基于高频数据HAR-CVX模型的沪深300指数的预测研究

doi:10.16381/j.cnki.issn1003-207x.2017.06.001

中国管理科学 ›› 2017, Vol. 25 ›› Issue (6): 1-10.doi: 10.16381/j.cnki.issn1003-207x.2017.06.001

• 论文 • 下一篇

基于高频数据HAR-CVX模型的沪深300指数的预测研究

刘晓倩¹, 王健², 吴广³

1. 上海外国语大学国际金融贸易学院, 上海 200083;
2. 山东理工大学理学院, 山东淄博 255049;
3. 上海财经大学统计与管理学院, 上海 200433

收稿日期:2016-06-30 修回日期:2017-01-19 出版日期:2017-06-20 发布日期:2017-08-26
通讯作者: 刘晓倩(1984-),女(汉族),山东淄博人,上海外国语大学国际金融贸易学院,讲师,研究方向:数量金融、风险管理和统计学,E-mail:xiaoqian7521@163.com. E-mail:xiaoqian7521@163.com.
基金资助:
国家自然科学基金委青年项目（71601123）；教育部人文社会科学青年基金项目（15YJC910004）；上海外国语大学青年基金项目（2015114051）

The Forecasting Analysis of HS300 Index based on HAR-CVX Model of High Frequency Data

LIU Xiao-qian¹, WANG Jian², WU Guang³

1. School of Economics and Finance, Shanghai International Studies University, Shanghai 200083, China;
2. School ofScience, Shandong University of Technology, Zibo 255049, China;
3. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China

Received:2016-06-30 Revised:2017-01-19 Online:2017-06-20 Published:2017-08-26

摘要/Abstract

摘要： 波动率可以衡量市场风险，对其准确预测在衍生品定价、风险管理和资产配置等方面有重要意义，是政府、资本市场及投资者共同关心的热点问题。本文在介绍了现有典型的各种波动率预测模型的基础上，将表示隐含波动率的市场波动率指数（CVX）作为影响因子引入高频数据HAR模型，构成HAR-CVX模型，该模型既利用了股票交易的高频数据，又利用了期权模拟交易的信息，最大程度地综合了可以利用的信息，所以预测效果更佳。以沪深 300 指数为研究对象，将几种常用波动率预测模型（GARCH模型、SV模型和HAR模型）与所提出的HAR-CVX模型进行滚动时间窗口样本外预测，并采用4种损失函数和SPA检验，对这几种模型的预测效果进行了评估，发现基于高频数据的HAR模型表现优于基于日收益率数据的GARCH模型和SV模型，并且加入了隐含波动率的HAR-CVX模型的预测效果更好。

关键词: 波动率预测, GARCH模型, SV模型, CVX, HAR-CVX模型

Abstract: Volatility is used to measure the market risk, and its accuracy prediction has an direct significance on derivative pricing, risk management and asset allocation. How to understand and measure the market risk accurately is related to investment decision efficiency and economic operation, which is the hot problem of the government department, investors and security market. Currently, the option is in simulated trading in China, and various options will soon list orderly. To research the volatility of correspondence stock and predict it accurately is very important to option pricing and option trading. The research has great theoretical and practical significance for regulation of the financial risk and will promote the long-term health development of Chinese options market. In this paper, detailed descriptions of the characteristics of different volatilities, the theoretical of various volatility forecasting models are given. The Realized Volatility, which is based on high-frequency data, is chosen as a representative of the future real volatility. The HS300 index is chosen as the research object, and then the preliminary analysis of the basic characteristics of volatility is carried on. Combined with the stock market implied volatility, the market volatility index-CVX is chosen as the impact factor and added to HAR model. Then are used a new model called HAR-CVX model is gotten. Then several common volatility forecasting models, such as GARCH model, SV model, HAR model and the HAR-CVX model, to forecast the volatility of HS300 index.The prediction method we used is called the out-of-sample rolling time window forecast method. Finally, four loss functions and SPA test are used to evaluate the prediction results. It is found that prediction effect of realized volatility models which is based on high frequency trading data is better than the SV model and GARCH model which are based on the daily price data. In addition, Considering the CVX index, which contains the implied volatility, improves the prediction effect of the HAR model.

Key words: volatility prediction, GARCH model, SV model, CVX, HAR-CVX model

中图分类号:

F830

刘晓倩, 王健, 吴广. 基于高频数据HAR-CVX模型的沪深300指数的预测研究[J]. 中国管理科学, 2017, 25(6): 1-10.

LIU Xiao-qian, WANG Jian, WU Guang. The Forecasting Analysis of HS300 Index based on HAR-CVX Model of High Frequency Data[J]. Chinese Journal of Management Science, 2017, 25(6): 1-10.

参考文献

[1] Fama E F. The behavior of stock-market prices[J]. Journal of Business, 1965, 38(1): 34-105.

[2] Mandelbrot B. The variation of some other speculative prices[J]. Journal of Business, 1967, 40(4): 393-393.

[3] Blattberg R C, Gonedes N J. A comparison of the stable and student distributions as statistical models for stock prices[J]. Journal of Business, 1974, 47(2): 244-280.

[4] Scott L O. Option pricing when the variance changes randomly: Theory, estimation, and an application[J]. Journal of Financial and Quantitative Analysis, 1987, 22(4): 419-438.

[5] Poterba J M, Summers L H. The persistence of volatility and stock market fluctuations[J]. The American Economic Keview, 1984, 76(5): 1142-1151.

[6] Stein J. Overreactions in the options market[J]. The Journal of Finance, 1989, 44(4): 1011-1023.

[7] Engle R F, Ng V K. Measuring and testing the impact of news on volatility[J]. Journal of Finance, 1993, 48(5): 1749-1778.

[8] Rubinstein M. Implied binomial trees[J]. The Journal of Finance, 1994, 49(3): 771-818.

[9] Eraker B, Johannes M, Polson N. The impact of jumps in volatility and returns[J]. Journal of Finance, 2003, 58(3): 1269-1300.

[10] Cont R. Volatility clustering in financial markets: Empirical facts and agent-based models[M]. Long memory in economics.Springer Berlin Heidelbery, 2007: 289-309.

[11] Buehler H. Volatility and dividends[J]. Preprint TU Berlin, 2010, 001-037.

[12] Andersen T G, Bollerslev T. Heterogeneous information arrivals and return volatility dynamics: Uncovering the long-run in high frequency returns[J]. The journal of Finance, 1997, 52(3): 975-1005.

[13] Beirne J, Caporale G M, Schulze-Ghattas M, et al. Volatility spillovers and contagion from mature to emerging stock markets[J]. Review of International Economics, 2009, 21(5): 1060-1075.

[14] McAleer M, Medeiros M C. A multiple regime smooth transition heterogeneous autoregressive model for long memory and asymmetries[J]. Journal of Econometrics, 2007, 147(1):104-119.

[15] Corsi F. A simple approximate long-memory model of realized volatility[J]. Journal of Financial Econometrics, 2009, 7(2): 174-196.

[16] Patton A J. Volatility forecast comparison using imperfect volatility proxies[J]. Journal of Econometrics, 2011, 160(1): 246-256.

[17] Hansen P R, Huang Zhuo, Shek H H. Realized GARCH: A joint model for returns and realized measures of volatility[J]. Journal of Applied Econometrics, 2012, 27(6): 877-906.

[18] Benavides G, Capistrán C. Forecasting exchange rate volatility: The superior performance of conditional combinations of time series and option implied forecasts[J]. Journal of Empirical Finance, 2012, 19(5): 627-639.

[19] 徐正国, 张世英. 调整“已实现”波动率与GARCH及SV模型对波动的预测能力的比较研究[J]. 系统工程, 2004, 22(8): 60-63.

[20] 李红权, 汪寿阳. 基于价格极差的金融波动率建模: 理论与实证分析[J]. 中国管理科学, 2009, 17(6): 1-8.

[21] 魏宇. 沪深300 股指期货的波动率预测模型研究[J]. 管理科学学报, 2010, 13(2): 66-76.

[22] 郑振龙, 黄薏舟. 波动率预测: GARCH模型与隐含波动率[J]. 数量经济技术经济研究, 2010, (1): 140-150.

[23] 刘志东, 薛莉. 金融市场高维波动率的扩展广义正交GARCH模型与参数估计方法研究[J]. 中国管理科学, 2010, 18(6): 33-41.

[24] 张小斐, 田金方. 异质金融市场驱动的已实现波动率计量模型[J]. 数量经济技术经济研究, 2011, 28(9): 140-153.

[25] 杨科, 田凤平, 林洪. 跳跃的估计、估计波动率的预测以及预测精度评价[J]. 中国管理科学, 2013, 21 (3): 50-60.

[26] 王天一, 赵晓军, 黄卓. 利用高频数据预测沪深300 指数波动率—基于REALIZED GARCH模型的实证研究[J]. 世界经济文汇, 2014, (5):17-30.

[27] 唐勇, 林欣. 考虑共同跳跃的波动建模—基于高频数据视角[J]. 中国管理科学, 2015, 23(8): 46-53.

[28] Engle R F. Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation[J]. Econometrica, 1982, 50(4): 987-1007.

[29] Bollerslev T. Generalized autoregressive conditional heteroskedasticity[J]. Journal of Econometrics, 1986, 31(3): 307-327.

[30] Black F, Scholes M. The pricing of options and corporate liabilities[J]. The Journal of Political Economy, 1973, 81 (3): 637-654.

基于高频数据HAR-CVX模型的沪深300指数的预测研究

The Forecasting Analysis of HS300 Index based on HAR-CVX Model of High Frequency Data

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