中国铜期货市场波动率估计与风险度量——基于广义已实现测度的Realized HAR GARCH模型

doi:10.16381/j.cnki.issn1003-207x.2020.0763

Abstract

Abstract: As China's copper futures market continues to expand, accurate estimation of the volatility of copper futures prices is of particular interest to academics.In this paper, using the high-frequency data samples of copper futures in Shanghai Futures Exchange, the Realized GARCH model and the Realized HAR GARCH model involving different types of realized measures are established under the skew-t distribution to forecast the volatility of returns and the daily VaR.Besides using the conventional realized measures as our benchmarks, the generalized realized measures are introduced into the models. Then, through the in-sample fitting and out-of-sample rolling prediction, likelihood function, VaR posterior tests, and loss function MCS test are applied to compare the results of the models on volatility estimation and evaluate the forecasting effects of the VaR.

Key words: copper futures market; Realized HAR GARCH; volatility; realized measure; VaR

CLC Number:

F830

CAI Guang-hui,XIANG Lin. The Volatility Estimation and VaR Measurement of China’s Copper Future Market: Based on Realized HAR GARCH Model Incorporating Generalized Realized Measures[J]. Chinese Journal of Management Science, 2021, 29(11): 1-12.

References

［1］ Andersen T G, Bollerslev T. Answering the skeptics: Yes, standard volatility models do provide accurate forecasts［J］. International Economic Review, 1998, 39(4): 885-905.
［2］ Engle R F. New frontiers for Arch models［J］. Journal of Applied Econometrics, 2002, 17(5): 425-446.
［3］ 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.
［4］王天一, 黄卓. 高频数据波动率建模—基于厚尾分布的Realized GARCH模型［J］. 数量经济技术经济研究, 2012, 29(5): 149-161.Wang Tianyi, Huang Zhuo. High frequency volatility modeling［J］. The Journal of Quantitative & Technical Economics, 2012, 29(5): 149-161.
［5］于孝建, 王秀花. 基于混频已实现GARCH模型的波动预测与VaR度量［J］. 统计研究, 2018, 35(1): 104-116.Yu Xiaojian, Wang Xiuhua. Mix frequency Realized GARCH models: The forecast of volatility and measure of VaR［J］. Statistical Research, 2018, 35(1): 104-116.
［6］ Muller U A, Dacorogna M M, Dave R D, et al. Volatilities of different time resolutions-Analyzing the dynamics of market components［J］. Journal of Empirical Finance, 1997, 4(2): 213-239.
［7］ Bollerslev T, Mikkelsen, H O. Modeling and pricing long memory in stock market volatility［J］. Journal of Econometrics, 1996, 73(1): 151-184.
［8］ Corsi F. A simple approximate long-memory model of realized volatility［J］. Journal of Financial Economics, 2009, 7(2): 174-196.
［9］朱学红, 邹佳纹, 韩飞燕, 等. 引入外部冲击的中国铜期货市场高频波动率建模与预测［J］. 中国管理科学, 2018, 26(9): 52-61.Zhu Xuehong, Zou Jiawen, Han Feiyan, etal. Forecasting and modeling of China’s nonferrous metal futures market volatility based on the introduction of external shocks: Taking copper as example［J］. Chinese Journal of Management Science, 2018, 26(9): 52-61.
［10］ Huang Zhuo, Liu Hao, Wang Tianyi. Modeling long memory volatility using realized measures of volatility: A realized HAR GARCH model［J］. Economic Modelling, 2016, 52: 812-821.
［11］王天一, 刘浩, 黄卓. 基于混频数据抽样的已实现波动率长记忆模型［J］. 系统工程学报, 2018, 33(6): 812-822.Wang Tianyi, Liu Hao, Huang Zhuo. Model for the long memory of realized volatility based on mixed data sampling［J］. Journal of Systems Engineering, 2018, 33(6): 812-822.
［12］秦学志, 林先伟, 王文华. 基于长记忆性特征的欧式期权模糊定价研究［J］. 系统工程理论与实践, 2019, 39(12): 3073-3083.Qin Xuezhi, Lin Xianwei, Wang Wenhua. Fuzzy pricing of European option based on the long-term memory property［J］. Systems Engineering-Theory & Practice, 2019, 39(12): 3073-3083.
［13］ Louzis D P, Xanthopoulos-Sisinis S, Refenes A P. The role of high frequency intra-daily data, daily range and implied volatility in multi-period value-at-risk forecasting［J］. Journal of Forecasting, 2013, 32(6): 561-576.
［14］黄友珀, 唐振鹏, 周熙雯. 基于偏t分布realized GARCH模型的尾部风险估计［J］. 系统工程理论与实践, 2015, 35(9): 2200-2208.Huang Youpo, Tang Zhenpeng, Zhou Xiwen. Estimation of tail risk based on realized GARCH model with skew-t distribution［J］. Systems Engineering-Theory & Practice, 2015, 35(9): 2200-2208.
［15］蒋伟, 顾研. 基于广义已实现测度的Realized GARCH模型改进及应用［J］. 数量经济技术经济研究, 2019, 36(7): 156-173.Jiang Wei, Gu Yan. Forecasting VaR: Realized GARCH incorporating generalized realized measures［J］. The Journal of Quantitative & Technical Economics, 2019, 36(7): 156-173.
［16］ Barndorff-Nielsen O E, Shephard N. Estimating quadratic variation using realized variance［J］. Journal of Applied Econometrics, 2002, 17(5): 457-477.
［17］ Barndorff-Nielsen O E, Shephard N. Power and bipowervariation with stochastic volatility and jumps［J］. Journal of Financial Economics, 2004, 2(1): 1-37.
［18］ Bandi F M, Russell J R. Microstructure noise, realized volatility, and optimal sampling［J］. Review of Economic Studies, 2008, 75(2): 339-369.
［19］ Andersen T G, Dobrev D, Schaumburg E. Jump-robust volatility estimation using nearest neighbor truncation［J］. Journal of Econometrics, 2012, 169(1): 75-93.
［20］ Jorion P. Measuring the risk in value at risk［J］. Financial Analysts Journal, 1996, 6(1): 47-56.
［21］ Acerbi C, Tasche D. On the coherence of expected shortfall［J］. Journal of Banking and Finance, 2002, 26(7): 1487-1503.
［22］ Kupiec P H. Techniques for verifying the accuracy of risk measurement models［J］. Journal of Derivatives, 1995, 3(2): 73-84.
［23］ Christoffersen P F. Evaluating interval forecasts［J］. International Economic Journal, 1998, 39(4): 841-862.
［24］ González-Rivera G, Lee T H, Mishra S. Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood［J］. International Journal of Forecasting, 2004, 20(4): 629-645.
［25］ Sarma M, Thomas S, Shah A. Selection of value-at-risk models［J］. Journal of Forecasting, 2003, 22(4): 337-358.
［26］ Hansen P R, Lunde A. A forecast comparison of volatility models: Does anything beat a GARCH(1,1)?［J］. Journal of Applied Econometrics, 2005, 20(7): 873-889.
［27］ Hansen P R, Lunde A, Nason J M. The model confidence set［J］. Econometrica, 2011, 79(2): 453-497.
［28］陈晓东. 中国金属期货市场高频波动率预测模型比较研究［J］. 管理工程学报, 2016, 30(3): 114-120.Chen Xiaodong. Comparative study on prediction model of high-frequency fluctuation of China's futures market［J］. Journal of Industrial Engineering and Engineering Management, 2016, 30(3): 114-120.
［29］ Hurst H E. Long-term storage capacity of reservoirs［J］. American Society of Civil Engineers, 1951, 116: 770-799.
［30］ Peters E E. Chaos and order in the capital markets: A new view of cycles, prices, and market volatility［M］. New York: John Wiley and Sons Inc, 1991.
［31］郑丰, 崔积钰, 马志伟. 沪铜期货市场长记忆特征的R/S分析［J］. 辽宁大学学报(自然科学版), 2013, 40(1): 14-20.Zheng Feng, Cui Jiyu. Ma Zhiwei. R/S analysis on long-term memory characteristics of copper future market in SHFE［J］. Journal of Liaoning University (Natural Science Edition), 2013, 40(1): 14-20.

[1]	Pengfei Luo, Yanming Yao, Yingxian Tan. The Principal-agent Problem and Financing Policies under the Public Health Emergencies [J]. Chinese Journal of Management Science, 2025, 33(10): 47-56.
[2]	Yanyi Ye, Xiaoguang Yang. Informed Trading in Stock Market and Credit Spreads in Bond Market [J]. Chinese Journal of Management Science, 2025, 33(9): 1-10.
[3]	Liu Gan, Yingli Cai, Mingyu Xu, Yingxian Tan. Information Acquisition in Secondary Market and Convertible Bond Financing [J]. Chinese Journal of Management Science, 2025, 33(9): 22-32.
[4]	Yinhong Yao, Xiaoxu Wang, Wei Chen, Zhensong Chen. Extreme Risk Spillover among Global Stock Markets Based on Transformer-LSTM Quantile Regression [J]. Chinese Journal of Management Science, 2025, 33(8): 1-13.
[5]	Weiping Wu, Yu Lin, Chengneng Jin, Zhenpeng Tang. Constrained Optimal Risk Sensitive Execution Problem with Stochastic Market Depth [J]. Chinese Journal of Management Science, 2025, 33(8): 14-25.
[6]	Jingzhou Yan, Zhongfei Li, Jie Mao, Xingyi Li. Strategic Trading and Market Quality under Ambiguous Volatility [J]. Chinese Journal of Management Science, 2025, 33(8): 26-36.
[7]	Wei Zhang, Zhuo Chen, Xu Feng, Xiong Xiong, Yongjie Zhang. Financial Systems Engineering in China: 2010－2024 — A Bibliometric Study [J]. Chinese Journal of Management Science, 2025, 33(7): 11-23.
[8]	Jun Tian, Zanqiang Dong, Yali Li. Research on Financing Strategy of Advance Payment Supply Chain [J]. Chinese Journal of Management Science, 2025, 33(7): 272-283.
[9]	Pengfei Luo, Xinle Liu, Ting Lu, Yong Zhang. Merger and Acquisition Decisions for Firm under Carbon Emission Reduction [J]. Chinese Journal of Management Science, 2025, 33(7): 337-345.
[10]	Ke Yang, Xin Liu, Fengping Tian. Cross-Market Contagion of Stock Market's Extreme Risks between China and Other Major Emerging Market Countries [J]. Chinese Journal of Management Science, 2025, 33(7): 44-53.
[11]	Lu Wen, Ling Feng. Interbank Risk Contagion and Effective Bailout Frontier under Dynamic Government Intervention [J]. Chinese Journal of Management Science, 2025, 33(6): 37-48.
[12]	Zisheng Ouyang, Xuewei Zhou. Systemic Risk Backtesting and Connectedness of Chinese Financial Institutions: Evidence from MES and ΔCoVaR [J]. Chinese Journal of Management Science, 2025, 33(6): 14-26.
[13]	Zhiming Zhao, Qiong Pan. The Impact of Debt-Based Incentives on Corporate Refinancing [J]. Chinese Journal of Management Science, 2025, 33(6): 27-36.
[14]	Chunping Tan, Xuezhi Qin, Qin Shang, Wenhua Wang, Xianwei Lin. Incentive Compatible Combined Relief Strategy for Enterprise’ s Financial Distress Based on Perpetual Debt Replacement and Strategic Debt Payment [J]. Chinese Journal of Management Science, 2025, 33(5): 13-25.
[15]	Hui Yu, Shuang Wang. Supply Chain Finance: How can Enterprises Achieve Sustainability? [J]. Chinese Journal of Management Science, 2025, 33(5): 280-289.

The Volatility Estimation and VaR Measurement of China’s Copper Future Market: Based on Realized HAR GARCH Model Incorporating Generalized Realized Measures

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 0