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Investigating the risk-return trade-off for crude oil futures using high-frequency data

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  • Gong, Xu
  • Wen, Fenghua
  • Xia, X.H.
  • Huang, Jianbai
  • Pan, Bin

Abstract

This paper comprehensively examines the existence and significance of a contemporaneous/intertemporal risk-return trade-off for crude oil futures using high-frequency transaction data. The results reveal that the contemporaneous relation between risk (volatility risk, downside risk or jump risk) and return in the crude oil futures market is negative and statistically significant and that the contemporaneous negative relation between downside risk and return is stronger than the two others. However, the intertemporal volatility/jump risk-return relationship is insignificant, and there isweak negative correlation between downside risk and expected return in the crude oil futures market. These findings can be explained by the asymmetric effect of risk on returns. The findings are robust across different samples and different measures of volatility, downside and jump risks. Thus, there is no risk-return trade-off in the crude oil futures market.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:196:y:2017:i:c:p:152-161
    DOI: 10.1016/j.apenergy.2016.11.112
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    as
    1. Todorova, Neda & Worthington, Andrew & Souček, Michael, 2014. "Realized volatility spillovers in the non-ferrous metal futures market," Resources Policy, Elsevier, vol. 39(C), pages 21-31.
    2. Lucas, Andre, 2000. "A Note on Optimal Estimation from a Risk-Management Perspective under Possibly Misspecified Tail Behavior," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(1), pages 31-39, January.
    3. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    4. Kristoufek, Ladislav, 2014. "Leverage effect in energy futures," Energy Economics, Elsevier, vol. 45(C), pages 1-9.
    5. Ji, Qiang & Guo, Jian-Feng, 2015. "Oil price volatility and oil-related events: An Internet concern study perspective," Applied Energy, Elsevier, vol. 137(C), pages 256-264.
    6. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    7. Haugom, Erik & Langeland, Henrik & Molnár, Peter & Westgaard, Sjur, 2014. "Forecasting volatility of the U.S. oil market," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 1-14.
    8. 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.
    9. Pettinau, Alberto & Ferrara, Francesca & Amorino, Carlo, 2012. "Techno-economic comparison between different technologies for a CCS power generation plant integrated with a sub-bituminous coal mine in Italy," Applied Energy, Elsevier, vol. 99(C), pages 32-39.
    10. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.
    11. Feng, Zhen-Hua & Wei, Yi-Ming & Wang, Kai, 2012. "Estimating risk for the carbon market via extreme value theory: An empirical analysis of the EU ETS," Applied Energy, Elsevier, vol. 99(C), pages 97-108.
    12. Yu, Jun, 2012. "A semiparametric stochastic volatility model," Journal of Econometrics, Elsevier, vol. 167(2), pages 473-482.
    13. Andersen, Torben G. & Bollerslev, Tim & Huang, Xin, 2011. "A reduced form framework for modeling volatility of speculative prices based on realized variation measures," Journal of Econometrics, Elsevier, vol. 160(1), pages 176-189, January.
    14. 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.
    15. Cifarelli, Giulio & Paladino, Giovanna, 2010. "Oil price dynamics and speculation: A multivariate financial approach," Energy Economics, Elsevier, vol. 32(2), pages 363-372, March.
    16. Efimova, Olga & Serletis, Apostolos, 2014. "Energy markets volatility modelling using GARCH," Energy Economics, Elsevier, vol. 43(C), pages 264-273.
    17. Li, Ziran & Sun, Jiajing & Wang, Shouyang, 2013. "An information diffusion-based model of oil futures price," Energy Economics, Elsevier, vol. 36(C), pages 518-525.
    18. Xin Huang & George Tauchen, 2005. "The Relative Contribution of Jumps to Total Price Variance," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(4), pages 456-499.
    19. Chatrath, Arjun & Miao, Hong & Ramchander, Sanjay & Wang, Tianyang, 2016. "An examination of the flow characteristics of crude oil: Evidence from risk-neutral moments," Energy Economics, Elsevier, vol. 54(C), pages 213-223.
    20. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 7(2), pages 174-196, Spring.
    21. Souček, Michael & Todorova, Neda, 2013. "Realized volatility transmission between crude oil and equity futures markets: A multivariate HAR approach," Energy Economics, Elsevier, vol. 40(C), pages 586-597.
    22. Bali, Turan G. & Demirtas, K. Ozgur & Levy, Haim, 2009. "Is There an Intertemporal Relation between Downside Risk and Expected Returns?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(4), pages 883-909, August.
    23. Broda, Simon A. & Haas, Markus & Krause, Jochen & Paolella, Marc S. & Steude, Sven C., 2013. "Stable mixture GARCH models," Journal of Econometrics, Elsevier, vol. 172(2), pages 292-306.
    24. Conlon, Thomas & Cotter, John, 2013. "Downside risk and the energy hedger's horizon," Energy Economics, Elsevier, vol. 36(C), pages 371-379.
    25. Shakouri, Mahmoud & Lee, Hyun Woo & Choi, Kunhee, 2015. "PACPIM: New decision-support model of optimized portfolio analysis for community-based photovoltaic investment," Applied Energy, Elsevier, vol. 156(C), pages 607-617.
    26. Zhang, Xibin & King, Maxwell L., 2008. "Box-Cox stochastic volatility models with heavy-tails and correlated errors," Journal of Empirical Finance, Elsevier, vol. 15(3), pages 549-566, June.
    27. Shen, Zhiwei & Ritter, Matthias, 2016. "Forecasting volatility of wind power production," Applied Energy, Elsevier, vol. 176(C), pages 295-308.
    28. Cotter, John & Hanly, Jim, 2010. "Time-varying risk aversion: An application to energy hedging," Energy Economics, Elsevier, vol. 32(2), pages 432-441, March.
    29. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    30. Galsband, Victoria, 2012. "Downside risk of international stock returns," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2379-2388.
    31. Carl Chiarella & Boda Kang & Christina Sklibosios Nikitopoulos & Thuy‐Duong Tô, 2016. "The Return–Volatility Relation in Commodity Futures Markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(2), pages 127-152, February.
    32. Benjamin Yibin Zhang & Hao Zhou & Haibin Zhu, 2009. "Explaining Credit Default Swap Spreads with the Equity Volatility and Jump Risks of Individual Firms," Review of Financial Studies, Society for Financial Studies, vol. 22(12), pages 5099-5131, December.
    33. Contreras, Javier & Rodríguez, Yeny E., 2014. "GARCH-based put option valuation to maximize benefit of wind investors," Applied Energy, Elsevier, vol. 136(C), pages 259-268.
    34. Wang, Lijun & An, Haizhong & Liu, Xiaojia & Huang, Xuan, 2016. "Selecting dynamic moving average trading rules in the crude oil futures market using a genetic approach," Applied Energy, Elsevier, vol. 162(C), pages 1608-1618.
    35. Kolos, Sergey P. & Ronn, Ehud I., 2008. "Estimating the commodity market price of risk for energy prices," Energy Economics, Elsevier, vol. 30(2), pages 621-641, March.
    36. Gorbacheva, Natalya V. & Sovacool, Benjamin K., 2015. "Pain without gain? Reviewing the risks and rewards of investing in Russian coal-fired electricity," Applied Energy, Elsevier, vol. 154(C), pages 970-986.
    37. Bollerslev, Tim & Osterrieder, Daniela & Sizova, Natalia & Tauchen, George, 2013. "Risk and return: Long-run relations, fractional cointegration, and return predictability," Journal of Financial Economics, Elsevier, vol. 108(2), pages 409-424.
    38. William J. Baumol, 1963. "An Expected Gain-Confidence Limit Criterion for Portfolio Selection," Management Science, INFORMS, vol. 10(1), pages 174-182, October.
    39. 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.
    40. Reboredo, Juan C. & Rivera-Castro, Miguel A. & Ugolini, Andrea, 2016. "Downside and upside risk spillovers between exchange rates and stock prices," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 76-96.
    41. Adam E. Clements & Neda Todorova, 2016. "Information Flow, Trading Activity and Commodity Futures Volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(1), pages 88-104, January.
    42. Price, Kelly & Price, Barbara & Nantell, Timothy J, 1982. "Variance and Lower Partial Moment Measures of Systematic Risk: Some Analytical and Empirical Results," Journal of Finance, American Finance Association, vol. 37(3), pages 843-855, June.
    43. Tauchen, George & Zhou, Hao, 2011. "Realized jumps on financial markets and predicting credit spreads," Journal of Econometrics, Elsevier, vol. 160(1), pages 102-118, January.
    44. Yi Hu & Dongmei Guo & Mingxi Wang & Xi Zhang & Shouyang Wang, 2015. "The Relationship between Energy Consumption and Economic Growth: Evidence from China’s Industrial Sectors," Energies, MDPI, Open Access Journal, vol. 8(9), pages 1-15, August.
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