沪港通能否促进A股与香港股票市场一体化

doi:10.16381/j.cnki.issn1003-207x.2016.11.001

Abstract

Abstract: Shanghai-Hong Kong Stock Connect is a cross-boundary investment channel under which investors in each market are able to trade shares on the other market using their local brokers and clearing houses. After the successful launching of the Shanghai-Hong Kong Stock Connect program in 2014, it is expected Shenzhen-Hong Kong Stock Connect program will be launched in 2016 according to Government Work Report 2015 and 2016. Does Shanghai-Hong Kong Stock Connect enhance stock market integration between Hong Kong and Mainland China? What predictions does it have on the launch of the Shenzhen-Hong Kong Stock Connect and How to promote stock market integration between Hong Kong and Mainland China? Differ from previous research, the stock market segmentation and integration is studied from a micro perspective focusing on the dynamics of the cross listed companies' A and H shares' prices. Under the assumption of efficient market, arbitrage will eliminate unreasonable gaps thus the stock prices (or return) of identical companies are apt to converge. If not, arbitrage opportunities arise and arbitrage capitalizes on unreasonable gaps while eliminate them and provide liquidity. The daily close prices of all together 84 cross listed companies from 2014 April, 10^th to 2016 March, 25^th are collected as our sample and the H shares' prices are adjusted to RMB prices by the exchange rate of identical day. The evolvement of price discrepancy between the cross listed A and H shares is first studied to analyze the impact of Shanghai-Hong Kong stock connect on market integration with application of transition model and log t test. And then arbitrage strategy between cross listed A and H shares is proposed to improve stock market integration by constructing arbitrage free interval with application of value at risk (VaR) model based on the assumption that gaps follow generalized Pareto distribution (GPD) and test its profitability.Empirical result shows that convergence between the cross listed A and H shares vary among individual companies and time, only 7 pairs of A and H shares of identical firm converge across the whole sample period, moreover there is no sign of uptrend as the number declines from 24 in 2014 to 8 in 2016 Q1. Therefore the integration level is unsatisfactory and Shanghai-Hong Kong Stock Connect has not fully functioned. However, arbitrage can contribute to market integration and the strategy produces significant average return of 3.64% for one week and 6.59% for two weeks. Out of sample test verifies the aforementioned findings and shows the strategy is effective and profitable. In this paper the evolvement of the Shanghai Hong Kong Stock Connect's policy effect is systematically studied. Besides, feasible strategy by arbitrage between cross listed stocks which facilitates market participants' spontaneous trading against unreasonable gaps as a solution of market segmentation is proposed. And our research provides enlightenment and empirical reference for Shenzhen-Hong Kong Stock Connect.

Key words: Shanghai-Hong Kong Stock Connect, stock market integration, log t test, GPD, VaR

CLC Number:

F830.9

YAN Hong-lei, ZHAO Sheng-min. Does Shanghai-Hong Kong Stock Connect Promote Integration between Hong Kong and Mainland China's stock market[J]. Chinese Journal of Management Science, 2016, 24(11): 1-10.

References

[1] Stulz R, Wasserfallen W, Foreign equity investment restrictions and shareholder wealth maximization: Theory and evidence[J]. Review of Financial Studies, 1995, 8(4):1019-1057.

[2] Grossman S, Stiglitz J. On the impossibility of informationally efficient markets[J]. American Economic Review, 1980, 70(3):393-408.

[3] Chakravarty S, Sarkar A, Wu Lifan. Information asymmetry, market segmentation and the pricing of cross-listed shares: Theory and evidence from Chinese A and B shares[J]. Journal of International Financial Markets, Institutions and Money, 1998, 8(3-4): 325-356.

[4] Chan K, Menkveld A, Yang Zhishu. Information asymmetry and asset prices: Evidence from the China foreign share discount[J]. Journal of Finance, 2008, 63(1):159-196.

[5] Bailey W, Jagtiani J, Foreign ownership restrictions and stock prices in the Thai capital market[J]. Journal of Financial Economics, 1994, 36 (1): 57-87.

[6] Wang S S, Jiang Li. Location of trade, ownership restrictions and market illiquidity: Examining Chinese A and H shares[J]. Journal of Banking and Finance, 2004, 28(6): 1273-1297.

[7] Geert B, Campbell H, Christian L. Liquidity and expected returns: Lessons from emerging markets[J]. Review of Financial Studies, 2007, 20(6):1783-1831.

[8] Mitchell M, Pulvino T, Stafford, E. Limited arbitrage in equity markets[J].The Journal of Finance, 2002, 57(2): 551-584.

[9] Bris A, Goetzmann W N, Zhu Ning. Efficiency and the bear: Short sales and markets around the world[J].The Journal of Finance, 2007, 62(3):1029-1079.

[10] Gagnon L, Karolyi A. Multi-market trading and arbitrage[J]. Journal of Financial Economics, 2010, 97(1): 53-80.

[11] 刘荣茂,刘恒昕. 沪港通对沪市股票市场有效性的影响[J]. 经济与管理研究,2015,36(8):54-62.

[12] 杨瑞杰,张向丽. 沪港通对大陆、香港股票市场波动溢出的影响研究—基于沪深300指数、恒生指数高频数据[J]. 金融经济学研究,2015,30(6):49-59.

[13] 陈学胜,覃家琦. 交叉上市股票价格发现能力差异及交易信息含量测度[J].中国管理科学,2013,21(2):9-16.

[14] 游家兴,郑挺国. 中国与世界金融市场从分割走向整合—基于DCC-MGARCH模型的检验[J]. 数量经济技术经济研究,2009,12:96-108.

[15] 郭彦峰,黄登仕,魏宇,等. A+H交叉上市股票间信息传递的不对称性研究[J]. 中国管理科学,2010,18(3):10-16.

[16] 张信东,赵芳. 沪、深股票市场与香港股票市场的溢出效应—基于发布港股直通车方案前后的比较分析[J]. 南开管理评论,2009,12(4):99-106.

[17] 仪垂林,张翠玉. 次贷危机前后中国内地与亚洲主要股票市场联动性分析[J]. 产业经济研究,2010,(5):79-86.

[18] 李晓广,张岩贵. 我国股票市场与国际市场的联动性研究—对次贷危机时期样本的分析[J]. 国际金融研究,2008,(11):75-80.

[19] 曾志坚,徐迪,谢赤. 金融危机影响下证券市场联动效应研究[J]. 管理评论, 2009,21(2): 33-39.

[20] 吴吉林,操君. 中国A、B、H股间市场一体化进程研究—基于SKEWED-T-GJR-COPULA方法的实证检验[J]. 南方经济,2011,(5):43-53.

[21] 何光辉,杨咸月,陈诗一.入世以来中国证券市场动态国际一体化研究[J]. 经济研究,2012,(10):82-96.

[22] 李红权,洪永淼,汪寿阳. 我国A股市场与美股、港股的互动关系研究:基于信息溢出视角[J]. 经济研究,2011,(8):15-25.

[23] 鲁旭,赵迎迎. 沪深港股市动态联动性研究—基于三元VAR-GJR-GARCH-DCC的新证据[J]. 经济评论,2012,(1):97-107.

[24] 易荣华,鞠瑾,刘家鹏. 基于交叉上市股票的市场估值效率测度与估值模式分析[J]. 中国管理科学,2016,24(1):30-37.

[25] 陈学胜,周爱民. 交叉上市股票价格发现及贡献差异的横截面分析[J]. 中国管理科学,2009,17(2):21-28.

[26] Grammig J, Melvin M, Schlag C. Internationally cross-listed stock prices during overlapping trading hours: price discovery and exchange rate effects[J]. Journal of Empirical Finance, 2005, 12(1):139-164.

[27] 胡伦超,余乐安,汤铃. 融资融券背景下证券配对交易策略研究—基于协整和距离的两阶段方法[J]. 中国管理科学,2016,24(4):1-9.

[28] Phillips P C B, Sul D. Transition modeling and econometric convergence tests[J]. Econometrica, 2007,75(6): 1771-1855.

[29] 赵胜民,闫红蕾. 内地与香港股票市场的一体化进程研究[J]. 中国经济问题, 2016, (1):124-135.

[30] Sheleifer A. Inefficienct markets: An introduction to behavioral finance[J].Journal of Institutional and Thearetical Economics,2002,158(2):369-373.

[31] De Jong A, Rosenthal L, Van Dijk M A. The risk and return of arbitrage in dual-listed companies[J]. Review of Finance, 2009, 13(3): 495-520.

[32] Schultz P, Shive S. Mispricing of dual-class shares: Profit opportunities, arbitrage, and trading[J].Journal of Financial Economics,2010,98(3):524-549.

[33] 黄金波,李仲飞,周鸿涛. 期望效用视角下的风险对冲效率[J]. 中国管理科学,2016,24(3):9-17.

[34] Embrechts P, Kluppelberg C, Mikosch T. Modeling extremal events for insurance and finance[M]. 1997, Berlin:Springer.

Does Shanghai-Hong Kong Stock Connect Promote Integration between Hong Kong and Mainland China's stock market

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