergence in per capita renewable energy consumption across U.S. States? Evidence from LM and RALS-LM unit root tests with breaks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 715-728.
    10. Stern, David I., 2012. "Modeling international trends in energy efficiency," Energy Economics, Elsevier, vol. 34(6), pages 2200-2208.
    11. Kounetas, Konstantinos Elias, 2018. "Energy consumption and CO2 emissions convergence in European Union member countries. A tonneau des Danaides?," Energy Economics, Elsevier, vol. 69(C), pages 111-127.
    12. Dayong Zhang and David C. Broadstock, 2016. "Club Convergence in the Energy Intensity of China," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    13. Nicholas Apergis & Christina Christou, 2016. "Energy productivity convergence: new evidence from club converging," Applied Economics Letters, Taylor & Francis Journals, vol. 23(2), pages 142-145, February.
    14. Balado-Naves, Roberto & Baños-Pino, José Francisco & Mayor, Matías, 2023. "Spatial spillovers and world energy intensity convergence," Energy Economics, Elsevier, vol. 124(C).
    15. Salman, Muhammad & Zha, Donglan & Wang, Guimei, 2022. "Assessment of energy poverty convergence: A global analysis," Energy, Elsevier, vol. 255(C).
    16. Zsuzsanna Csereklyei & David I. Stern, 2014. "Global Energy Use: Decoupling or Convergence?," CCEP Working Papers 1419, Centre for Climate & Energy Policy, Crawford School of Public Policy, The Australian National University.
    17. David I.Stern, 2010. "Modeling International Trends in Energy Efficiency and Carbon Emissions," Environmental Economics Research Hub Research Reports 1054, Environmental Economics Research Hub, Crawford School of Public Policy, The Australian National University.
    18. Tolón-Becerra, A. & Lastra-Bravo, X. & Botta, G.F., 2010. "Methodological proposal for territorial distribution of the percentage reduction in gross inland energy consumption according to the EU energy policy strategic goal," Energy Policy, Elsevier, vol. 38(11), pages 7093-7105, November.
    19. Akram, Vaseem & Rath, Badri Narayan & Sahoo, Pradipta Kumar, 2020. "Stochastic conditional convergence in per capita energy consumption in India," Economic Analysis and Policy, Elsevier, vol. 65(C), pages 224-240.
    20. Wu, Jianxin & Wu, Yanrui & Se Cheong, Tsun & Yu, Yanni, 2018. "Distribution dynamics of energy intensity in Chinese cities," Applied Energy, Elsevier, vol. 211(C), pages 875-889.
    21. Bollino, Carlo Andrea & Galeotti, Marzio, 2021. "On the Water-Energy-Food Nexus: Is there Multivariate Convergence?," FEEM Working Papers 309919, Fondazione Eni Enrico Mattei (FEEM).
    22. Liddle, Brantley, 2012. "OECD Energy Intensity: Measures, Trends, and Convergence," MPRA Paper 52085, University Library of Munich, Germany.
    23. Ivanovski, Kris & Awaworyi Churchill, Sefa & Smyth, Russell, 2018. "A club convergence analysis of per capita energy consumption across Australian regions and sectors," Energy Economics, Elsevier, vol. 76(C), pages 519-531.
    24. Stern, David I., 2010. "The Role of Energy in Economic Growth," Working Papers 249380, Australian National University, Centre for Climate Economics & Policy.
    25. González-Álvarez, María A. & Montañés, Antonio & Olmos, Lorena, 2020. "Towards a sustainable energy scenario? A worldwide analysis," Energy Economics, Elsevier, vol. 87(C).
    26. Liddle, Brantley, 2012. "Breaks and trends in OECD countries' energy–GDP ratios," Energy Policy, Elsevier, vol. 45(C), pages 502-509.
    27. Liu, Yang & Zhong, Sheng, 2021. "Cross-Economy Dynamics in Energy Productivity: Evidence from 47 Economies over the Period 2000–2015," ADBI Working Papers 1215, Asian Development Bank Institute.
    28. Lv, Yulan & Chen, Wei & Cheng, Jianquan, 2020. "Effects of urbanization on energy efficiency in China: New evidence from short run and long run efficiency models," Energy Policy, Elsevier, vol. 147(C).
    29. Shemelis Kebede Hundie & Megersa Debela Daksa, 2019. "Does energy-environmental Kuznets curve hold for Ethiopia? The relationship between energy intensity and economic growth," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 8(1), pages 1-21, December.
    30. Jiang, Lei & Folmer, Henk & Ji, Minhe & Zhou, P., 2018. "Revisiting cross-province energy intensity convergence in China: A spatial panel analysis," Energy Policy, Elsevier, vol. 121(C), pages 252-263.
    31. Fallahi, Firouz, 2017. "Stochastic convergence in per capita energy use in world," Energy Economics, Elsevier, vol. 65(C), pages 228-239.
    32. Parker, Steven & Liddle, Brantley, 2017. "Economy-wide and manufacturing energy productivity transition paths and club convergence for OECD and non-OECD countries," Energy Economics, Elsevier, vol. 62(C), pages 338-346.
    33. Ming Luo & Ruguo Fan & Yingqing Zhang, 2017. "A Study on China’s Urban Electricity Productivity Convergence with Spatial Smooth Transition Effect," Sustainability, MDPI, vol. 9(8), pages 1-18, August.
    34. Borozan, Djula, 2017. "Testing for convergence in electricity consumption across Croatian regions at the consumer's sectoral level," Energy Policy, Elsevier, vol. 102(C), pages 145-153.
    35. Satoshi Honma & Jin-Li Hu, 2011. "Industry-level Total-factor Energy Efficiency in Developed Countries," Discussion Papers 51, Kyushu Sangyo University, Faculty of Economics.
    36. Herrerias, M.J. & Aller, Carlos & Ordóñez, Javier, 2017. "Residential energy consumption: A convergence analysis across Chinese regions," Energy Economics, Elsevier, vol. 62(C), pages 371-381.
    37. Bhattacharya, Mita & Inekwe, John Nkwoma & Sadorsky, Perry & Saha, Anjan, 2018. "Convergence of energy productivity across Indian states and territories," Energy Economics, Elsevier, vol. 74(C), pages 427-440.
    38. Honma, Satoshi & Hu, Jin-Li, 2014. "Industry-level total-factor energy efficiency in developed countries: A Japan-centered analysis," Applied Energy, Elsevier, vol. 119(C), pages 67-78.
    39. Goto, Mika & Sueyoshi, Toshiyuki, 2023. "Sustainable development and convergence under energy sector transition in industrial nations: An application of DEA environmental assessment," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    40. Mohammadi, Hassan & Ram, Rati, 2012. "Cross-country convergence in energy and electricity consumption, 1971–2007," Energy Economics, Elsevier, vol. 34(6), pages 1882-1887.
    41. Cheong, Tsun Se & Li, Victor Jing & Shi, Xunpeng, 2019. "Regional disparity and convergence of electricity consumption in China: A distribution dynamics approach," China Economic Review, Elsevier, vol. 58(C).
    42. Deichmann, Uwe & Reuter, Anna & Vollmer, Sebastian & Zhang, Fan, 2019. "The relationship between energy intensity and economic growth: New evidence from a multi-country multi-sectorial dataset," World Development, Elsevier, vol. 124(C), pages 1-1.
    43. Santiago, Renato & Fuinhas, José Alberto & Marques, António Cardoso, 2020. "An analysis of the energy intensity of Latin American and Caribbean countries: Empirical evidence on the role of public and private capital stock," Energy, Elsevier, vol. 211(C).
    44. Le, Thai-Ha & Chang, Youngho & Park, Donghyun, 2017. "Energy demand convergence in APEC: An empirical analysis," Energy Economics, Elsevier, vol. 65(C), pages 32-41.
    45. Parker, Steven & Liddle, Brant, 2017. "Analysing energy productivity dynamics in the OECD manufacturing sector," Energy Economics, Elsevier, vol. 67(C), pages 91-97.
    46. Romero-Ávila, Diego & Omay, Tolga, 2022. "Convergence of per capita energy consumption around the world: New evidence from nonlinear panel unit root tests," Energy Economics, Elsevier, vol. 111(C).
    47. Liu, Tie-Ying & Lee, Chien-Chiang, 2020. "Convergence of the world’s energy use," Resource and Energy Economics, Elsevier, vol. 62(C).
    48. Bello, Mufutau Opeyemi & Ch'ng, Kean Siang, 2022. "Convergence in energy intensity of GDP: Evidence from West African countries," Energy, Elsevier, vol. 254(PA).
    49. Xia, X.H. & Huang, G.T. & Chen, G.Q. & Zhang, Bo & Chen, Z.M. & Yang, Q., 2011. "Energy security, efficiency and carbon emission of Chinese industry," Energy Policy, Elsevier, vol. 39(6), pages 3520-3528, June.
    50. Bashir, Muhammad Farhan & MA, Benjiang & Shahbaz, Muhammad & Shahzad, Umer & Vo, Xuan Vinh, 2021. "Unveiling the heterogeneous impacts of environmental taxes on energy consumption and energy intensity: Empirical evidence from OECD countries," Energy, Elsevier, vol. 226(C).
    51. Jianhuan Huang & Yantuan Yu & Chunbo Ma, 2018. "Energy Efficiency Convergence in China: Catch-Up, Lock-In and Regulatory Uniformity," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 70(1), pages 107-130, May.
    52. Wang, Chunhua, 2013. "Changing energy intensity of economies in the world and its decomposition," Energy Economics, Elsevier, vol. 40(C), pages 637-644.
    53. Hodžić, Sabina & Šikić, Tanja Fatur & Dogan, Eyup, 2023. "Green environment in the EU countries: The role of financial inclusion, natural resources and energy intensity," Resources Policy, Elsevier, vol. 82(C).
    54. Azad Haider & Wimal Rankaduwa & Farzana Shaheen & Sunila Jabeen, 2023. "The Nexus between GHGs Emissions and Clean Growth: Empirical Evidence from Canadian Provinces," Sustainability, MDPI, vol. 15(3), pages 1-19, January.

  16. Olivier ROUSSE & Benoît SEVI, 2006. "Banking behavior under uncertainty: Evidence from the US Sulfur Dioxide Emissions Allowance Trading Program," Cahiers du CREDEN (CREDEN Working Papers) 06.02.63, CREDEN (Centre de Recherche en Economie et Droit de l'Energie), Faculty of Economics, University of Montpellier 1.

    Cited by:

    1. Creti, Anna & Villeneuve, Bertrand, 2008. "Equilibrium Storage in a Markov Economy," MPRA Paper 11944, University Library of Munich, Germany.

Articles

  1. Derek Bunn, Julien Chevallier, Yannick Le Pen, and Benoit Sevi, 2017. "Fundamental and Financial Influences on the Co-movement of Oil and Gas Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    See citations under working paper version above.
  2. Duc Khuong Nguyen & Benoît Sévi & Bo Sjö & Gazi Salah Uddin, 2017. "The role of trade openness and investment in examining the energy-growth-pollution nexus: empirical evidence for China and India," Applied Economics, Taylor & Francis Journals, vol. 49(40), pages 4083-4098, August.
    See citations under working paper version above.
  3. 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.
    See citations under working paper version above.
  4. 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.
    See citations under working paper version above.
  5. Julien Chevallier & Benoît Sévi, 2014. "On the Stochastic Properties of Carbon Futures Prices," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 58(1), pages 127-153, May.
    See citations under working paper version above.
  6. Sévi, Benoît, 2013. "An empirical analysis of the downside risk-return trade-off at daily frequency," Economic Modelling, Elsevier, vol. 31(C), pages 189-197.
    See citations under working paper version above.
  7. Baena, César & Sévi, Benoît & Warrack, Allan, 2012. "Funds from non-renewable energy resources: Policy lessons from Alaska and Alberta," Energy Policy, Elsevier, vol. 51(C), pages 569-577.

    Cited by:

    1. Taguchi, Hiroyuki & Ganbayar, Javkhlan, 2022. "An econometric study on the classification and effectiveness of natural resource funds," MPRA Paper 114392, University Library of Munich, Germany.
    2. Ouoba, Youmanli, 2020. "Natural resources fund types and capital accumulation: A comparative analysis," Resources Policy, Elsevier, vol. 66(C).
    3. Hiroyuki Taguchi & Javkhlan Ganbayar, 2022. "Natural Resource Funds: Their Objectives and Effectiveness," Sustainability, MDPI, vol. 14(17), pages 1-20, September.
    4. Issaka Dialga & Youmanli Ouoba, 2022. "How do extractive resources affect human development ? Evidence from a panel data analysis," Post-Print hal-04467781, HAL.
    5. Ouoba, Youmanli, 2016. "Natural resources: Funds and economic performance of resource-rich countries," Resources Policy, Elsevier, vol. 50(C), pages 108-116.

  8. Fang, Yan & Ielpo, Florian & Sévi, Benoît, 2012. "Empirical bias in intraday volatility measures," Finance Research Letters, Elsevier, vol. 9(4), pages 231-237.

    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. Pankaj Agrrawal & Faye W. Gilbert & Jason Harkins, 2022. "Time Dependence of CAPM Betas on the Choice of Interval Frequency and Return Timeframes: Is There an Optimum?," JRFM, MDPI, vol. 15(11), pages 1-18, November.
    3. Aitor Ciarreta & Peru Muniainy & Ainhoa Zarraga, 2017. "Modelling Realized Volatility in Electricity Spot Prices: New insights and Application to the Japanese Electricity Market," ISER Discussion Paper 0991, Institute of Social and Economic Research, Osaka University.
    4. Omura, Akihiro & Li, Bin & Chung, Richard & Todorova, Neda, 2018. "Convenience yield, realised volatility and jumps: Evidence from non-ferrous metals," Economic Modelling, Elsevier, vol. 70(C), pages 496-510.

  9. Chevallier, Julien & Sévi, Benoît, 2012. "On the volatility–volume relationship in energy futures markets using intraday data," Energy Economics, Elsevier, vol. 34(6), pages 1896-1909.
    See citations under working paper version above.
  10. Benoît Sévi & César Baena, 2012. "A reassessment of the risk-return tradeoff at the daily horizon," Economics Bulletin, AccessEcon, vol. 32(1), pages 190-203.

    Cited by:

    1. Benoît Sévi & César Baena, 2013. "The explanatory power of signed jumps for the risk-return tradeoff," Economics Bulletin, AccessEcon, vol. 33(2), pages 1029-1046.

  11. Julien Chevallier & Benoît Sévi, 2011. "On the realized volatility of the ECX CO 2 emissions 2008 futures contract: distribution, dynamics and forecasting," Annals of Finance, Springer, vol. 7(1), pages 1-29, February.
    See citations under working paper version above.
  12. Yannick Le Pen & Benoît Sévi, 2011. "Macro factors in oil futures returns," International Economics, CEPII research center, issue 126-127, pages 13-38.

    Cited by:

    1. Julien Chevallier & Benoît Sévi, 2013. "A Fear Index to Predict Oil Futures Returns," Working Papers 2013.62, Fondazione Eni Enrico Mattei.
    2. Derek Bunn & Julien Chevallier & Yannick Le Pen & Benoît Sévi, 2017. "Fundamental and Financial Influences on the Co-movement of Oil and Gas prices," Post-Print hal-01619890, HAL.

  13. Chevallier, Julien & Le Pen, Yannick & Sévi, Benoît, 2011. "Options introduction and volatility in the EU ETS," Resource and Energy Economics, Elsevier, vol. 33(4), pages 855-880.
    See citations under working paper version above.
  14. Benoît Sévi & César Baena, 2011. "Brownian motion vs. pure-jump processes for individual stocks," Economics Bulletin, AccessEcon, vol. 31(4), pages 3138-3152.

    Cited by:

    1. Jan-Christian Gerlach & Jerome Kreuser & Didier Sornette, 2020. "Awareness of crash risk improves Kelly strategies in simulated financial time series," Papers 2004.09368, arXiv.org.

  15. Le Pen, Yannick & Sévi, Benoît, 2010. "Volatility transmission and volatility impulse response functions in European electricity forward markets," Energy Economics, Elsevier, vol. 32(4), pages 758-770, July.
    See citations under working paper version above.
  16. Le Pen, Yannick & Sévi, Benoît, 2010. "What trends in energy efficiencies? Evidence from a robust test," Energy Economics, Elsevier, vol. 32(3), pages 702-708, May.

    Cited by:

    1. Vo, Duc Hong & Vo, Long Hai & Ho, Chi Minh, 2022. "Regional convergence of nonrenewable energy consumption in Vietnam," Energy Policy, Elsevier, vol. 169(C).
    2. Liddle, Brantley, 2012. "Breaks and trends in OECD countries' energy–GDP ratios," Energy Policy, Elsevier, vol. 45(C), pages 502-509.

  17. Sévi, Benoît, 2010. "The newsvendor problem under multiplicative background risk," European Journal of Operational Research, Elsevier, vol. 200(3), pages 918-923, February.

    Cited by:

    1. M Denuit & L Eeckhoudt & O Jokung, 2013. "Non-differentiable transformations preserving stochastic dominance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(9), pages 1441-1446, September.
    2. Wu, Zhengping & Crama, Pascale & Zhu, Wanshan, 2012. "The newsvendor’s optimal incentive contracts for multiple advertisers," European Journal of Operational Research, Elsevier, vol. 220(1), pages 171-181.
    3. Colombo, Luca & Labrecciosa, Paola, 2012. "A note on pricing with risk aversion," European Journal of Operational Research, Elsevier, vol. 216(1), pages 252-254.
    4. Guo, Xu & Wong, Wing-Keung & Zhu, Lixing, 2013. "An analysis of portfolio selection with multiplicative background risk," MPRA Paper 51331, University Library of Munich, Germany.

  18. Le Pen, Yannick & Sévi, Benoît, 2010. "On the non-convergence of energy intensities: Evidence from a pair-wise econometric approach," Ecological Economics, Elsevier, vol. 69(3), pages 641-650, January.
    See citations under working paper version above.
  19. Benoît Sévi, 2006. "Ederington's ratio with production flexibility," Economics Bulletin, AccessEcon, vol. 7(1), pages 1-8.

    Cited by:

    1. Leif Beisland & Dennis Frestad, 2013. "How fair-value accounting can influence firm hedging," Review of Derivatives Research, Springer, vol. 16(2), pages 193-217, July.

  20. Benoît Sévi & Fabrice Yafil, 2005. "A special case of self-protection: The choice of a lawyer," Economics Bulletin, AccessEcon, vol. 4(6), pages 1-8.

    Cited by:

    1. Olivier Mesly & Hareesh Mavoori & Nicolas Huck, 2023. "The Role of Financial Spinning, Learning, and Predation in Market Failure," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 14(1), pages 517-543, March.
    2. Rongili Biswas & Carla Marchese & Fabio Privileggi, 2013. "Firm’s tax evasion in a principal-agent model with self-protection," Journal of Economics, Springer, vol. 110(2), pages 125-140, October.
    3. Biswas, Rongili & Marchese, Carla & Privileggi, Fabio, 2009. "Tax evasion in a principal-agent model with self-protection," POLIS Working Papers 138, Institute of Public Policy and Public Choice - POLIS.

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