股价波动的长记忆性与横截面股票收益——基于中国市场的实证研究

doi:10.16381/j.cnki.issn1003-207x.2020.1410

摘要/Abstract

摘要： 本文基于GPH估计法，对我国股票市场个股的长记忆性进行滚动估计，得到时变的长记忆性指标，并在此基础上从横截面的视角，对股价波动的长记忆性与股票预期收益率之间的关系进行了深入细致的研究。实证结果表明：中国证券市场上绝大部分的股票都具有显著的股价波动长记忆性特征。无论在组合还是在个股层面，股价波动的长记忆程度与其未来的超额收益之间均存在显著的负相关关系。即便控制了公司的特征变量后，这一负相关关系依然显著存在。进一步考虑了不同的市场状态、不同的持有期，以及不同的长记忆估计方法后，长记忆指标的风险溢酬始终显著为负，说明长记忆指标中确实包含公司特征因素之外的重要定价信息。

关键词: 长记忆性；风险溢酬；资产定价

Abstract: Long memory processes are time series with long-range dependence and exist in numerous fields such as physics, hydrology, economics and finance. Long memory processes have drawn extensive attention from academics and practitioners since Hurst (1951). However, most of the existing studies have analyzed the long memory only in the time-series dimension. To the best of our knowledge, it is the first to examine the relationship between long memory volatility and expected returns in Chinese stock market from cross-sectional perspective.

Key words: long memory; risk premium; asset pricing

中图分类号:

F830.91

陈淼鑫, 黄振伟. 股价波动的长记忆性与横截面股票收益——基于中国市场的实证研究[J]. 中国管理科学, 2023, 31(4): 1-10.

CHEN Miao-xin, HUANG Zhen-wei. Long Memory Volatility and Cross-section Stock Returns: Empirical Research in Chinese Stock Market[J]. Chinese Journal of Management Science, 2023, 31(4): 1-10.

参考文献

［1］ Hurst H E. Long-term storage capacity of reservoirs［J］. Transactions of American Society of Civil Engineers, 1951, 116: 770-799.
［2］ Baillie R T. Long memory processes and fractional integration in econometrics［J］. Journal of Econometrics, 1996, 73(1): 5-59.
［3］ Granger C W J, Joyeux R. An introduction to long-memory time series models and fractional differencing［J］. Journal of Time Series Analysis, 1980, 1(1): 15-29.
［4］ Hosking J R M. Fractional differencing［J］. Biometrika, 1981, 68(1): 165-176.
［5］ Baillie R T, Bollerslev T, Mikkelsen H O. Fractionally integrated generalized autoregressive conditional heteroskedasticity［J］. Journal of Econometrics, 1996, 74(1): 3-30.
［6］ Geweke J, Porter-Hudak S. The estimation and application of long memory time series models［J］. Journal of Time Series Analysis, 1983, 4(4): 221-238.
［7］ Robinson P M. Gaussian semiparametric estimation of long range dependence［J］. The Annals of Statistics, 1995b, 23(5): 1630-1661.
［8］ Lo A W. Long-term memory in stock market prices［J］. Econometrica, 1991, 59(5):1279-1313.
［9］ Crato N, Ray B K. Memory in returns and volatilities of commodity futures’ contracts［J］. Journal of Futures Markets, 2000, 20(6):525-543.
［10］ Lu Y K, Perron P. Modeling and forecasting stock return volatility using a random level shift model［J］. Journal of Empirical Finance, 2010, 17(1):138-156.
［11］ Rossi E, Magistris P S D. Long memory and tail dependence in trading volume and volatility［J］. Journal of Empirical Finance, 2013, 22(3): 94-112.
［12］ Abbritti M, Gil-Alana L A, Lovcha Y, et al. Term structure persistence［J］. Journal of Financial Econometrics, 2016, 14(2): 331-352.
［13］ Caporale G M, Gil-Alana L, Plastun A. Long memory and data frequency in financial markets［J］. Journal of Statistical Computation and Simulation, 2019, 89(10): 1763-1779.
［14］ Nguyen D B B, Prokopczuk M, Sibbertsen P. The memory of stock return volatility: asset pricing implications［J］. Journal of Financial Markets, 2020, 47: 1-18.
［15］陈梦根. 中国股市长期记忆效应的实证研究［J］. 经济研究, 2003(3): 70-78.Chen Menggen. Empirical study on the long-memory effect in Chinese stock market［J］. Economic Research Journal, 2003(3): 70-78.
［16］罗登跃, 王玉华．上海股市收益率和波动性长记忆特征实证研究［J］.金融研究, 2005(11): 109-116.Luo Dengyue, Wang Yuhua. Empirical study on the long-memory of stock returns and volatility in Shanghai stock market［J］. Journal of Financial Research, 2005(11): 109-116.
［17］何兴强, 李仲飞. 上证股市收益的长期记忆：基于V/S的经验分析［J］. 系统工程理论与实践, 2006, 26(12): 47-54.He Xingqiang, Li Zhongfei. Long-term memory in stock returns of Shanghai stock exchange: evidence from V/S statistic［J］. Systems Engineering-Theory & Practice, 2006, 26(12): 47-54.
［18］苑莹, 庄新田. 中国股票市场的长记忆性与市场发展状态［J］. 数理统计与管理, 2008, 27(1): 156-163.Yuan Ying, Zhuang Xintian. Long memory and state of market development in Chinese stock markets［J］. Journal of Applied Statistics and Management, 2008, 27(1): 156-163.
［19］余俊, 方爱丽, 熊文海. 国际股票市场收益的长记忆性比较研究［J］. 中国管理科学, 2008, 16(4): 24-29.Yu Jun, Fang Aili, Xiong Wenhai. Research on the long memory in international stock returns［J］. Chinese Journal of Management Science, 2008, 16(4): 24-29.
［20］李云红, 魏宇, 张帮正. 股票市场历史信息的长记忆性特征研究［J］. 中国管理科学, 2015, 23(9): 37-45.Li Yunhong, Wei Yu, Zhang Bangzheng. Research on the long memory characters of stock market's history information［J］. Chinese Journal of Management Science, 2015, 23(9): 37-45.
［21］余立威，陈涛. 股票市场的长记忆及其动态结构［J］. 数理统计与管理, 2017, 36(6): 1106-1118.Yu Liwei, Chen Tao. Long-term memory in stock market and its dynamic structure［J］. Journal of Applied Statistics and Management, 2017, 36(6): 1106-1118.
［22］ Zhou Wenchao, Xu Haichao, Cai Zhengyu, et al. Peculiar statistical properties of Chinese stock indices in bull and bear market phases［J］. Physica A: Statistical Mechanics and its Applications, 2009, 388(6): 891-899.
［23］唐勇, 朱鹏飞. 沪深300 股指期货牛熊周期的长记忆性、风险和有效性实证研究: 基于多重分形视角［J］. 管理评论, 2019, 31(8): 59-70.Tang Yong, Zhu Pengfei. Research of long memory, risk and efficiency of bull and bear based on CSI300 index futures: from the perspective of multifractality［J］. Management Review, 2019, 31(8): 59-70.
［24］ Robinson P M. Log-periodogram regression of time series with long range dependence［J］. The Annals of Statistics, 1995a, 23(3): 1048-1072.
［25］ Hurvich C M, Deo R S. Plug-in selection of the number of frequencies in regression estimates of the memory parameter of a long-memory time series［J］. Journal of Time Series Analysis, 1999, 20(3): 331-341.
［26］ Fama E F, French K R. Common risk factors in the returns on stocks and bonds［J］. Journal of Financial Economics, 1993, 33(1): 3-56.
［27］ Nartea G V, Kong Dongmin, Wu Ji. Do extreme returns matter in emerging markets? Evidence from the Chinese stock market［J］. Journal of Banking and Finance, 2017, 76(3): 189-197.
［28］ Amihud Y. Illiquidity and stock returns: Cross-section and time-series effects［J］. Journal of Financial Markets, 2002, 5(1): 31-56.
［29］ Corsi F. A simple approximate long-memory model of realized volatility［J］. Journal of Financial Econometrics, 2009, 7(2): 174-196.
［30］何兴强, 周开国. 牛、熊市周期和股市间的周期协同性［J］. 管理世界, 2006(4): 35-40.He Xingqiang, Zhou Kaiguo. Bull, bear market cycle and the cyclical synergy between stock markets［J］. Journal of Management World, 2006(4): 35-40.

[1]	梁墨, 李鸿翔, 张顺明. 基于ST预测的财务困境测度与股票横截面收益[J]. 中国管理科学, 2023, 31(2): 138-149.
[2]	何朝林, 张棋翔, 曹旺栋. 基于异质价格信念的金融资产泡沫形成机制[J]. 中国管理科学, 2022, 30(12): 162-173.
[3]	刘超, 郭亚东. 多时间尺度下行业间系统性金融风险溢出及拓扑结构分析[J]. 中国管理科学, 2022, 30(10): 46-59.
[4]	蒋崇辉, 刘林. 惯性因子跟踪策略的有效性：来自中国股票市场的证据[J]. 中国管理科学, 2022, 30(5): 86-97.
[5]	朱莉, 刘向丽, 杨晓光. 投资者情绪影响股指期现货市场的价格动态关系吗？[J]. 中国管理科学, 2022, 30(4): 52-62.
[6]	向诚, 杨俊. 谁在市场中赌博？基金博彩行为研究[J]. 中国管理科学, 2021, 29(11): 224-236.
[7]	沈根祥，张靖泽. 条件异方差动态Nelson-Siegel利率期限结构模型及其应用[J]. 中国管理科学, 2021, 29(10): 1-11.
[8]	陈文博, 陈浪南. 股市投资者对盈余公告的反应——基于博彩偏好的视角[J]. 中国管理科学, 2021, 29(9): 1-11.
[9]	黄诒蓉, 白羽轩. 网络传染是“真羊群”还是“伪羊群”?——网络传染程度对资本市场定价效率的影响[J]. 中国管理科学, 2021, 29(9): 12-24.
[10]	何朝林, 涂蓓, 王鹏. 动态均值-方差资产组合的有效性:时变风险容忍度视角[J]. 中国管理科学, 2021, 29(1): 1-11.
[11]	金秀, 尘娜, 王佳. 安全投资转移视角下的跨行业投资组合模型及实证[J]. 中国管理科学, 2020, 28(11): 12-22.
[12]	陈其安, 张慧, 陈抒妤. 股指期货交易加剧了中国股票市场波动性吗？——基于投资者结构的理论和实证研究[J]. 中国管理科学, 2020, 28(4): 1-13.
[13]	欧阳红兵, 黄亢, 闫洪举. 基于LSTM神经网络的金融时间序列预测[J]. 中国管理科学, 2020, 28(4): 27-35.
[14]	赵华, 肖佳文. 考虑微观结构噪声与测量误差的波动率预测[J]. 中国管理科学, 2020, 28(4): 48-60.
[15]	鲁万波, 黄光麟, Kris Boudt. 股市涨跌预测与量化投资策略:基于时变矩成分分析[J]. 中国管理科学, 2020, 28(2): 1-12.