沪深300股指期现货市场的交易竞争与价格发现的关系研究
|
摘要
以我国沪深300股指期现货市场为研究对象,从交易规模方面采用Lotka-Volterra模型检验沪深300股指期现货市场的交易竞争,从交易价格方面采用向量自回归模型、向量误差修正模型检验沪深300股指期现货市场的价格发现速度,采用永久短暂模型和信息份额模型检验沪深300股指期现货市场的价格发现强度,据此分析沪深300股指期现货市场的交易竞争与价格发现的时变特征及内在关系。研究结果表明,我国沪深300股指期现货市场的交易竞争与价格发现均呈现出明显的时变特征,沪深300股指期现货市场的交易竞争能力与价格发现能力呈正相关关系。本文的研究结论从沪深300股指期现货市场交易竞争能力的动态性角度解释了其价格发现能力的时变性,从股指期现货市场的深层价量关系方面剖析了股指期现货市场微观结构。
Based on the data of the CSI 300 stock index futures and the CSI 300 stock index spot markets, the trading competition relationships between the stock index futures and spot markets are tested with the Lotka-Volterra model, and the price discovery relationships between the stock index futures and spot markets are tested with the VAR, VECM, PT and IS model. Furthermore, the relationships between trading competition and price discovery are studied. The conclusions are drawn as follows: there are significant time-varying characteristics in both trading competition and price discovery between the CSI 300 stock index futures and spot markets, the trading competition is positively correlated with the price discovery, and the dynamic trading competition can explain the time-varying price discovery between the CSI 300 stock index futures and spot markets.
Based on the data of the CSI 300 stock index futures and the CSI 300 stock index spot markets, the trading competition relationships between the stock index futures and spot markets are tested with the Lotka-Volterra model, and the price discovery relationships between the stock index futures and spot markets are tested with the VAR, VECM, PT and IS model. Furthermore, the relationships between trading competition and price discovery are studied. The conclusions are drawn as follows: there are significant time-varying characteristics in both trading competition and price discovery between the CSI 300 stock index futures and spot markets, the trading competition is positively correlated with the price discovery, and the dynamic trading competition can explain the time-varying price discovery between the CSI 300 stock index futures and spot markets.
引文
[1] 林仁皓. 市场知情交易概率(VPIN)在股指期货交易规则修改前后的适用性分析[J]. 投资研究, 2016,35(3):140-150
[2] 华仁海,刘庆富. 股指期货与股指现货市场间的价格发现能力探究[J]. 数量经济技术经济研究, 2010,(10):90-100
[3] 何诚颖,张龙斌,陈薇. 基于高频数据的沪深300指数期货价格发现能力研究[J]. 数量经济技术经济研究, 2011,(5):139-151
[4] 刘向丽,张雨萌. 基于向量误差修正模型的股指期货价格发现功能研究[J]. 管理评论, 2012,24(2):71-77
[5] 严敏,巴曙松,吴博. 我国股指期货市场的价格发现与波动溢出效应[J]. 系统工程, 2009,27(10):32-38
[6] 刘庆富,华仁海. 中国股指期货与股票现货市场之间的风险传递效应研究[J]. 统计研究, 2011,28(11):84-90
[7] 方匡南,蔡振忠. 我国股指期货价格发现功能研究[J]. 统计研究, 2012,29(5):73-78
[8] Yang J., Yang Z., Zhou Y. Intraday Price Discovery and Volatility Transmission in Stock Index and Stock Index Futures Markets: Evidence from China[J]. Journal of Futures Markets, 2012,32(2):99-121
[9] 代宏霞,林祥友. 股指期货合约存续期价格引导关系的时变性研究[J]. 投资研究, 2012,31(5):127-140
[10] 李政,卜林,郝毅. 我国股指期货价格发现功能的再探讨——来自三个上市品种的经验证据[J]. 财贸经济, 2016,37(7):79-93
[11] Lotka A. J. Elements of Physical Biology[M]. Philadelphia: William & Wilkins Companies, 1925
[12] Volterra V. Variazioni e Fluttuazioni Del Numero Dindividui in Specie Animali Conviventi. Mem[J]. R. Accad. Naz. dei. Lincei, 1926,6(2):31-113
[13] Modis T. Technological Forecasting at the Stock Market[J]. Technological Forecasting and Social Change, 1999,62(3):173-202
[14] Farmer J. D. A Simple Model for the Nonequilibrium Dynamics and Evolution of a Financial Market[J]. International Journal of Theoretical and Applied Finance, 2000,3(3):425-441
[15] Ito T., Lin W. L. Race to the Center: Competition for the Nikkei 225 Futures Trade[J]. Journal of Empirical Finance, 2001,8(3):219-242
[16] Lee S. T., Lee D. J., Oh H. S. Technological Forecasting at the Korean Stock Market: A Dynamic Competition Analysis Using the Lotka-Volterra Model[J]. Technological Forecasting and Social Change, 2005,72(4):1044-1057
[17] Kloppers P. H., Greeff J. C. Lotka-Volterra Model Parameter Estimation Using Experiential Data[J]. Applied Mathematics and Computation, 2013,224(1):817-825
[18] Marasco A., Picucci A., Romano A. Market Share Dynamics Using Lotka-Volterra Models[J]. Technological Forecasting & Social Change, 2016,105:49-62
[19] 孔东民. Lotka-Volterra系统下市场结构的演进[J]. 管理工程学报, 2005,19(3):77-81
[20] 熊熊,张维,李帅,等. 基于Lotka-Volterra模型的股指期货市场竞争分析[J]. 系统工程学报, 2009,24(5):581-588
[21] 姚亚伟,廖士光. 股指期货与股票现货市场竞争关系研究——来自中国的经验证据[J]. 证券市场导报, 2011,(9):43-50
[22] 曹广喜,徐龙炳. 香港和内地证券市场的动态竞争关系研究——基于A+H交叉上市公司的实证分析[J]. 财经研究, 2011,37(9):38-48
[23] 廖士光,朱伟骅,徐辉. 创业板市场与主板市场关系研究——来自沪深证券市场的经验证据[J]. 财经研究, 2014,40(6):27-37
[24] 李建勇,彭维瀚,刘天晖. 我国多层次场内股票市场板块互动关系研究——种间关系的视角[J]. 金融研究, 2016,(5):82-96
[25] 林祥友,胡双,彭滟茹,等. 沪港通对沪深港股市竞争关系的影响研究[J]. 软科学, 2017,31(5):140-144
[26] 林祥友,甘雨婕. 基于高频数据的股指期货动态价量关系研究[J]. 经济与管理评论, 2014,(2):121-129
[27] 代宏霞,臧旻,林祥友. 股指期货不同交易类型对价格波动影响的差异性研究[J]. 投资研究, 2013,32(6):105-114
[28] 黄健柏,程慧,郭尧琦,等. 金属期货量价关系的多重分形特征研究——基于MF-DCCA方法[J]. 管理评论, 2013,25(4):77-85
[29] 龚旭,文凤华,黄创霞,等. HAR-RV-EMD-J模型及其对金融资产波动率的预测研究[J]. 管理评论, 2017,29(1):19-32
[30] Gonzalo J., Granger C. Estimation of Common Long-Memory Components in Cointegrated Systems[J]. Journal of Business & Economic Statistics, 1995,13(1):27-35
[31] Hasbrouck J. One Security, Many Markets: Determining the Contributions to Price Discovery[J]. Journal of Finance, 1995,50(4):1175-1199
[32] Baillie R. T., Booth G. G., Tse Y., et al. Price Discovery and Common Factors Models[J]. Journal of Financial Markets, 2002,5(3):309-321
[2] 华仁海,刘庆富. 股指期货与股指现货市场间的价格发现能力探究[J]. 数量经济技术经济研究, 2010,(10):90-100
[3] 何诚颖,张龙斌,陈薇. 基于高频数据的沪深300指数期货价格发现能力研究[J]. 数量经济技术经济研究, 2011,(5):139-151
[4] 刘向丽,张雨萌. 基于向量误差修正模型的股指期货价格发现功能研究[J]. 管理评论, 2012,24(2):71-77
[5] 严敏,巴曙松,吴博. 我国股指期货市场的价格发现与波动溢出效应[J]. 系统工程, 2009,27(10):32-38
[6] 刘庆富,华仁海. 中国股指期货与股票现货市场之间的风险传递效应研究[J]. 统计研究, 2011,28(11):84-90
[7] 方匡南,蔡振忠. 我国股指期货价格发现功能研究[J]. 统计研究, 2012,29(5):73-78
[8] Yang J., Yang Z., Zhou Y. Intraday Price Discovery and Volatility Transmission in Stock Index and Stock Index Futures Markets: Evidence from China[J]. Journal of Futures Markets, 2012,32(2):99-121
[9] 代宏霞,林祥友. 股指期货合约存续期价格引导关系的时变性研究[J]. 投资研究, 2012,31(5):127-140
[10] 李政,卜林,郝毅. 我国股指期货价格发现功能的再探讨——来自三个上市品种的经验证据[J]. 财贸经济, 2016,37(7):79-93
[11] Lotka A. J. Elements of Physical Biology[M]. Philadelphia: William & Wilkins Companies, 1925
[12] Volterra V. Variazioni e Fluttuazioni Del Numero Dindividui in Specie Animali Conviventi. Mem[J]. R. Accad. Naz. dei. Lincei, 1926,6(2):31-113
[13] Modis T. Technological Forecasting at the Stock Market[J]. Technological Forecasting and Social Change, 1999,62(3):173-202
[14] Farmer J. D. A Simple Model for the Nonequilibrium Dynamics and Evolution of a Financial Market[J]. International Journal of Theoretical and Applied Finance, 2000,3(3):425-441
[15] Ito T., Lin W. L. Race to the Center: Competition for the Nikkei 225 Futures Trade[J]. Journal of Empirical Finance, 2001,8(3):219-242
[16] Lee S. T., Lee D. J., Oh H. S. Technological Forecasting at the Korean Stock Market: A Dynamic Competition Analysis Using the Lotka-Volterra Model[J]. Technological Forecasting and Social Change, 2005,72(4):1044-1057
[17] Kloppers P. H., Greeff J. C. Lotka-Volterra Model Parameter Estimation Using Experiential Data[J]. Applied Mathematics and Computation, 2013,224(1):817-825
[18] Marasco A., Picucci A., Romano A. Market Share Dynamics Using Lotka-Volterra Models[J]. Technological Forecasting & Social Change, 2016,105:49-62
[19] 孔东民. Lotka-Volterra系统下市场结构的演进[J]. 管理工程学报, 2005,19(3):77-81
[20] 熊熊,张维,李帅,等. 基于Lotka-Volterra模型的股指期货市场竞争分析[J]. 系统工程学报, 2009,24(5):581-588
[21] 姚亚伟,廖士光. 股指期货与股票现货市场竞争关系研究——来自中国的经验证据[J]. 证券市场导报, 2011,(9):43-50
[22] 曹广喜,徐龙炳. 香港和内地证券市场的动态竞争关系研究——基于A+H交叉上市公司的实证分析[J]. 财经研究, 2011,37(9):38-48
[23] 廖士光,朱伟骅,徐辉. 创业板市场与主板市场关系研究——来自沪深证券市场的经验证据[J]. 财经研究, 2014,40(6):27-37
[24] 李建勇,彭维瀚,刘天晖. 我国多层次场内股票市场板块互动关系研究——种间关系的视角[J]. 金融研究, 2016,(5):82-96
[25] 林祥友,胡双,彭滟茹,等. 沪港通对沪深港股市竞争关系的影响研究[J]. 软科学, 2017,31(5):140-144
[26] 林祥友,甘雨婕. 基于高频数据的股指期货动态价量关系研究[J]. 经济与管理评论, 2014,(2):121-129
[27] 代宏霞,臧旻,林祥友. 股指期货不同交易类型对价格波动影响的差异性研究[J]. 投资研究, 2013,32(6):105-114
[28] 黄健柏,程慧,郭尧琦,等. 金属期货量价关系的多重分形特征研究——基于MF-DCCA方法[J]. 管理评论, 2013,25(4):77-85
[29] 龚旭,文凤华,黄创霞,等. HAR-RV-EMD-J模型及其对金融资产波动率的预测研究[J]. 管理评论, 2017,29(1):19-32
[30] Gonzalo J., Granger C. Estimation of Common Long-Memory Components in Cointegrated Systems[J]. Journal of Business & Economic Statistics, 1995,13(1):27-35
[31] Hasbrouck J. One Security, Many Markets: Determining the Contributions to Price Discovery[J]. Journal of Finance, 1995,50(4):1175-1199
[32] Baillie R. T., Booth G. G., Tse Y., et al. Price Discovery and Common Factors Models[J]. Journal of Financial Markets, 2002,5(3):309-321