一种混合建模方法及其在ETF期权定价中的应用

doi:10.16381/j.cnki.issn1003-207x.2020.12.005

中国管理科学 ›› 2020, Vol. 28 ›› Issue (12): 44-53.doi: 10.16381/j.cnki.issn1003-207x.2020.12.005

一种混合建模方法及其在ETF期权定价中的应用

杨昌辉^1,2,3, 邵臻^1,2,3, 刘辰¹, 付超^1,2,3

1. 合肥工业大学管理学院, 安徽合肥 230009;
2. 过程优化与智能决策教育部重点实验室, 安徽合肥 230009;
3. 智能决策与信息系统技术教育部工程研究中心, 安徽合肥 230009

收稿日期:2020-08-08 修回日期:2020-09-13 出版日期:2020-12-20 发布日期:2021-01-11
通讯作者: 杨昌辉(1974-),女(汉族),安徽怀宁人,合肥工业大学管理学院,副教授,博士,研究方向:财务管理、金融建模与风险管理,E-mail:yangchanghui@hfut.edu.cn. E-mail:yangchanghui@hfut.edu.cn
基金资助:
国家自然科学基金资助项目（71771076，72071058，71601063，71690235）

A Hybrid Modeling Framework and Its Application for Exchange Traded Fund Options Pricing

YANG Chang-hui^1,2,3, SHAO Zhen^1,2,3, LIU Chen¹, FU Chao^1,2,3

1. School of Management, Hefei University of Technology, Hefei 230009, China;
2. Key Laboratory of Process Optimization and Intelligent Decision-making(Hefei University of Technology), Ministry of Education, Hefei 230009, China;
3. Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies, Hefei 230009, China

Received:2020-08-08 Revised:2020-09-13 Online:2020-12-20 Published:2021-01-11

摘要/Abstract

摘要： 科学合理的交易型开放式指数基金（ETF）期权定价有利于充分发挥其风险对冲功能，也是一个需要准确掌握市场规律并兼顾经济学意义的复杂建模过程。本文提出了一种新的混合建模方法，将嵌套长短时记忆神经网络模型（NLSTM）与Heston模型结合，实现ETF期权定价偏差的动态修正，并基于华夏上证50ETF、嘉实沪深300ETF和华泰柏瑞沪深300ETF的高频期权数据，实验验证了所提方法的有效性。研究结果表明，不同类型ETF期权价格的波动特征差异显著，无论是基于BS定价模型还是Heston定价模型都难以准确刻画ETF期权价格的复杂变化规律。通过将NLSTM神经网络模型与Heston模型结合，能够有效地捕捉不同类型ETF期权的动态变化规律，从而提升ETF期权定价的准确性。

关键词: 期权定价, ETF期权, 深度神经网络, 金融风险

Abstract: The scientific and reasonable exchange traded fund (ETF) options price contributes to implementing risk hedging function. This complex modeling process needs to consider the economic significance and accurately grasp the market rules. The issue of pricing ETF options is studied and a hybrid ETF options model is proposed. It combines the Nested-LSTM neural network model and the Heston model for the modeling, and dynamically corrects the option pricing deviation. The high-frequency data of ChinaAMC China 50 ETF, Harvest SZSE SME-CHINEXT 300 ETF and Huatai-PB CSI 300 ETF are taken as examples to verify the effectiveness of the proposed model. The experiment results show that the volatility characteristics of different types of ETF options prices are significantly different. Therefore, neither the Black-Scholes model nor the Heston model can be adapted to handle complex variation rules of ETF option prices accurately. By introducing Nested-LSTM neural network model into the Heston model, the proposed model can effectively capture the dynamic change rules of different types of ETF options, thus improving the estimation accuracy of ETF option prices effectively.

Key words: option pricing, ETF options, deep neural network, financial risk

中图分类号:

F830

杨昌辉, 邵臻, 刘辰, 付超. 一种混合建模方法及其在ETF期权定价中的应用[J]. 中国管理科学, 2020, 28(12): 44-53.

YANG Chang-hui, SHAO Zhen, LIU Chen, FU Chao. A Hybrid Modeling Framework and Its Application for Exchange Traded Fund Options Pricing[J]. Chinese Journal of Management Science, 2020, 28(12): 44-53.

参考文献

[1] Muravyev D, Pearson N D, Broussard J P. Is there price discovery in equity options?[J]. SSRN Electronic Journal, 2010.
[2] 陈忠阳, 赵阳. 衍生产品、风险对冲与公司价值——一个理论综述[J]. 管理世界, 2007,(11):139-149.
[3] Liang Chao, Tang Linchun, Li Yan, et al. Which sentiment index is more informative to forecast stock market volatility? Evidence from China[J]. International Review of Financial Analysis, 2020, 71:101552.
[4] Black F, Scholes M. The pricing of options and corporate liabilities[J]. Journal of Political Economy, 1973, 81(3):637-654.
[5] Wingert S, Mboya M P, Sibbertsen P. Distinguishing between breaks in the mean and breaks in persistence under long memory[J]. Economics Letters, 2020, 193:109338.
[6] 刘志东, 郑雪飞. 基于Hawkes因子模型的股价共同跳跃研究[J]. 中国管理科学, 2018, 26(7):18-31.
[7] Branger N, Rodrigues P, Schlag C. Level and slope of volatility smiles in long-run risk models[J]. Journal of Economic Dynamics and Control, 2018, 86:95-122.
[8] Bitto A, Frühwirth-Schnatter S. Achieving shrinkage in a time-varying parameter model framework[J]. Journal of Econometrics, 2019, 210(1):75-97.
[9] Golbabai A, Nikan O. A computational method based on the moving least-squares approach for pricing double barrier options in a time-fractional black-scholes model[J]. Computational Economics, 2020, 55(1):119-141.
[10] Araneda A A. The fractional and mixed-fractional CEV model[J]. Journal of Computational and Applied Mathematics, 2020, 363:106-123.
[11] Ghasemalipour S, Fathi-Vajargah B. Fuzzy simulation of European option pricing using mixed fractional Brownian motion[J]. Soft Computing, 2019, 23(24):13205-13213.
[12] Chen Homing, Hu Chengfeng. A relaxed cutting plane algorithm for solving the Vasicek-type forward interest rate model[J]. European Journal of Operational Research, 2010, 204(2):343-354.
[13] Merton R C. Option pricing when underlying stock returns are discontinuous[J]. Journal of Financial Economics, 1976, 3(1-2):125-144.
[14] Soleymani F, Saray B N. Pricing the financial Heston-Hull-White model with arbitrary correlation factors via an adaptive FDM[J]. Computers & Mathematics with Applications, 2019, 77(4):1107-1123.
[15] Joslin S. Can unspanned stochastic volatility models explain the cross section of bond volatilities?[J]. Management Science, 2018, 64(4):1707-1726.
[16] Recchioni M C, Sun Y. An explicitly solvable Heston model with stochastic interest rate[J]. European Journal of Operational Research, 2016, 249(1):359-377.
[17] Hagan P, Lesniewski A, Woodward D. Probability distribution in the SABR model of stochastic volatility[A]//Friz P K, Gatheral J, Gulisashvili A, et al. Large deviations and asymptotic methods in finance. Cham:Springer International Publishing, 2015:1-35.
[18] 李斌, 何万里. 一种寻找Heston期权定价模型参数的新方法[J]. 数量经济技术经济研究, 2015, 32(3):129-146.
[19] Audrino F, Fengler M R. Are classical option pricing models consistent with observed option second-order moments? Evidence from high-frequency data[J]. Journal of Banking & Finance, 2015, 61:46-63.
[20] Fan R, Taylor S J, Sandri M. Density forecast comparisons for stock prices, obtained from high-frequency returns and daily option prices[J]. Journal of Futures Markets, 2018, 38(1):83-103.
[21] Baum C F, Zerilli P, Chen L. Stochastic volatility, jumps and leverage in energy and stock markets:Evidence from high frequency data[J]. Energy Economics, 2019:104481.
[22] Chatrath A, Miao H, Ramchander S, et al. An examination of the flow characteristics of crude oil:Evidence from risk-neutral moments[J]. Energy Economics, 2016, 54:213-223.
[23] Muravyev D, Ni X (Charles). Why do option returns change sign from day to night?[J]. Journal of Financial Economics, 2020, 136(1):219-238.
[24] Remer R, Mahnke R. Application of Heston model and its solution to German DAX data[J]. Physica A:Statistical Mechanics and its Applications, 2004, 344(1-2):236-239.
[25] Daniel G, Joseph N L, Brée D S. Stochastic volatility and the goodness-of-fit of the Heston model[J]. Quantitative Finance, 2005, 5(2):199-211.
[26] Noh J, Kim T H. Forecasting volatility of futures market:The S&P 500 and FTSE 100 futures using high frequency returns and implied volatility[J]. Applied Economics, 2006, 38(4):395-413.
[27] Aït-Sahalia Y, Fan J, Li Y. The leverage effect puzzle:Disentangling sources of bias at high frequency[J]. Journal of Financial Economics, 2013, 109(1):224-249.
[28] Wang P. Pricing currency options with support vector regression and stochastic volatility model with jumps[J]. Expert Systems with Applications, 2011, 38(1):1-7.
[29] Leitao á, Grzelak L A, Oosterlee C W. On a one time-step Monte Carlo simulation approach of the SABR model:Application to European options[J]. Applied Mathematics and Computation, 2017, 293:461-479.
[30] Yao J, Li Y, Tan C L. Option price forecasting using neural networks[J]. Omega, 2000, 28(4):455-466.
[31] Niu Tong, Wang Jianzhou, Lu Haiyan, et al. Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting[J]. Expert Systems with Applications, 2020, 148:113237.
[32] Zhang Y, Yan B, Aasma M. A novel deep learning framework:Prediction and analysis of financial time series using CEEMD and LSTM[J]. Expert Systems with Applications, 2020, 159:113609.
[33] Andreou P C, Charalambous C, Martzoukos S H. Pricing and trading European options by combining artificial neural networks and parametric models with implied parameters[J]. European Journal of Operational Research, 2008, 185(3):1415-1433.
[34] 张丽娟, 张文勇. 基于Heston模型和遗传算法优化的混合神经网络期权定价研究[J]. 管理工程学报, 2018, 32(3):142-149.
[35] Huh J. Pricing options with exponential Lévy neural network[J]. Expert Systems with Applications, 2019, 127:128-140.
[36] 吴鑫育, 李心丹, 马超群. 考虑微观结构噪声的非仿射期权定价研究——基于上证50ETF期权高频数据的实证分析[J]. 中国管理科学, 2017, 25(12):99-108.
[37] Ahlip R, Rutkowski M. Pricing of foreign exchange options under the Heston stochastic volatility model and CIR interest rates[J]. Quantitative Finance, 2013, 13(6):955-966.
[38] Liu Jun, Pan Jun. Dynamic derivative strategies[J]. Journal of Financial Economics, 2003, 69(3):401-430.
[39] 李志生, 杜爽, 林秉旋. 卖空交易与股票价格稳定性——来自中国融资融券市场的自然实验[J]. 金融研究, 2015(6):173-188.
[40] 杨子晖, 陈雨恬, 张平淼. 重大突发公共事件下的宏观经济冲击、金融风险传导与治理应对[J]. 管理世界, 2020, 36(5):13-35+7.

一种混合建模方法及其在ETF期权定价中的应用

A Hybrid Modeling Framework and Its Application for Exchange Traded Fund Options Pricing

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 10

[1]	王献东, 何建敏. 模糊随机不确定环境下考虑决策者主观判断的亚式期权定价[J]. 中国管理科学, 2020, 28(9): 33-44.
[2]	吴鑫育, 赵凯, 李心丹, 马超群. 时变风险厌恶下的期权定价——基于上证50ETF期权的实证研究[J]. 中国管理科学, 2019, 27(11): 11-22.
[3]	吴鑫育, 李心丹, 马超群. 考虑微观结构噪声的非仿射期权定价研究——基于上证50ETF期权高频数据的实证分析[J]. 中国管理科学, 2017, 25(12): 99-108.
[4]	陈崇萍, 陈志祥. 网络销售中退货担保与定价决策[J]. 中国管理科学, 2016, 24(6): 52-60.
[5]	黄守军, 杨俊, 陈其安. 基于B-S期权定价模型的V2G备用合约协调机制研究[J]. 中国管理科学, 2016, 24(10): 10-21.
[6]	林敏华. 信贷资产证券化、异质性投资者和金融风险[J]. 中国管理科学, 2015, 23(6): 25-31.
[7]	赵攀, 肖庆宪. 基于Tsallis分布及跳扩散过程的欧式期权定价[J]. 中国管理科学, 2015, 23(6): 41-48.
[8]	陈守东, 王妍. 我国金融机构的系统性金融风险评估——基于极端分位数回归技术的风险度量[J]. 中国管理科学, 2014, 22(7): 10-17.
[9]	吴鑫育, 杨文昱, 马超群, 汪寿阳. 基于非仿射随机波动率模型的期权定价研究[J]. 中国管理科学, 2013, (1): 1-7.
[10]	韩立岩, 叶浩, 李伟. 股指期权定价的非参数数值方法研究[J]. 中国管理科学, 2012, (1): 23-29.
[11]	郑红, 游春. 补充医疗保险的障碍期权定价方法及其应用[J]. 中国管理科学, 2011, 19(6): 169-176.
[12]	张鸿彦. 基于人工智能的隐含波动率的敏感度的研究[J]. 中国管理科学, 2008, 16(3): 125-130.
[13]	马蒙蒙, 蔡晨, 王兆祥. 基于二叉树期权定价模型的企业R&D项目价值评估研究[J]. 中国管理科学, 2004, (3): 22-27.
[14]	吴云, 何建敏. 上升敲出期权定价模型的求解方法研究[J]. 中国管理科学, 2002, (6): 23-26.
[15]	郑长德. 论经理人股票期权激励的有效性[J]. 中国管理科学, 2001, (5): 74-80.