带有市场交易信息和随机微观噪声下的杠杆效应研究

doi:10.16381/j.cnki.issn1003-207x.2018.1363

Abstract

Abstract: Leverage effect is a widespread existence in risk management, which is also an important topic in financial econometrics. On the other hand, it is valuable for us to study leverage effect based on the market trade information and the stochastic noise model, since that rich information is available and not viewed as the stochastic noise in high-frequency financial market. In this paper, the leverage effect combining trading information with stochastic microstructure noise model is studied, and the new estimator of leverage effect is proposed, which has the convergence rate n^1/8. The variance and the relative theorems are also provided. A simulation is conducted to study that leverage effect could be estimated more efficiently and more accurately via the general market microstructure information. Simulation results show the leverage effect estimator performs better asymptotic normal property and smaller biases. An empirical study has been carried out to demonstrate that leverage effect is significant in forecasting volatility, which provides a useful theoretical basis for relevant financial departments to conduct financial guidance.

Key words: leverage effect, high frequency data, market microstructure information, quadratic covariation, volatility

CLC Number:

O212

Yuan Hui-ling, XU Lu, Zhou Yong. Leverage Effect Combining Trading Information with Stochastic Microstructure Noise[J]. Chinese Journal of Management Science, 2020, 28(9): 12-22.

References

[1] Black F.Studies of stock price volatility changes[C]//Proceedings of the 1976 Meetings of the Businessand Economic Statistics Section,American Statistical Association,Washington, DC.USA, 1976:177-181.
[2] Christie A A. The stochastic behavior of commonstock variances:value,leverage and interest rate effects[J].Journal of Financial Economics,1982,10(4):407-432.
[3] Nelson D B.Conditional heteroskedasticity in asset returns:a new approach[J].Econometrica, 1991,59(2):347-370.
[4] Engle R F.Ng V K.Measuringand testing the impact of news on volatility[J].The Journal ofFinance,1993,48(5):1749-1778.
[5] Bekaert G.Wu Guojun.Asymmetricvolatility and risk in equity markets[J]. Review of Financial Studies, 2000, 13(1):1-42.
[6] Bouchaud J P. Matacz A.Potters M. The leverage effect in financial markets:retarded volatility and market panic[C]//Proceedings of the Science and Finance, the Research Division of Capital Fund Management, Levallois Cedex, France, February, 2001.
[7] Bollerslev T. Litvinova J. Tauchen G. Leverage and volatilityfeedback effects in high-frequency data[J].Journal of Financial Econometrics,2006,4(3):353-384.
[8] Ait-Sahalia Y. Fan Jianqing. Li Yingying. The leverage effect puzzle:disentangling sources of bias at high frequency[J]. Journal of Financial Economics, 2013, 109(1):224-249.
[9] Zhang Lan. Myland P A. Ait-Shahalia Y. A tale of two time scales:determining integrated volatility with noisy high frequency data[J]. Journal of American Statistical Association, 2005, 100(472):1394-1411.
[10] Jacod J. Li Yingying. Mykland P A. et al. Microstructure noise in the continuous case:the pre-averaging approach[J]. Stochastic Process and their Application, 2009, 119(7):2249-2276.
[11] Wang D C. Mykland P A. The estimation of the leverage effect with high frequency data[J]. Journal of the American Statistical Association, 2014, 109(505):197-215.
[12] Andersen T G. Bollerslev T. Diebold F X. et al. Great realizations[J]. Risk, 2000, 13(1):105-108.
[13] Roll R. A simple implicit measure of the effective bid-ask spread in an efficient market[J]. The Journal of Finance, 1984, 39(4):1127-1139.
[14] Glosten L R. Harris L E. Estimating the components of the bid/ask spread[J]. Journal of Financial Economics, 1988, 21(2):123-142.
[15] Harris L E. Statistical properties of the Roll serial covariance bid/ask spread estimator[J]. The Journal of Finance, 1990, 45(2):579-590.
[16] Almgren R. Chriss N. Optimal execution of portfolio transactions[J]. Journal of Risk, 2000, 3(1):5-39.
[17] Li Yingying, Xie Shangyu, Zheng Xinghua. Efficient estimation of integrated volatility incorporating trading information[J]. Journal of Econometrics, 2016, 195(1):33-50.
[18] Yuan Huiling, Mu Yan, Zhou Yong. Leverage effect in high-frequency data with market microstructure[J]. Statistics and its interface, 2020, 13(1):91-101.
[19] Keim D B. Madhavan A. The upstairs market for large-block transactions:analysis and measurement of price effects[J]. The Review of Financial Studies, 1996, 9(1):1-36.
[20] Koopman S J. Jungbacker B. Hol E. Forecasting daily variability of the S&P 100 stock index using historial, realised and implied volatility measurement[J]. The Journal of Empirical Finance, 2005, 12(3):445-475.
[21] 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.
[22] 沈根祥, 邹欣悦. 已实现波动GAS-HEAVY模型及其实证研究[J]. 中国管理科学, 2019, 27(1):1-10.
[23] Kallenberg O. Foundations of modern probability, probability and its applications[M]. New York:Springer-Verlag, 1997.

Leverage Effect Combining Trading Information with Stochastic Microstructure Noise

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 10

[1]	CHEN Qi-an, ZHANG Hui. Systemic Risk Shock, Enterprise Innovation Ability and Stock Price Volatility: Theoretical and Empirical Research [J]. Chinese Journal of Management Science, 2021, 29(3): 1-13.
[2]	CHEN Qiang, GONG Yu-ting. Specification Test of Volatility Functions in Jump Diffusion Processes using Nearest Neighbor Truncation [J]. Chinese Journal of Management Science, 2020, 28(7): 45-56.
[3]	SHEN Gen-xiang, ZOU Xin-yue. Identification and Measurement of Leverage Effects Using Local Correlation and Truncated Distorted Mix Copula Constructing [J]. Chinese Journal of Management Science, 2020, 28(7): 68-76.
[4]	QU Hui, ZHANG Yi. The Study of High-dimensional Volatility Estimators and Forecasting Models based on Volatility Timing Performance [J]. Chinese Journal of Management Science, 2020, 28(5): 62-70.
[5]	CHEN Qi-an, ZHANG Hui, CHEN Shu-yu. Does Stock Index Futures Trading Increase the Stock Market Volatility in China?——Theoretical and Empirical Research Based on Investor Structure [J]. Chinese Journal of Management Science, 2020, 28(4): 1-13.
[6]	ZHAO Hua, XIAO Jia-wen. Volatility Forecasting in the Presence of Microstructure Noise and Measurement Error [J]. Chinese Journal of Management Science, 2020, 28(4): 48-60.
[7]	ZHANG Tong-hui, 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.
[8]	LI Hao-hua, ZHANG Xiao-qiang, LUO Peng-fei, LI Xin-dan. The Pricing of Relationship Loan and Optimal Loan Interest Rate under Ambiguity Aversion [J]. Chinese Journal of Management Science, 2020, 28(10): 36-42.
[9]	LIU Feng-gen, WU Jun-chuan, YANG Xi-te, OUYANG Zi-sheng. Long-run Dynamic Effect of Macro-economy on Stock Market Volatility Based on Mixed Frequency Data Model [J]. Chinese Journal of Management Science, 2020, 28(10): 65-76.
[10]	ZHANG Li-deng, TANG Qi-ming, ZHANG Yu-hang. Housing Price Volatility, Housing Credit, and Macroprudential Policy [J]. Chinese Journal of Management Science, 2019, 27(6): 1-9.
[11]	ZHENG Zun-xin, WANG Hua-ran, ZHU Fu-min. Studies on Volatility Features and Jump Behavior of Shanghai 50ETF Market Based on Levy-GARCH Model [J]. Chinese Journal of Management Science, 2019, 27(2): 41-52.
[12]	WU Xin-yu, ZHAO Kai, LI Xin-dan, MA Chao-qun. Option Pricing Under Time-Varying Risk Aversion: An Empirical Study Based on SSE 50ETF Options [J]. Chinese Journal of Management Science, 2019, 27(11): 11-22.
[13]	SHEN Gen-xiang, ZOU Xin-yue. GAS-HEAVY Model for Realized Measures of Volatility and Returns [J]. Chinese Journal of Management Science, 2019, 27(1): 1-10.
[14]	LIANG Xu-zhuo, NI De-bing, TANG Xiao-wo. The Transmission Mechanism and Result of The Two-side Exchange Rate Risk in Supply Chains [J]. Chinese Journal of Management Science, 2019, 27(1): 44-52.
[15]	ZHU Xue-hong, ZOU Jia-wen, HAN Fei-yan, CHEN Jin-yu. 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.