In this paper, the arbitrage process between stock index futures and spot based on the ETF portfolio is studied through high frequency data. In consideration of the transaction cost through the 12 futures contract in 2013 with 5 minutes high frequency data, the model of ETF arbitrage fund portfolio stock index futures is constructed using no arbitrage interval analysis method. It is found that the reverse arbitrage opportunity of China's stock index futures market is less than positive arbitrage opportunity. The mispricing rate in the process of spot and future arbitrage is high and shows non-equilibrium. Moreover, the introduction of margin trading inhibits the arbitrage behavior. Due to a higher cost rate of margin trading, which also makes arbitrage-free interval of margin trading investor expand, it gets the positive arbitrage opportunity greater than the positive arbitrage. As the transaction time of the quarter-month contract is relatively long, so its arbitrage opportunity is far more than other types of index futures. But continuous positive arbitrage opportunity of the quarter-month contract is more than the other types of contracts, and the continuous reverse arbitrage opportunity of it is significantly fewer than other types of contract. Although the future delivery date price limitation is of 20% by the trading rules, it is found that in the delivery day, total number of over boundary of arbitrage-free interval, longest duration of over boundary, average mispricing rate, return of instantaneous arbitrage, continuous arbitrage opportunity of two types investors are almost same. For the delivery day, the volatility degree of index future price last two hours is significantly lower than the volatility of two hours before, it is because that the final delivery price of future is confirmed by average stock index futures price of last two hours, which makes the price stabilize. This study is helpful to improve the efficiency of stock index futures arbitrage trading to a certain extent.
WANG Liang, QIN Long-hao, LIU Xiao, CHEN Jie
. A Study on Stock Index Futures Arbitrage by ETF with High Frequency Data[J]. Chinese Journal of Management Science, 2018
, 26(5)
: 9
-20
.
DOI: 10.16381/j.cnki.issn1003-207x.2018.05.002
[1] Ekholm A G. Portfolio returns and manager activity:How to decompose tracking error into security selection and market timing?[J].Journal of Empirical Finance, 2012, 19(3):349-358.
[2] Tu A H, Hsieh W L G, Wu W S. Market uncertainty, expected volatility and the mispricing of S&P 500 index futures[J]. Journal of Empirical Finance, 2016,35(1):78-98.
[3] Ross S A. The arbitrage theory of capital asset pricing[J]. Journal of Economic Theory, 1990, 13(3):341-360.
[4] Kim B H, Chun S E, Min H G, Nonlinear dynamics in arbitrage of the S&P 500 index and futures:A threshold error-correction model[J]. Economic Modelling, 2010,27(2):566-573.
[5] Delong 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.
[6] Fung A K W,Lam K.Overreaction of index futures in Hong Kong[J].Journal of Empirical Finance,2004, 11(3):331-351.
[7] Alsayed H,McGroarty F.Ultra-High-Frequency algorithmic arbitrage across international index futures[J]. Journal of Forecasting,2014,33(6):391-408.
[8] McMillan D G, Vlkü N. Persistent mispricing in a recently opened emerging index futures market:Arbitrageurs invited[J]. Journal of Futures Markets, 2009, 29(3):218-243.
[9] Bialkowski J, Jakubowski J. Stock index futures arbitrage in emerging markets:Polish evidence[J]. International Review of Financial Analysis, 2008, 17(2):363-381.
[10] Brenner M, Subrahmanyam M G, Uno J. Stock index futures arbitrage in the Japanese markets[J].Japan and the World Economy, 1989, 1(3):303-330.
[11] Tsaih R, Hsu Y Lai CC.Forecasting S&P 500 stock index futures with a hybrid AI system[J].Decision Support Systems, 1998, 23(2):161-174.
[12] 曹栋,张佳. 基于GARCH-M模型的股指期货对股市波动影响的研究[J]. 中国管理科学, 2017,25(1):27-34.
[13] 简志宏,曾裕峰,刘曦腾. 基于CAViaR模型的沪深300股指期货隔夜风险研究[J]. 中国管理科学,2016,24(9):1-10.
[14] 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.
[15] 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.
[16] 张健,方兆本. 股指期货套期保值模型选择[J]. 中国科学技术大学学报,2012,42(3):191-196.
[17] 魏卓,陈冲,魏先华. 基于高频数据的中国市场股指期货套利[J]. 系统工程理论与实践,2012,32(3):476-482.
[18] 刘伟,陈敏,梁斌. 基于金融高频数据的ETF套利分析[J]. 中国管理科学,2009,17(2):1-7.
[19] 刘岚,马超群. 中国股指期货市场期现套利及定价效率研究[J]. 管理科学学报,2013,16(3):41-52.