FIN  >> Vol. 7 No. 1 (January 2017)

    一种基于高频数据的汇市统计套利策略
    A Statistical Arbitrage Strategy in Forex Market Based on High-Frequency Data

  • 全文下载: PDF(2502KB) HTML   XML   PP.38-46   DOI: 10.12677/FIN.2017.71005  
  • 下载量: 2,341  浏览量: 3,044  

作者:  

肖敦健,夏冰倩:南京工业大学海外教育学院,江苏 南京;
邓晓卫:南京工业大学数理科学学院,江苏 南京

关键词:
统计套利高频数据协整模型汇市交易Statistical Arbitrage High-Frequency Data Cointegration Model Forex Trading

摘要:

资本市场中,套利交易是一种规避风险的重要交易方式。统计套利在近几十年来在国外资本市场十分盛行。而国内资本市场由于缺乏做空机制,统计套利等量化投资策略很难得以实现。而近年来随着融资融券和股指期货的推出,这一局面有所缓解,放开做空机制已是大势所趋。本文尝试采用统计套利的思想,利用协整模型,先在机制成熟的外汇市场进行套利检验,选取相关度较高的欧元/美元(EUR/USD)和瑞郎/日元(CHR/JPY)两个货币对在2015年5月28日20时至5月29日20时每分钟收盘价的价差时间序列进行实证研究,发现日内存在大量的套利机会,从而为今后投资者提供了一种新颖的量化投资思路。

In capital market, arbitrage is an essential trading method to avoid risks. Statistical arbitrage, which is a genre of arbitrage, has been widely utilized by foreign financial institutions since several decades ago. Since lacking of Short Hedge Mechanism, statistical arbitrage can hardly be realized in domestic capital markets. However, the situation is being relieved with the introduction of margin trading and stock index futures. And the trend to set up Short Hedge Mechanism is overwhelming. In this dissertation, we tested the arbitrage chances in forex market by adopting the thought of statistical arbitrage, combining with cointegration modeling and every minute’s closing rate of EUR/USD and CHR/JPY, which are highly correlated with each other, within 24 hours. After studying in the time series of price difference, we found that there were abundant opportunities for arbitrage. Hence, we are able to give out a novel quantified path for investors in the future.

文章引用:
肖敦健, 邓晓卫, 夏冰倩. 一种基于高频数据的汇市统计套利策略[J]. 金融, 2017, 7(1): 38-46. http://dx.doi.org/10.12677/FIN.2017.71005

参考文献

[1] Levich, R.M. (2001) International Financial Markets. McGraw-Hill/Irwin, New York, 141-152.
[2] Bolland, P.J. and Connor, J.T. (1998) A Robust Non-Linear Multivariate Kalman Filter for Arbitrage Identification in High Frequency Data. Neural Networks in Financial Engineering, 122-135.
[3] Engle, R.F. and Granger, C.W.J. (1987) Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55, 251-276.
https://doi.org/10.2307/1913236
[4] Burgess, N. (2000) Statistical Arbitrage Models of the FTSE 100. Computational Finance, The MIT Press, Cambridge, 297-312.
[5] Rudy, J., Dunis, C., Giorgioni, G. and Laws, J. (2010) Statistical Arbitrage and High-Frequency Data with an Application to Eurostoxx 50 Equities. Social Science Electronic Publishing, Rochester.
[6] 方昊. 统计套利的理论模式及应用分析: 基于中国封闭式基金市场的检验[J]. 统计与决策, 2005(12): 14-16.
[7] 仇中群, 程希骏. 基于协整的股指期货跨期套利模型[J]. 系统工程, 2008(12): 26-29.
[8] 叶恒洁. 基于协整的统计套利实证研究[J]. 中国商贸, 2009(13): 238-238.
[9] Hogan, S., Jarrow, R., Teo, M. and Warachka, M. (2004) Testing Market Efficiency Using Statistical Arbitrage with Applications to Momentum and Value Strategies. Journal of Financial Economics, 73, 525-565.
https://doi.org/10.1016/j.jfineco.2003.10.004
[10] Vidyamurthy, G. (2004) Pairs Trading: Quantitative Methods and Analysis. Pearson Schweiz Ag, Zug, 35-47.