Volatility Risk Premium and Its Influencing Factors by Jump Diffusion Model ——Evidence from the Shanghai 50 ETF Options

doi:10.12005/orms.2019.0232

Operations Research and Management Science ›› 2019, Vol. 28 ›› Issue (10): 123-131.DOI: 10.12005/orms.2019.0232

• Aplication Research • Previous Articles Next Articles

Volatility Risk Premium and Its Influencing Factors by Jump Diffusion Model ——Evidence from the Shanghai 50 ETF Options

WANG Su-sheng, HU Ming-zhu, LI Zi-long

School of Economics and Management, Harbin Institute of Technology, Shenzhen 518055, China

Received:2018-09-03 Online:2019-10-25

跳扩散条件下波动率风险溢价及影响因素研究——基于上证50 ETF期权市场的实证

王苏生, 胡明柱, 李梓龙

哈尔滨工业大学深圳经济管理学院,广东深圳 518055

作者简介:王苏生(1969-),男, 湖北洪湖人,教授,博士生导师,博士,研究方向:金融工程;胡明柱(1991-),男,湖南邵阳人,博士研究生,研究方向:金融工程;李梓龙(1988-),男,广东客家人,博士研究生,研究方向:金融工程。
基金资助:
深圳市软科学项目(JCYJ20140417173156101);广东省哲学社会科学规划项目(GD18XYJ36)

Abstract

Abstract: This paper uses the daily frequency data of Shanghai 50 ETF and ETF options. We use SVCJ model, MCMC and Fourier transform method to extract volatility risk premium from P measure and Q measure. The research shows that the SVCJ model has better market fitting goodness than the SV model and the SVJ model, and the Fourier transform method can improve the estimation efficiency. In the period of market turbulence, volatility risk premium is negative generally, and investors are risk-averse, who have a higher willingness to buy options to hedge volatility risk. In non-violent turbulence period, the volatility risk premium is positive generally, and investors generally act risk seeking. Market returns, volatility, turnover rate, and investor sentiment have a significant impact on volatility risk premium.

Key words: Shanghai 50 ETF option, SVCJ, Markov Chain Monte Carlo, Fourier transform, volatility risk premium

摘要： 本文采用上证50 ETF及其期权交易数据,运用SVCJ模型、MCMC及傅里叶变换等方法,从P测度及Q测度中提取波动率风险溢价,并分析了其时变特征及影响因素。实证研究表明:SVCJ模型相较于SV模型及SVJ模型具有更好的市场拟合优度;傅里叶变换法能提高波动率风险溢价的估计效率;波动率风险溢价具有时变特征,在市场急剧动荡时期,波动率风险溢价基本为负,投资者厌恶波动风险,购买期权对冲波动风险的意愿较高;在市场非急剧动荡时期,波动率风险溢价基本为正,投资者偏好波动风险,购买期权对冲波动风险的意愿较低;市场收益率、波动率、换手率及投资者情绪对波动率风险溢价具有显著的影响。

关键词: 上证50 ETF期权, SVCJ模型, MCMC, 傅里叶变换, 波动率风险溢价

CLC Number:

F830.91

WANG Su-sheng, HU Ming-zhu, LI Zi-long. Volatility Risk Premium and Its Influencing Factors by Jump Diffusion Model ——Evidence from the Shanghai 50 ETF Options[J]. Operations Research and Management Science, 2019, 28(10): 123-131.

王苏生, 胡明柱, 李梓龙. 跳扩散条件下波动率风险溢价及影响因素研究——基于上证50 ETF期权市场的实证[J]. 运筹与管理, 2019, 28(10): 123-131.

References

[1] 王苏生,许桐桐,王俊博.上证50股指期货、ETF期权与ETF市场的价格发现能力对比分析[J].运筹与管理,2017(9):131-140.
[2] Chen Y, Shu J, Zhang J E. Investor sentiment, variance risk premium and delta-hedged gains[J]. Applied Economics, 2016, 48(31): 2952-2964.
[3] Della Corte P, Ramadorai T, Sarno L. Volatility risk premia and exchange rate predictability[J]. Journal of Financial Economics, 2016, 120(1): 21-40.
[4] Johnson T L. Risk premia and the vix term structure[J]. Journal of Financial and Quantitative Analysis, 2017, 52(6): 2461-2490.
[5] Londono J M, Zhou H. Variance risk premiums and the forward premium puzzle[J]. Journal of Financial Economics, 2017, 124(2): 415-440.
[6] 陈蓉,方昆明.波动率风险溢酬:时变特征及影响因素[J].系统工程理论与实践,2011,31(4):761-770.
[7] 王茵田,朱英姿.中国股票市场风险溢价研究[J].金融研究,2011(7):152-166.
[8] Dew-Becker I, Giglio S, Le A, et al. The price of variance risk[J]. Journal of Financial Economics, 2017, 123(1): 225-250.
[9] Feunou B, Jahan-Parvar M R, Okou C. Downside variance risk premium[J]. Journal of Financial Econometrics, 2017, 16(3): 341-383.
[10] Bollerslev T, Todorov V, Xu L. Tail risk premia and return predictability[J]. Journal of Financial Economics, 2015, 118(1): 113-134.
[11] Bekaert G, Hoerova M. The VIX, the variance premium and stock market volatility[J]. Journal of Econometrics, 2014, 183(2): 181-192.
[12] 方艳,张元玺,乔明哲.上证50ETF期权定价有效性的研究:基于B-S-M模型和蒙特卡罗模拟[J].运筹与管理,2017,26(8):157-166.
[13] Heston S L. A closed-form solution for options with stochastic volatility with applications to bond and currency options[J]. Review of Financial & Studies, 1993, 6(2): 327-343.
[14] Bates D S. Jumps and stochastic volatility: exchange rate processes implicit in deutsche mark options[J]. Review of Financial Studies, 1996, 9(1): 69-107.
[15] Duffie D, Pan J, Singleton K. Transform analysis and asset pricing for affine jump-diffusions[J]. Econometrica, 2000, 68(6): 1343-1376.
[16] Eraker B, Johannes M, Polson N. The impact of jumps in volatility and returns[J]. Journal of Finance, 2003, 58(3): 1269-1300.
[17] Broadie M, Chernov M, Johannes M. Model specification and risk premia: evidence from futures options[J]. Journal of Finance, 2007, 62(3): 1453-1490.
[18] Neumann M, Prokopczuk M, Simen C W. Jump and variance risk premia in the S&P 500[J]. Journal of Banking & Finance, 2016, 69(8): 72-83.
[19] Duan J C, Yeh C Y. Jump and volatility risk premiums implied by VIX[J]. Journal of Economic Dynamics and Control, 2010, 34(11): 2232-2244.
[20] 周海林,吴鑫育.基于VIX的波动率风险溢价估计[J].中国管理科学,2013,21(11):365-374.
[21] Xinyu W U, Zhou H. GARCH diffusiton model, iVIX, and volatility risk premium[J]. Economic Computation & Economic Cybernetics Studies & Research, 2016, 50(1): 327-342.
[22] Papantonis I. Volatility risk premium implications of GARCH option pricing models[J]. Economic Modelling, 2016, 58: 104-115.
[23] 吴鑫育,周海林,李心丹.波动率风险溢价:基于香港权证市场的实证[J].运筹与管理,2018,27(2):133-137.
[24] 郑振龙,郑国忠.高阶矩风险溢酬:信息含量及影响因素[J].数理统计与管理,2017,36(3):550-570.
[25] Bakshi G, Kapadia N. Delta-hedged gains and the negative market volatility risk premium[J]. Review of Financial Studies, 2003, 16(2): 527-566.
[26] Carr P, Wu L. Variance risk premiums[J]. Review of Financial Studies, 2009, 22(3): 1311-1341.
[27] Tim Bollerslev, Michael Gibson, Hao Zhou. Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities[J]. Journal of Econometrics, 2011, 160(1): 235-245.
[28] Larsson K, Nossman M . Jumps and stochastic volatility in oil prices: time series evidence[J]. Energy Economics, 2011, 33(3): 504-514.
[29] 乔柯南,乔晗.收益率,波动率与投资者风险偏好[J].系统工程理论与实践,2016,36(10):2489-2497.

Volatility Risk Premium and Its Influencing Factors by Jump Diffusion Model ——Evidence from the Shanghai 50 ETF Options

跳扩散条件下波动率风险溢价及影响因素研究——基于上证50 ETF期权市场的实证

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 2

Recommended Articles

Metrics

[1]	MA Zong-gang, ZHENG Jun, HUANG Jin-bo,YUAN Kun. An FFT Method for Pricing Catastrophe Bonds with the Longstaff Interest Model Based on Guangdong Province’s Data [J]. Operations Research and Management Science, 2018, 27(11): 147-156.
[2]	WANG Chun-fa, WANG Cui-ping. Option Pricing for a Jump Diffusion Model with Log-Uniform Jump-Amplitudes in a Regime-Switching Market Using FFT [J]. Operations Research and Management Science, 2011, 20(2): 152-159.