我国黄金期货价格波动率预测研究：来自模型缩减方法的新证据

doi:10.16381/j.cnki.issn1003-207x.2020.0488

中国管理科学 ›› 2022, Vol. 30 ›› Issue (4): 30-41.doi: 10.16381/j.cnki.issn1003-207x.2020.0488

我国黄金期货价格波动率预测研究：来自模型缩减方法的新证据

梁超¹, 魏宇², 马锋¹, 李霞飞¹

1.西南交通大学经济管理学院，四川成都610031； 2.云南财经大学金融学院，云南昆明650221

收稿日期:2020-03-24 修回日期:2020-05-19 出版日期:2022-04-20 发布日期:2022-04-26
通讯作者: 魏宇(1975-)，男(汉族)，四川攀枝花人，云南财经大学金融学院，教授，研究方向：金融工程与风险管理、能源金融，Email：weiyusy@126.com. E-mail:weiyusy@126.com
基金资助:
国家自然科学基金资助项目 (71671145、71971191、71701170)；云南省高校科技创新团队项目(2019014)；云南省科技计划基础研究重点项目(202001AS070018)

Forecasting Volatility of China Gold Futures Price: New Evidence from Model Shrinkage Methods

LIANG Chao¹, WEI Yu², MA Feng¹, LI Xia-fei¹

1. School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, China;2. School of Finance, Yunnan University of Finance and Economics, Kunming 650221, China

Received:2020-03-24 Revised:2020-05-19 Online:2022-04-20 Published:2022-04-26
Contact: 魏宇 E-mail:weiyusy@126.com

摘要/Abstract

摘要： 黄金作为重要的避险资产，对其价格波动的定量描述和预测对于各类投资者的风险管理决策意义重大。基于标准回归预测模型，采用主成分分析、组合预测和两种主流的模型缩减方法(Elastic net 和Lasso)构建新的波动率预测模型，探究哪种方法能够更有效地利用多个预测因子信息。进一步，运用模型信度集合(model confidence set，MCS)、样本外R2和方向测试(Direction-of-Change，DoC)三种评价方法检验新模型的样本外预测精度。实证结果显示：不论是基于哪一种评价方法，相比其它竞争模型，两种缩减模型的样本外预测精度均为最优，可以为我国黄金期货价格的波动率预测提供可靠保障。

关键词: 已实现波动率；高频数据；组合预测；机器学习；Elastic net；Lasso

Abstract: As an important safe-haven asset, the quantitative description and prediction of the price volatility of gold is of great significance to the risk management decisions for various investors. Based on the standard regression model, principal component analysis, combination forecast, and two model shrinkage methods (i.e., Elastic net and Lasso) are employed to design new volatility prediction models and investigate which method can more effectively use the information of multiple predictors. In addition, the model confidence set (MCS), out-of-sample R2, Direction-of-change (DoC) evaluation methods are used to assess the out-of-sample prediction accuracy of the new models. The empirical results show that no matter which evaluation method is used, compared with other competition models, the out-of-sample prediction accuracy of the two shrinkage models is the best, which can provide a reliable guarantee for the volatility forecast of China's gold futures prices.

Key words: realized volatility; high-frequency data; combination forecast; machine learning; Elastic net; Lasso

中图分类号:

F830

梁超, 魏宇, 马锋, 李霞飞. 我国黄金期货价格波动率预测研究：来自模型缩减方法的新证据[J]. 中国管理科学, 2022, 30(4): 30-41.

LIANG Chao, WEI Yu, MA Feng, LI Xia-fei. Forecasting Volatility of China Gold Futures Price: New Evidence from Model Shrinkage Methods[J]. Chinese Journal of Management Science, 2022, 30(4): 30-41.

参考文献

［1］龚旭, 曹杰, 文凤华, 等. 基于杠杆效应和结构突变的HAR族模型及其对股市波动率的预测研究［J］. 系统工程理论与实践, 2020, 40(5): 1113-1133.Gong Xu, Cao Jie, Wen Fenghua, et al. The HAR-type models with leverage and structural breaks and their applications to the volatility forecasting of stock market［J］. Systems Engineering Theory & Practice, 2020, 40(5): 1113-1133.
［2］郭晨, 吴君民. 我国银行业二元风险混合传染效应研究［J］. 云南财经大学学报, 2019, 35(7): 64-76.Guo Chen, Wu Junmin. Research on the mixed contagion effect of dual risks in China’s banking industry［J］. Journal of Yunnan University of Finance and Economics, 2019, 35(7): 64-76.
［3］杨科, 陈浪南. 股市波动率的短期预测模型和预测精度评价［J］. 管理科学学报, 2012, 15(5): 19-31.Yang Ke, Chen Langnan. Short-term volatility forecast model and its performance evaluation［J］. Journal of Management Sciences in China, 2012, 15(5): 19-31.
［4］徐欣, 徐惟. 银行杠杆、同业业务创新与系统性风险［J］. 云南财经大学学报, 2019, 35(11): 49-58.Xu Xin, Xu Wei. Bank leverage, interbank business innovation and systematic risk［J］. Journal of Yunnan University of Finance and Economics, 2019, 35(11): 49-58.
［5］徐巧玲. 劳动收入、不确定风险与家庭金融资产选择［J］. 云南财经大学学报, 2019, 35(5): 75-86.Xu Qiaoling. Labor Income，Uncertain risks and the selection of family financial assets［J］. Journal of Yunnan University of Finance and Economics, 2019, 35(5): 75-86.
［6］郭彦峰, 肖倬. 中美黄金市场的价格发现和动态条件相关性研究［J］. 国际金融研究, 2009(11): 75-83.Guo Yanfeng，Xiao Zhuo. The study of price discov-ery and dynamic correlation coefficients between the Chinese and American gold markets［J］. Studies of International Finance，2009(11): 75-83.
［7］冯辉, 张蜀林. 国际黄金期货价格决定要素的实证分析［J］. 中国管理科学, 2012, 20(S1): 424-428.Feng Hui, Zhang Shulin. The empirical analysis about the determinants of international gold futures prices［J］. Chinese Journal of Management Science, 2012, 20(S1): 424-428.
［8］ Liu Sun, Tang Tingfei, McKenzie A M, et al. Low-frequency volatility in China’s gold futures market and its macroeconomic determinants［J］. Mathematical Problems in Engineering, 2015.
［9］ Smales L A. Commodity market volatility in the presence of US and Chinese macroeconomic news［J］. Journal of Commodity Markets, 2017, 7: 15-27.
［10］刘金娥, 高佳辉. 投资者情绪与黄金期货价格动态关系研究［J］. 价格理论与实践, 2017(9): 80-83.Liu Jiner, Guo Jiahui. Analysis on investor sentiment and gold futures price fluctuation［J］. Price:Theory & Practice, 2017(09): 80-83.
［11］ Luo Xingguo, Qin Shihua, Ye Zinan. The information content of implied volatility and jumps in forecasting volatility: Evidence from the Shanghai gold futures market［J］. Finance Research Letters, 2016, 19: 105-111.
［12］ Wei Yu, Liang Chao, Li Yan, et al. Can CBOE gold and silver implied volatility help to forecast gold futures volatility in China? Evidence based on HAR and Ridge regression models［J］. Finance Research Letters, 2020, 35: 101287.
［13］ Santamaría-Bonfil G, Frausto-Solís J, Vázquez-Rodarte I. Volatility forecasting using support vector regression and a hybrid genetic algorithm［J］. Computational Economics, 2015, 45(1): 111-133.
［14］ Ma Feng, Liu Jing, Wahab M I M, et al. Forecasting the aggregate oil price volatility in a data-rich environment［J］. Economic Modelling, 2018, 72: 320-332.
［15］ Zhang Yaojie, Ma Feng, Wang Yudong. Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?［J］. Journal of Empirical Finance, 2019, 54: 97-117.
［16］ Rapach D E, Strauss J K, Zhou Guofu. Out-of-sample equity premium prediction: Combination forecasts and links to the real economy［J］. The Review of Financial Studies, 2010, 23(2): 821-862.
［17］ Andersen T G, Bollerslev T. Answering the skeptics: Yes, standard volatility models do provide accurate forecasts［J］. International economic review, 1998: 885-905.
［18］ Liu L Y, Patton A J, Sheppard K. Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes［J］. Journal of Econometrics, 2015, 187(1): 293-311.
［19］ Tibshirani R. Regression shrinkage and selection via the lasso［J］. Journal of the Royal Statistical Society: Series B (Methodological), 1996, 58(1): 267-288.
［20］ Zou Hui, Hastie T. Regularization and variable selection via the elastic net［J］. Journal of the royal statistical society: series B (statistical methodology), 2005, 67(2): 301-320.
［21］ Hansen P R. A test for superior predictive ability［J］. Journal of Business & Economic Statistics, 2005, 23(4): 365-380.
［22］ Hansen P R, Lunde A, Nason J M. The model confidence set［J］. Econometrica, 2011, 79(2): 453-497.
［23］魏宇, 马锋, 黄登仕. 多分形波动率预测模型及其MCS检验［J］. 管理科学学报, 2015, 18(8): 61-72.Wei Yu, Ma Feng, Huang Dengshi. Multi-fractal volatility forecasting model and its MCS test［J］. Journal of Management Sciences in China, 2015, 18(8): 61-72.
［24］雷立坤, 余江, 魏宇, 等. 经济政策不确定性与我国股市波动率预测研究［J］. 管理科学学报, 2018, 21(6): 88-98.Lei Likun，Yu Jiang，Wei Yu，et al. Forecasting volatility of Chinese stock market with economic policy uncertainty［J］. Journal of Management Sciences in China，2018，21(6)： 88-98.
［25］ Clark T E, West K D. Approximately normal tests for equal predictive accuracy in nested models［J］. Journal of econometrics, 2007, 138(1): 291-311.
［26］ Degiannakis S, Filis G. Forecasting oil price realized volatility using information channels from other asset classes［J］. Journal of International Money and Finance, 2017, 76: 28-49.
［27］ Pesaran M H, Timmermann A. A simple nonparametric test of predictive performance［J］. Journal of Business & Economic Statistics, 1992, 10(4): 461-465.

[1]	郭冉冉,叶五一,刘小泉,缪柏其. 商品期货投资组合与市场收益的尾部相依研究[J]. 中国管理科学, 2024, 32(10): 11-19.
[2]	韩鑫韬,张晓敏,刘星. 宏观审慎管理配合下的最优货币政策选择[J]. 中国管理科学, 2024, 32(10): 1-10.
[3]	成思聪,王天一. 引入隔夜信息的期权定价模型研究[J]. 中国管理科学, 2024, 32(9): 1-10.
[4]	吴鑫育,谢海滨,马超群. 经济政策不确定性与人民币汇率波动率[J]. 中国管理科学, 2024, 32(8): 1-14.
[5]	谢楠,何海涛,周艳菊,王宗润. 乡村振兴背景下基于中央政府项目补贴分析的供应链金融决策研究[J]. 中国管理科学, 2024, 32(8): 214-229.
[6]	于孝建,刘国鹏,刘建林,肖炜麟. 基于LSTM网络和文本情感分析的股票指数预测[J]. 中国管理科学, 2024, 32(8): 25-35.
[7]	倪宣明,郑田田,赵慧敏,武康平. 基于最优异质收益率因子的资产定价研究[J]. 中国管理科学, 2024, 32(8): 50-60.
[8]	于文华,任向阳,杨坤,魏宇. 传染病不确定性对大宗商品期货价格波动的非对称影响研究[J]. 中国管理科学, 2024, 32(5): 254-264.
[9]	蔡毅,唐振鹏,吴俊传,杜晓旭,陈凯杰. 基于灰狼优化的混频支持向量机在股指预测与投资决策中的应用研究[J]. 中国管理科学, 2024, 32(5): 73-80.
[10]	李仲飞,周骐. 一个基于BL模型和复杂网络的行业配置模型[J]. 中国管理科学, 2024, 32(4): 1-13.
[11]	张雪彤,张卫国,王超. 发达市场与新兴市场的尾部风险[J]. 中国管理科学, 2024, 32(4): 14-25.
[12]	尹海员,寇文娟. 基于朴素贝叶斯法的投资者情绪度量及其对股票特质风险的影响[J]. 中国管理科学, 2024, 32(4): 38-47.
[13]	王晓燕,杨胜刚,张科坤. 终极所有权结构与企业委托贷款行为[J]. 中国管理科学, 2024, 32(4): 48-57.
[14]	李爱忠,任若恩,董纪昌. 图网络风险感知与稀疏低秩的组合管理策略[J]. 中国管理科学, 2024, 32(4): 58-65.
[15]	吴鑫育,姜晓晴,李心丹,马超群. 基于已实现EGARCH-FHS模型的上证50ETF期权定价研究[J]. 中国管理科学, 2024, 32(3): 105-115.

我国黄金期货价格波动率预测研究：来自模型缩减方法的新证据

Forecasting Volatility of China Gold Futures Price: New Evidence from Model Shrinkage Methods

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 0