Chinese Journal of Management Science ›› 2022, Vol. 30 ›› Issue (7): 9-19.doi: 10.16381/j.cnki.issn1003-207x.2019.2148
• Articles • Previous Articles Next Articles
QU Hui, SHEN Wei
Received:
2019-12-26
Revised:
2021-05-25
Online:
2022-08-05
Published:
2022-08-05
Contact:
瞿慧
E-mail:linda59qu@nju.edu.cn
CLC Number:
QU Hui, SHEN Wei. Investor Attention and Covariance Forecasting in China’s Stock Markets——A Study Based on the MHAR Type Models[J]. Chinese Journal of Management Science, 2022, 30(7): 9-19.
[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] Barndorff-Nielsen O E, Hansen P R, Lunde A, et al. Designing realized kernels to measure the ex-post variation of equity prices in the presence of noise[J]. Econometrica, 2008, 76(6): 1481-1536. [3] Barndorff-Nielsen O E, Shephard N. Econometric analysis of realized covariation: High frequency based covariance, regression, and correlation in financial economics[J]. Econometrica, 2004, 72(3): 885-925. [4] Barndorff-Nielsen O E, Hansen P R, Lunde A, et al. Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading[J]. Journal of Econometrics, 2011, 162(2): 149-169 [5] Corsi F. A simple approximate long-memory model of realized volatility[J]. Journal of Financial Econometrics, 2009, 7(2): 174-196. [6] Chiriac R, Voev V. Modelling and forecasting multivariate realized volatility[J]. Journal of Applied Econometrics, 2011, 26(6): 922-947. [7] Bauer G H, Vorkink K. Forecasting multivariate realized stock market volatility[J]. Journal of Econometrics, 2011, 160(1): 93-101. [8] Hautsch N, Kyj L M, Malec P. Do high-frequency data improve high-dimensional portfolio allocations[J]? Journal of Applied Econometrics, 2015, 30(2): 263-290. [9] Oh D H, Patton A J. High-dimensional copula-based distributions with mixed frequency data[J]. Journal of Econometrics, 2016, 193(2): 349-366. [10] Bollerslev T, Patton A J, Quaedvlieg R. Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions[J]. Journal of Econometrics, 2018, 207(1): 71-91. [11] Kahneman D. Attention and effort[M]. Prentice-Hall, Englewood Cliffs, NJ, 1973. [12] Da Z, Engelberg J, Gao P J. In search of attention[J]. The Journal of Finance, 2011, 66(5): 1461-1499. [13] Andrei D, Hasler M. Investor attention and stock market volatility[J]. Review of Financial Studies, 2015, 28(1): 33-72. [14] Aouadi A, Arouri M, Teulon F. Investor attention and stock market activity: Evidence from France[J]. Economic Modelling, 2013, 35(3): 674-681. [15] Goddard J, Kita A, Wang Qingwei. Investor attention and FX market volatility[J]. Journal of International Financial Markets Institutions and Money, 2015, 38: 79-96. [16] 苑莹, 樊晓倩. 投资者异常关注与成交量和波动率的交叉相关性及传导方向——基于复杂性理论视角[J]. 系统管理学报, 2019, 28(2): 89-99.Yuan Ying, Fan Xiaoqian. Cross-correlations and conducting direction between abnormal investor attention, trading volume, and volatility based on the perspective of complexity theory[J]. Journal of Systems & Management, 2019, 28(2): 89-99. [17] 张跃军, 李书慧. 投资者关注度对国际原油价格波动的影响研究[J]. 系统工程理论与实践, 2020, 40(10): 2519-2529.Zhang Yuejun, Li Shuhui. The impact of investor attention on international crude oil price volatility[J]. Systems Engineering-Theory & Practice, 2020, 40(10): 2519-2529. [18] 田金方, 杨晓彤, 薛瑞, 等. 不确定性事件、投资者关注与股市异质特征——以COVID-19概念股为例[J]. 财经研究, 2020, 46(11): 19-33.Tian Jinfang, Yang Xiaotong, Xue Rui, et al. Uncertain event, investor attention and heterogeneity of the stock market: A case study on COVID-19[J]. Journal of Finance and Economics, 2020, 46(11): 19-33. [19] Dimpfl T, Jank S. Can internet search queries help to predict stock market volatility?[J]. European Financial Management, 2016, 22(2): 171-192. [20] 陈声利, 关涛, 李一军. 基于跳跃、好坏波动率与百度指数的股指期货波动率预测[J].系统工程理论与实践, 2018, 38(2): 299-316.Chen Shengli, Guan Tao, Li Yijun. Forecasting realized volatility of Chinese stock index futures based on jumps, good-bad volatility and Baidu index[J]. Systems Engineering-Theory & Practice,2018, 38(2): 299-316. [21] Zhang Wei, Yan Kai, Shen Dehua. Can the Baidu index predict realized volatility in the Chinese stock market[J]? Financial Innovation, 2021, 7(1): 1-31. [22] 张同辉, 苑莹, 曾文. 投资者关注能提高市场波动率预测精度吗?——基于中国股票市场高频数据的实证研究[J]. 中国管理科学, 2020, 28(11): 192-205.Zhang Tonghui, Yuan Ying, Zeng Wen. Can investor attention help to predict stock market volatility? An empirical research based on Chinese stock market high-frequency data[J]. Chinese Journal of Management Science, 2020, 28(11): 192-205. [23] 瞿慧, 沈微. 基于LSTHAR模型的投资者关注对股市波动影响研究[J]. 中国管理科学,2020, 28(7): 23-34.Qu Hui, Shen Wei. The impact of investor attention on market volatility based on the LSTHAR model[J]. Chinese Journal of Management Science, 2020, 28(7): 23-34. [24] Barber B M, Odean T, Zhu N. Systematic noise[J]. Journal of Financial Markets, 2009, 12(4): 547-569. [25] De Long J B, Shleifer A, Summers L H, et al. Noise trader risk in financial markets[J]. Journal of Political Economy, 1990, 98(4):703-738. [26] Alfarano S, Lux T. A noise trader model as a generator of apparent financial power laws and long memory[J]. Macroeconomic Dynamics, 2007, 11(S1): 80-101. [27] Laurent S, Rombouts J V K, Violante F. On loss functions and ranking forecasting performances of multivariate volatility models[J]. Journal of Econometrics, 2013, 173(1): 1-10. [28] Diebold F X, Mariano R S. Comparing predictive accuracy[J]. Journal of Business and Economic Statistics, 1995, 13(3): 253-263. [29] Hansen P R, Lunde A, Nason J M. The model confidence set[J]. Econometrica, 2011, 79(2): 453-497. [30] 吴丹, 毕仁敏. 用户移动搜索与桌面搜索行为对比研究[J]. 现代图书情报技术, 2016, 32(2):1-8.Wu Dan, Bi Renmin. Mobile and desktop search behaviors: A comparative study[J]. New Technology of Library and Information Service, 2016, 32(2):1-8. |
[1] | HUANG Jin-bo, WU Li-li, YOU Yi-ling. Mean-VaR Model Based on the Asymmetric Laplace Distribution [J]. Chinese Journal of Management Science, 2022, 30(5): 31-40. |
[2] | JIANG Chong-hui, LIU Lin. The Effectiveness of Momentum Factor Tracking Strategy: Evidence from China Stock Market [J]. Chinese Journal of Management Science, 2022, 30(5): 86-97. |
[3] | ZHU Li, LIU Xiang-li, YANG Xiao-guang. Does Investor Sentiment Affect the Price Dynamic Relationship of Stock Index Futures-spot Market? [J]. Chinese Journal of Management Science, 2022, 30(4): 52-62. |
[4] | HUANG Jin-bo, CHEN Ling-xi, DING Jie. Corporate Social Responsibility, Mediacoverage and Stock Pricecrash Risk [J]. Chinese Journal of Management Science, 2022, 30(3): 1-12. |
[5] | HU Zhi-jun, LING Ai-fan, YANG Chao. The Ambiguity Premium in China’s A-shares Market——The Analysis from Intra-day High Frequency Data [J]. Chinese Journal of Management Science, 2022, 30(1): 42-53. |
[6] | LU Wan-bo, KANG Jing-hao. GAS-SKST-F Model and Its Application in High Frequency Multivariate Volatility Forecast [J]. Chinese Journal of Management Science, 2022, 30(1): 77-87. |
[7] | XIANG Cheng, YANG Jun. Who Gambles in the Market? A Study on Mutual Funds’ Preferences for Lottery-like Stocks [J]. Chinese Journal of Management Science, 2021, 29(11): 224-236. |
[8] | SHEN Genxiang, ZHANG Jingze. Dynamic Nelson-Siegel Term Structure Model with GARCH Error Terms and It’s Applications [J]. Chinese Journal of Management Science, 2021, 29(10): 1-11. |
[9] | CHEN Wen-bo, CHEN Lang-nan. Reactions of Stock Investors to Earnings Announcements——A Perspective from Gambling Preference [J]. Chinese Journal of Management Science, 2021, 29(9): 1-11. |
[10] | HUANG Yi-rong, BAI Yu-xuan. Is It “Intentional Herding” or “Spurious Herding”? The Influence of Network Contagion Degree on the Pricing Efficiency of Capital Market [J]. Chinese Journal of Management Science, 2021, 29(9): 12-24. |
[11] | WU Xin-yu, LI Xin-dan, MA Chao-qun. Measuring VaR Based on the Information Content of Option and High-frequency Data [J]. Chinese Journal of Management Science, 2021, 29(8): 13-23. |
[12] | JIN Xiu, CHEN Na, WANG Jia. Empirical Study on Cross-industry Asset Allocation Model under the Perspective of Flight-to-quality [J]. Chinese Journal of Management Science, 2020, 28(11): 12-22. |
[13] | WANG Sheng-quan, CHEN Lang-nan, LIU Ren-hao. Asset Bubbles, Technological Innovation and Economic Growth [J]. Chinese Journal of Management Science, 2020, 28(10): 1-12. |
[14] | WANG Jia, JIN Xiu, WANG Xu, LI Gang. Research on Variance Minimization Hedging Based on Time-Varying Markov DCC-GARCH Model [J]. Chinese Journal of Management Science, 2020, 28(10): 13-23. |
[15] | ZHU Peng-fei, TANG Yong, ZHONG Li. Portfolio Strategy Based on Wavelet-High Order Moments model-Take the International Crude Oil Markets as An Research Objects [J]. Chinese Journal of Management Science, 2020, 28(10): 24-35. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||
|