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Taking the CSI 500 stock index futures as the research object, the regression model of dummy variables of five indicators, including high-frequency return rate, volume change rate and near and far month contract price, was established. Then test whether these five indicators are affected by intraday effect and carry out statistical arbitrage based on intraday effect of spread. The empirical results show that the CSI 500 stock index futures have obvious intraday price fluctuations within 15 minutes of opening, and the intraday effect of the near-month contract is more significant than the far-month contract. The arbitrage strategy based on the intraday effect of spreading all the sample of both inside and outside can achieve higher success rate and yield, which is suitable for the short-term arbitrage. In actual trading, given the known probability of intraday profit, the intraday arbitrage method can provide reference for trading operation and risk aversion, so as to avoid losses caused by missed arbitrage opportunities.

The intraday effect is a kind of calendar effect. The calendar effect refers to the market vision related to date and time. Its universal existence has been confirmed by numerous scholars, which has a certain impact on financial market investment and academic research. According to the research ideas and methods of experts and scholars, there are roughly three types of tests for the intraday effect. The first category, descriptive statistics, some scholars based on the time series of financial variables, as well as skewness, kurtosis, normality, fluctuation interval and mean and other factors to analyze the existence of intraday effect or not. For example, Wu Gang-hui (2014) divided the trading time into 18 time periods and calculated the average value of six indicators such as volume, depth, absolute spread and relative spread in each time period to discuss the intraday effect characteristics [

Statistical arbitrage refers to the use of quantitative analysis methods to build a portfolio based on statistical correlation theory, independent of economic theory, to achieve a certain degree of risk aversion, and obtain stable excess returns. The key to statistical arbitrage is the choice of arbitrage strategy. The strategy of statistical arbitrage is very rich, mainly including arbitrage strategy based on co-integration, strategy based on neural network model and strategy based on principal component analysis. The strategy based on co-integration is widely used and widely studied. For example, Hao Ning (2016) uses statistical arbitrage to establish a long-term equilibrium co-integration equation to analyze the arbitrage space of CSI 500 stock index futures [

The CSI 500 stock index futures is a futures contract with the CSI 500 Index as the subject matter. Since its listing on April 16, 2015, it has been less than three years. At present, the domestic research on the CSI 500 stock index futures mainly focuses on price discovery and risk. Research was on transfer and other functions, impact on stock market stocks, spot market and statistical arbitrage. For example, Xu Jinjian (2016) introduced a virtual variable GARCH model to empirically analyze the impact of stock index futures on the index volatility [

According to the sample selection method of the CSI 500 Index, the CSI 500 stock index futures with the CSI 500 Index as the target can reflect the development status of the small and medium-sized companies. Secondly, since the listing time has only been three years, there is relatively little research on the CSI 500 stock index futures. Therefore, it is of great significance to conduct the intraday effect and statistical arbitrage research on the intraday trading data for the CSI 500 stock index futures. I hope that through the empirical research of this article, the financial investors will be referred to the investment information and expand the research scope of the CSI 500 stock index futures.

Calendar effects are a kind of market vision. In financial markets, the cyclical trend of financial indicators such as volatility, trading volume, and trading frequency is related to the date, including seasonal effects, monthly effects, and intra-week effects, due date effect, and so on [

Specifically, the seasonal effect refers to a phenomenon in which abnormal changes in sequence objects are related to seasonal factors. The week effect, also known as the intra-week effect, means that the change in the economic indicator on a certain day of the week is significantly different from the other days. Similar to the week effect, there is also a workday effect. Obviously, the workday effect, that is, the sequence object, differs between weekdays and non-workdays. The maturity effect, also known as the delivery date effect, refers to the abnormal fluctuation of the yield, volatility or volume of the underlying index on the settlement date of the financial product [

This paper uses the virtual variable regression method to test the intraday effect of the CSI 500 stock index futures trading data indicators. To prevent multicollinearity, a session dummy variable is set every 5 minutes. Intraday trading 4 hours for a total of 48 5 minute periods, reducing the total number of dummy variables by one, excluding the last 5 minutes. That is to say, 47 dummy variables and constant terms are introduced in the regression model for research.

In order to prevent pseudo-regression and ensure the reliability of parameter test and estimation results, the stationary test, autocorrelation test and heteroscedasticity test should be performed on the test variables respectively. Then select the appropriate model based on the test results. When the sequence satisfies smoothness, no autocorrelation, and heteroscedasticity, select the OLS model with dummy variables, i.e.:

X t = α 0 + α 1 h 1 + α 2 h 2 + ⋯ + α 46 h 46 + α 47 h 47 + ε t (1)

where, X t represents the variable of intraday effect test, α i , i = 0 , 1 , 2 , ⋯ 47 represents constant terms and coefficients, h i is a dummy variable, ε t is a random error term. In order to prevent the occurrence of multicollinearity, the dummy variable for testing the intraday effect is one less than the actual one, that is, the first 5 minute period in each trading day is not included. When at 9:30-9:35, h 1 = 1 h 2 = h 3 = ⋯ = h 47 = 0 . When at 9:35 - 9:40, h 2 = 1 h 1 = h 3 = ⋯ = h 47 = 0 . Other definitions and so on. When h 1 = h 2 = h 3 = ⋯ = h 47 = 0 , the significance of the constant term is the significance of the last 5 minutes period.

When the sequence satisfies smoothness, no autocorrelation, but heteroscedasticity, choose the GARCH model with dummy variables.

{ X t = α 0 + α 1 h 1 + α 2 h 2 + ⋯ + α 47 h 47 + ε t , σ t 2 = β 0 + ∑ i = 1 q β i ε t − i 2 + ∑ i = 1 p β i σ t − i 2 , (2)

where, the above equations are mean equation and conditional variance equation with dummy variables respectively. X t is the variable of intraday effect test, α i , β i represents constant term and variable coefficient, ε t is a random error term. h i , i = 1 , 2 , ⋯ , 47 is the dummy variable corresponding to the 5-minute time period of the day.

When the sequence is stable and there is autocorrelation, but there is no heteroscedasticity, choose the ARMA model with dummy variables, namely:

{ f ( t ) = ϕ 0 + ϕ 1 X t − 1 + ⋯ + ϕ p X t − p + ε t − θ 1 ε t − 1 − ⋯ − θ q ε t − q X t = f ( t ) + α 0 + α 1 h 1 + α 2 h 2 + ⋯ + α 47 h 47 + e t (3)

where, f ( t ) represents the ARMA(p,q) model, p is the autoregressive order, q is the moving average order, ϕ i ( i = 1 , 2 , ⋯ , p ) and θ i ( i = 1 , 2 , ⋯ , q ) represent the coefficients of the autoregressive term and the moving average term, respectively. Other symbols have the same meaning as above.

When the sequence is stable and has autocorrelation and heteroscedasticity, ARMA-GARCH model with dummy variables is selected, that is:

{ X t = f ( t ) + α 0 + α 1 h 1 + α 2 h 2 + ⋯ + α 47 h 47 + ε t σ t 2 = β 0 + ∑ i = 1 q β i ε t − i 2 + ∑ i = 1 p β i σ t − i 2 (4)

where, f ( t ) represents the ARMA model, the above equations are the mean value equation and conditional variance equation with dummy variables and ARMA model added. Other symbols have the same meaning as above. When the sequence is not stable, the differential processing is performed first, and then the autocorrelation and heteroscedasticity are performed under the condition that the smoothness is satisfied, and then the appropriate model category is selected.

In the existing literature, statistical arbitrage mainly uses setting thresholds for closing positions and closing thresholds. Arbitrage based on the predicted standard residual sequence, the accuracy of the model prediction and the operating speed of the actual transaction affect the possibility and size of the arbitrage. According to the intraday effect test of the spread, there is a significant fluctuation in the spread during the intraday effect period. If the position can be closed by a known time difference, the arbitrage success rate and profitability can be increased. Combine this idea with the actual establishment of arbitrage mechanism; the main trading strategies are as follows.

Arbitrage is carried out according to the intraday period of the intra-sample spread and the coefficient of the corresponding dummy variable, when the significant dummy variable coefficient in the model is positive, carry out forward arbitrage for the intraday period corresponding to this variable. Start a position at the beginning, that is, buy a near-month contract, sell a distant month contract, and close the position at the end, that is, sell the near-month contract and buy the far-month contract. Assume that the spread between the near and far-month contracts IC1709 and IC1712 detects the intraday effect at 9:55 - 10:00 am, and the dummy variable coefficient is positive. Then, according to the intraday effect arbitrage strategy, the forward transaction is carried out, and the position is opened at 9:55, and the position is closed at 10:00. In actual operation, the arbitrage effect may be poor, and the arbitrage plan needs to be corrected. The specific measures are as follows:

1) Select the best intraday effect test model in the sample. According to the period and the variable coefficient of the significant dummy variables in the model, the arbitrage of the near and far-month contract closing price data in the sample is arbitrarily, and the success rate and total success rate and profitability of each arbitrage interval are counted;

2) When there are more significant variables, that is, there are many arbitrage opportunities in the day, and there are adjacent arbitrage intervals in the same arbitrage direction, try to merge the adjacent arbitrage periods, reduce the cost of handling fees, and observe whether the arbitrage effect is improved;

3) When the success rate of some arbitrage intervals is not high and the profit of the arbitrage interval is low, delete these arbitrage opportunities.

This paper establishes a virtual variable model to explore whether the intraday trading of CSI 500 stock index futures has an intraday effect. If it exists, build a statistical arbitrage strategy on this basis. The modeling steps are as follows:

1) Data preprocessing. Using the 5-minute data of the day to generate logarithmic rate of return (high-frequency yield), volume change rate, volume change rate, price fluctuation range, and near-distance monthly contract price difference, and 47 time-varying dummy variables;

2) Stationarity, autocorrelation and heteroscedasticity test. Performing stationarity tests on five test variables of near and far-month contracts in different samples, and smoothing non-stationary sequences; Secondly, autocorrelation test and ARCH effect test are respectively carried out;

3) Selection test model. Select the appropriate test model based on the results of stationarity, autocorrelation and ARCH effect test;

4) Model fitting and test result analysis. Construct a model for the test variables separately, and judge the intraday effect form according to the parameter significance analysis of the dummy variables in the fitted optimal model;

5) Build statistical arbitrage strategies and implementation fixes. According to the intraday effect of the price difference test, the corresponding statistical arbitrage strategy is constructed. The data in the sample was used for verification, and the arbitrage interval with unsatisfactory results was modified.

In order to understand the intraday effect of CSI 500 stock index futures more comprehensively and enhance the reliability of the results, from the perspective of sampling points and near and far-month contracts. Select IC1705 (near-month contract) and IC1706 (far-month contract) from April 7 to April 20, 2017 (10 trading days), and IC1709 (near-month contract) and IC1712 (far-month contract) in 2017 From July 7th to July 20th (10 trading days) as two comparison samples. A total of 3840 high-frequency 5-minute trading data were used for the intraday effect study. In the statistical arbitrage strategy study, the closing price of IC1709 (near-month contract) and IC1712 (far-month contract) from July 7 to July 20, 2017 (10 trading days) is used as sample data in 2017. The closing price of solstice on July 21, 2017 (5 trading days) is used as the out of sample forecast data. The data in this article comes from the access to the letter futures trading software. Example data is shown in

This paper chooses the high-frequency yield, trading volume, position, spread and price fluctuation of stock index futures as the research object. The price volatility is defined by the highest price and the lowest price. Taking the logarithm can make the data more stable, does not change the correlation between the data, and the logarithmic rate of return satisfies the additivity, which has better statistical characteristics than the simple rate of return, so the rate of return is in logarithmic form [

{ l r t = ( ln p t − ln p t − 1 ) × 100 d v o l t = v o l t − v o l t − 1 v o l t − 1 × 100 d o p e t = o p e t − o p e t − 1 o p e t − 1 × 100 j c t = p 1 t − p 2 t v p t = ln ( h p t ) − ln ( l p t ) (5)

where, l r t is logarithmic yield, d v o l t is the volume change rate, d o p e t is the rate of change in position, j c t represents the sequence of spreads, v p t represents the price fluctuation range; p t and p t − 1 are the closing price of stock index futures at t, t − 1 , v o l t and v o l t − 1 are the volume at t, t − 1 , o p e t and o p e t − 1 are the amount of positions at t, t − 1 , p 1 t represents the time t of the current month contract, p 2 t is the closing price at time t of the far month contract, h p t and l p t represent the highest and lowest prices at time t.

In this paper, we first test the stability, autocorrelation and heteroscedasticity of the five intraday effect test variables, and select the appropriate model based on the test results. The test results are shown in the table.

As shown in

Among them, there were 27 zero trading volumes in the far month contract IC1712 of July for CSI 500 stock index futures in 5 minutes, making it impossible for the sequence to test autocorrelation and ARCH effect. The zero trading volume distribution was analyzed independently. The specific distribution periods are shown in

According to the statistical analysis of the 5 minutes of zero trading volume distribution of IC1712 stock index futures within a day, it can be seen that two of the 27 times without trading are 10:35 - 10:40, 11:20 - 11:25 and 11:25 - 11:30, as well as 13:00 - 13:05, 13:15 - 13:20 and 14:20 - 14:25 in the afternoon. The periods with zero trading volume once are mainly distributed in the afternoon trading time, especially in the half hour from 13:25 to 13:55. To sum up, the probability of zero trading volume in IC1712 stock index futures within 10 minutes before the closing bell in the morning and 20 minutes before the opening bell in the afternoon is relatively high.

According to the test results in

date | time | closing price | |
---|---|---|---|

1 | 2017/4/7 | 935 | 6430 |

2 | 2017/4/7 | 940 | 6429 |

3 | 2017/4/7 | 945 | 6439.2 |

4 | 2017/4/7 | 950 | 6435 |

... | ... | ||

477 | 2017/4/20 | 1445 | 6288.8 |

478 | 2017/4/20 | 1450 | 6280.8 |

479 | 2017/4/20 | 1455 | 6281.2 |

480 | 2017/4/20 | 1500 | 6278.8 |

index | contract | stationarity test | autocorrelation test | ARCH effect test | index | contract | stationarity test | autocorrelation test | ARCH effect test |
---|---|---|---|---|---|---|---|---|---|

logarithmic yield | IC1705 | 0.01 | 0.5847 | 3.55E−05 | position change rate | IC1705 | 0.01 | <2.2E−16 | <0.2E−16 |

IC1706 | 0.01 | 0.2688 | 0.002402 | IC1706 | 0.01 | <2.2E−16 | 1.05E−07 | ||

IC1709 | 0.01 | 0.000883 | 7.06E−05 | IC1709 | 0.01 | <2.2E−16 | 0.01792 | ||

IC1712 | 0.01 | 0.2436 | 3.11E−12 | IC1712 | 0.01 | 4.78E−13 | 2.53E−11 | ||

volume change rate | IC1705 | 0.01 | 7.64E−06 | 0.5234 | price fluctuation | IC1705 | 0.01 | 2.00E−13 | 1.57E−10 |

IC1706 | 0.01 | 1.19E−09 | 0.3437 | IC1706 | 0.01 | 1.00E−11 | 8.61E−12 | ||

IC1709 | 0.01 | 6.23E−11 | 0.6447 | IC1709 | 0.01 | <2.2E−16 | <0.2E−16 | ||

IC1712 | 0.01 | NA | NA | IC1712 | 0.01 | <2.2E−16 | <0.2E−16 | ||

spread | April | 0.06839 | <2.2E−16 | <2.2E−16 | spread | July | 0.4484 | <2.2E−16 | <0.2E−16 |

times | periods of time | |||||
---|---|---|---|---|---|---|

1 | 10:00 - 10:05 | 10:40 - 10:45 | 10:45 - 10:50 | 11:00 - 11:05 | 11:15 - 11:20 | |

13:10 - 13:15 | 13:25 - 13:30 | 13:35 - 13:40 | 13:40 - 13:45 | 13:45 - 13:50 | ||

13:05 - 13:10 | 14:00 - 14:05 | 13:50 - 13:55 | 14:10 - 14:15 | 14:50 - 14:55 | ||

2 | 10:35 - 10:40 | 11:20 - 11:25 | 11:25 - 11:30 | 13:00 - 13:05 | 13:15 - 13:20 | 14:20 - 14:25 |

index | contract | model category | index | contract | model category |
---|---|---|---|---|---|

logarithmic yield | IC1705 | GARCH | Position change rate | IC1705 | ARMA-GARCH |

IC1706 | GARCH | IC1706 | ARMA-GARCH | ||

IC1709 | ARMA-GARCH | IC1709 | ARMA-GARCH | ||

IC1712 | GARCH | IC1712 | ARMA-GARCH | ||

volume change rate | IC1705 | ARMA | Price fluctuation | IC1705 | ARMA-GARCH |

IC1706 | ARMA | IC1706 | ARMA-GARCH | ||

IC1709 | ARMA | IC1709 | ARMA-GARCH | ||

IC1712 | ―― | IC1712 | ARMA-GARCH | ||

spread | IC1705-IC1706 | ARIMA-GARCH | spread | IC1709-IC1712 | ARIMA-GARCH |

The preliminary model category in

contract | intraday effect period | coefficient | p | contract | intraday effect period | coefficient | p |
---|---|---|---|---|---|---|---|

IC1705 | 9:35 - 9:40 | −0.09922 | 0.00672 | IC1706 | 9:30 - 9:35 | 0.07836 | 0.046875 |

10:05 - 10:10 | −0.09322 | 0.008344 | 9:35 - 9:40 | −0.11553 | 0.003324 | ||

13:20 - 13:25 | −0.09174 | 0.012883 | 13:20 - 13:25 | −0.07697 | 0.038694 | ||

13:55 - 14:00 | 0.072919 | 0.070689 | 10:05 - 10:10 | −0.06285 | 0.077906 | ||

IC1709 | 9:30 - 9:35 | −0.13929 | 0.018972 | IC1712 | 9:55 - 10:00 | −0.10723 | 0.044437 |

10:20 - 10:25 | −0.06442 | 0.000138 | 10:30 - 10:35 | 0.103596 | 0.034577 | ||

10:40 - 10:45 | −0.22498 | 0.004304 | 14:40 - 14:45 | −0.09625 | 0.061231 | ||

10:45 - 10:50 | −0.12455 | 0.034558 | 14:45 - 14:50 | 0.115593 | 0.043447 | ||

11:25 - 11:30 | −0.12324 | 0.000179 | |||||

13:30 - 13:35 | −0.09796 | 0 |

contract | intraday effect period | coefficient | p | contract | intraday effect period | coefficient | p |
---|---|---|---|---|---|---|---|

IC1705 | 10:15 - 10:20 | −81.1126 | 0.0451 | IC1706 | 10:15 - 10:20 | −75.6245 | 0.0733 |

13:00 - 13:05 | −102.163 | 0.0116 | 11:15 - 11:20 | −86.9353 | 0.0394 | ||

13:05 - 13:10 | −69.1094 | 0.0879 | 13:00 - 13:05 | −115.709 | 0.0072 | ||

14:25 - 14:30 | −76.6795 | 0.0578 | 13:30 - 13:35 | −86.2141 | 0.0412 | ||

IC1709 | 11:00 - 11:05 | −109.441 | 0.0002 | 14:25 - 14:30 | −61.313 | 0.0373 | |

11:15 - 11:20 | −81.0439 | 0.0059 | 13:30 - 13:35 | −60.6029 | 0.0396 | ||

9:50 - 9:55 | −73.7594 | 0.0122 | 14:05 - 14:10 | −59.6402 | 0.0428 | ||

13:00 - 13:05 | −71.3882 | 0.0153 | 10:25 - 10:30 | −55.3037 | 0.0603 | ||

13:45 - 13:50 | −66.546 | 0.0238 | 11:25 - 11:30 | −53.1169 | 0.0712 |

object | period of significant intraday effect | |||||
---|---|---|---|---|---|---|

population | 9:30 - 9:35 | 9:35 - 9:40 | 9:40 - 9:45 | 9:45 - 9:50 | 9:50 - 9:55 | |

recent months contract | 9:30 - 9:35 | 9:35 - 9:40 | 9:40 - 9:45 | 9:45 - 9:50 | 9:50 - 9:55 | 9:55 - 10:00 |

10:00 - 10:05 | 10:05 - 10:10 | 10:30 - 10:35 | 11:20 - 11:25 | 13:00 - 13:05 | ||

far-month contract | 9:30 - 9:35 | 9:35 - 9:40 | 9:40 - 9:45 | 9:45 - 9:50 | 9:50 - 9:55 | 14:20 - 14:25 |

April sample | virtual variable | intraday effect period | coefficient | p | July sample | intraday effect period | coefficient | p |
---|---|---|---|---|---|---|---|---|

h_{35} | 13:50 - 13:55 | −5.248 | 0.001 | 13:50 - 13:55 | 5.6262 | 0.0288 | ||

h_{32} | 13:35 - 13:40 | −4.849 | 0.004 | 13:35 - 13:40 | 7.1880 | 0.0067 | ||

h_{40} | 14:15 - 14:20 | −4.606 | 0.004 | 14:15 - 14:20 | 4.8469 | 0.0405 | ||

h_{33} | 13:40 - 13:45 | −3.693 | 0.028 | 13:40 - 13:45 | 10.485 | 0.0000 | ||

h_{29} | 13:20 - 13:25 | −3.595 | 0.034 | 13:20 - 13:25 | 6.5375 | 0.0158 | ||

h_{41} | 14:20 - 14:25 | −3.204 | 0.041 | 14:20 - 14:25 | 5.3476 | 0.0207 | ||

h_{37} | 14:00 - 14:05 | −3.173 | 0.055 | 14:00 - 14:05 | 7.229 | 0.0038 |

object | period of significant intraday effect | ||||||
---|---|---|---|---|---|---|---|

population | 9:30 - 9:35 | 9:35 - 9:40 | 9:40 - 9:45 | ||||

recent months contract | 9:30 - 9:35 | 9:35 - 9:40 | 9:40 - 9:45 | 9:45 - 9:50 | 9:50 - 9:55 | 10:10 - 10:15 | 13:55 - 14:00 |

far-month contract | 9:30 - 9:35 | 9:35 - 9:40 | 9:40 - 9:45 | 9:45 - 9:50 | 10:05 - 10:10 | ||

April sample | 9:30 - 9:35 | 9:35 - 9:40 | 9:40 - 9:45 | 13:55 - 14:00 | |||

July sample | 9:30 - 9:35 | 9:35 - 9:40 | 9:40 - 9:45 | 9:45 - 9:50 | 9:50 - 9:55 | 9:55 - 10:00 | 10:00 - 10:05 |

The imaginary coefficient of the dummy variable and the corresponding parameter estimated in the above table indicate that the CSI 500 stock index futures yield does have an intraday effect. Different days, the degree of intraday effect is different. Among them, the near month IC1709 of the sample in July has a significant negative yield at 9:30 to 9:35, 10:40 to 10:45, 10:45 to 10:50 and 11:25 to 11:30, and the far month IC1712 has a significant positive yield at 10:30 to 10:35 and 14:45 to 14:50. The intraday effect of the front-month contract is significant during 13:30 - 13:35, and the yield is negative.

According to the test results, the volume change rate of the sample contract in the near month of July changed significantly in different periods of the day. In the April sample, when 10:15 - 10:20 and 13:00 - 13:05, there are intraday effect in the near and far month contracts; In general, the intraday effect of the front-month contract is significant between 13:00 - 13:05 and 14:25 - 14:30, and the coefficient of the corresponding dummy variable is negative, indicating a decrease in trading volume.

According to the test results, the price difference sequence has more time periods during which the fluctuation range is larger during the day. From _{35}, h_{32}, h_{40}, h_{33}, h_{29}, h_{41} and h_{37}. That is, the interval in which the intra-day effect exists is mainly distributed at 13:20 - 13:25, 13:35 - 13:45, 13:50 - 13:55, 14:00 - 14:05 and 14:15 - 14:25. Comparing the dummy variable coefficients, there is a negative spread in the April sample during the significant period of the intraday effect, while the July sample has a positive spread and the spread is more volatile.

As can be seen from

It is known from the above that the spread sequence has significant intraday fluctuations, so the direction and time of arbitrage are made according to the intraday effect test model of the coefficient of significant variables in the spread sequence (see

According to this arbitrage design, carry out simulated arbitrage on the data in the sample. The success rate of arbitrage in each period is shown in

As shown in

After 20 significant periods of consolidation correction, reduced to 12 arbitrage interval. After the modified arbitrage design in the 10-day sample, the arbitrage effect will be improved, and the total success rate will increase to 69.2%. After deducting the handling fee, the profit will be 41951.54 yuan, of which the maximum single profit is 4284.81 yuan, the single maximum. The loss is −2877.28 yuan. If using the same arbitrage strategy to simulate the data outside the sample, the arbitrage total output power of the sample outside the sample can reach 83.3%, the profit is 7629.28 yuan, the maximum profit for a single arbitrage is 1521.907 yuan, and the maximum loss is 1799.34 yuan. At 9:30 - 9:40, 9:55 - 10:00, 10:25 - 10:30, 13:20 - 13:45, 13:50 - 13:55, 14:00 - 14:05 and 14:55-15:00 These 7 time slots are arbitrage, the success rate is 100%, but the arbitrage fails in the 14:30-14:45 period. After expanding the sample out-of-sample forecast, the success rate is reduced, and the profit increase is not large. The 5-day success rate outside the sample is 66.7%, the total profit is 15424.53 yuan, and the single maximum profit is 3121.601 yuan. The maximum loss is the same as the 2-day result outside the sample. The arbitrage success rate is 100% profitable in the two intervals of 13:50 - 13:55 and 14:00 - 14:05, and the success rate of other arbitrage intervals is generally reduced.

intraday effect period | arbitrage direction | buying time | closing time | arbitrage success rate in the sample |
---|---|---|---|---|

9:30 - 9:35 | positive | 9:31 | 9:35 | 60% |

9:35 - 9:40 | positive | 9:35 | 9:40 | 70% |

9:55 - 10:00 | positive | 9:55 | 10:00 | 70% |

10:25 - 10:30 | negative | 10:25 | 10:30 | 70% |

10:45 - 10:50 | negative | 10:45 | 10:50 | 60% |

10:55 - 11:00 | negative | 10:55 | 11:00 | 60% |

13:20 - 13:25 | positive | 13:20 | 13:25 | 70% |

13:25 - 13:30 | positive | 13:25 | 13:30 | 60% |

13:30 - 13:35 | positive | 13:30 | 13:35 | 40% |

13:35 - 13:40 | positive | 13:35 | 13:40 | 50% |

13:40 - 13:45 | positive | 13:40 | 13:45 | 70% |

13:50 - 13:55 | positive | 13:50 | 13:55 | 50% |

14:00 - 14:05 | negative | 14:00 | 14:05 | 80% |

14:15 - 14:20 | positive | 14:15 | 14:20 | 60% |

14:20 - 14:25 | positive | 14:20 | 14:25 | 50% |

14:30 - 14:35 | positive | 14:30 | 14:35 | 80% |

14:35 - 14:40 | positive | 14:35 | 14:40 | 40% |

14:40 - 14:45 | positive | 14:40 | 14:45 | 60% |

14:50 - 14:55 | positive | 14:50 | 14:55 | 70% |

14:55 - 15:00 | negative | 14:55 | 15:00 | 80% |

Note: Due to the inability to obtain the opening price at 9:30 am, the arbitrage interval is 9:30 - 9:35, and the position is opened at 9:31.

significant variables | corresponding period of time | variation coefficient | p | arbitrage direction | buying time | closing time |
---|---|---|---|---|---|---|

h_{1} | 9:30 - 9:35 | 3.989318 | 0.035948 | positive | 9:31 | 9:35 |

h_{2} | 9:35 - 9:40 | 5.284662 | 0.007923 | positive | 9:35 | 9:40 |

h_{6} | 9:55 - 10:00 | 5.502699 | 0.015617 | positive | 9:55 | 10:00 |

h_{12} | 10:25 - 10:30 | −6.10354 | 0.016757 | negative | 10:25 | 10:30 |

h_{16} | 10:45 - 10:50 | −5.48434 | 0.039483 | negative | 10:45 | 10:50 |

h_{18} | 10:55 - 11:00 | −5.664 | 0.035961 | negative | 10:55 | 11:00 |
---|---|---|---|---|---|---|

h_{29} | 13:20 - 13:25 | 6.53757 | 0.015836 | positive | 13:20 | 13:25 |

h_{30} | 13:25 - 13:30 | 9.536892 | 0.000401 | positive | 13:25 | 13:30 |

h_{31} | 13:30 - 13:35 | 6.508251 | 0.015065 | positive | 13:30 | 13:35 |

h_{32} | 13:35 - 13:40 | 7.188046 | 0.006782 | positive | 13:35 | 13:40 |

h_{33} | 13:40 - 13:45 | 10.48504 | 0.000067 | positive | 13:40 | 13:45 |

h_{35} | 13:50 - 13:55 | 5.626211 | 0.028821 | positive | 13:50 | 13:55 |

h_{37} | 14:00 - 14:05 | −7.229 | 0.003851 | negative | 14:00 | 14:05 |

h_{40} | 14:15 - 14:20 | 4.846947 | 0.040511 | positive | 14:15 | 14:20 |

h_{41} | 14:20 - 14:25 | 5.34761 | 0.020729 | positive | 14:20 | 14:25 |

h_{43} | 14:30 - 14:35 | 5.084704 | 0.020153 | positive | 14:30 | 14:35 |

h_{44} | 14:35 - 14:40 | 4.211954 | 0.0468 | positive | 14:35 | 14:40 |

h_{45} | 14:40 - 14:45 | 4.974627 | 0.014848 | positive | 14:40 | 14:45 |

h_{47} | 14:50 - 14:55 | 3.762071 | 0.043918 | positive | 14:50 | 14:55 |

h_{48} | 14:55 - 15:00 | −14.401 | 0 | negative | 14:55 | 15:00 |

arbitrage direction | buying time | closing time | in-sample | 2 days out of sample success rate | 5 days out of sample success rate |
---|---|---|---|---|---|

positive | 9:31 | 9:40 | 70.0% | 100% | 60.0% |

positive | 9:55 | 10:00 | 70.0% | 100% | 60.0% |

negative | 10:25 | 10:30 | 70.0% | 100% | 80.0% |

negative | 10:45 | 10:50 | 60.0% | 50% | 60.0% |

negative | 10:55 | 11:00 | 60.0% | 50% | 60.0% |

positive | 13:20 | 13:45 | 90.0% | 100% | 60.0% |

positive | 13:50 | 13:55 | 50.0% | 100% | 100.0% |

negative | 14:00 | 14:05 | 70.0% | 100% | 100.0% |

positive | 14:15 | 14:25 | 70.0% | 50% | 60.0% |

positive | 14:30 | 14:45 | 60% | 0% | 60.0% |

positive | 14:50 | 14:55 | 70% | 50% | 40% |

negative | 14:55 | 15:00 | 80% | 100% | 60% |

the total success rat | 69.2% | 83.3% | 66.7% | ||

profit (yuan) | 41951.54 | 7628.28 | 15424.53 | ||

the biggest profit (yuan) | 4284.81 | 1521.907 | 3121.601 | ||

the biggest loss (yuan) | −2877.28 | −1799.34 | −1799.34 |

In this paper, we construct different virtual variable regression models for high-frequency yield, volume change rate, position change rate, price fluctuation range and price difference series by means of stationarity test, autocorrelation test and heteroscedasticity test. Then, from the sampling point and the distance of the contract, the intraday effect form of CSI 500 stock index futures is analyzed and summarized. And the statistical arbitrage strategy is constructed based on the manifestation of the intraday effect of the spread. When the coefficient of the significant variable in the intraday effect test model is positive, the intraday effect period corresponding to the variable is positively arbitrarily; the starting point is opened, and the ending time is closed. Secondly, the arbitrage strategy is revised according to the success rate of each arbitrage interval in the sample, and the modified arbitrage strategy is used to simulate the arbitrage of the sample closing price. The main conclusions are as follows:

Intraday effect of high frequency yield: The CSI 500 stock index futures yield does have an intraday effect, and the intraday effect is different under different samples. Overall, the high-frequency yields have changed significantly in at least four of the 48 5-minute periods of the day (20 minutes), and the intraday effect in the near-month contract is more pronounced than the far-month contract.

Intraday effect of volume change rate: The trading rate change rate of the recent month contract is significant in the two periods of 13:00 - 13:05 and 14:25 - 14:30, and the corresponding dummy variable coefficient is negative, indicating that the trading volume has decreased. There are 27 zero-volume cases in the 5-minute data of IC1712 stock index futures within 10 trading days, mainly distributed in the morning at 10:35 - 10:40, 11:20 - 11:25, 11:25 - 11:30, 13:00 - 13:05, 13:15 - 13:20 and 14:20 - 14:25.

Intraday effect of the rate of change in positions: The rate of change in positions has a significant intraday effect in both overall and near-term contracts. During the period of 9:30 - 9:55, the volume of positions changed significantly within 25 minutes of opening. The change rate of position holding in the near month contract mainly has intraday effect within 40 minutes after the opening of the morning and 5 minutes after the opening of the afternoon, while the intraday effect in the morning of the trading day in the far month contract is within 25 minutes after the opening of the morning, and the significant period in the afternoon is concentrated within 14:20-14:25.

Intraday effect of price volatility: The CSI 500 stock index has obvious intraday price fluctuations within 15 minutes of the opening period. The near-month contract is more significant than the far-month contract. The dummy variable coefficient is significant every 5 minutes during the 25-minute opening period.

Intraday effect of spread between near and far month contracts: The spread is most affected by the intraday effect among the five test variables. In general, the time period during which the daytime changes are mainly distributed is 13:20 - 13:25, 13:35 - 13:45, 13:50 - 13:55, 14:00 - 14:05 and 14:15 - 14:25.

Based on the intraday effect of the spread within 10 trading days in the sample, after the strategy correction of merging adjacent arbitrage intervals, the total success rate of the new arbitrage strategy in the sample is equal to 69.2%, and the profit after deducting the commission fee is 41951.54 yuan. The arbitrage total output power of the two trading days is 83.3%, the profit is 7629.28 yuan, and the maximum arbitrage gain is 1521.907 yuan. At 9:30 - 9:40, 9:55 - 10:00, 10:25 - 10:30, 13:20 - 13:45, 13:50 - 13:55, 14:00 - 14:05 and 14:55 - 15:00, this is 7 arbitrage period; the success rate is 100%. To sum up, statistical arbitrage strategy based on the intraday effect of spread has better arbitrage effect. This paper also has some shortcomings, which need to be further studied and improved. Therefore, in future studies, intraday effect and statistical arbitrage can be further discussed from the perspective of sample size.

The authors declare no conflicts of interest regarding the publication of this paper.

Zhang, J.W., Tang, G.Q., Miao, Q.F. and Yang, J.L. (2019) The Statistical Arbitrage Study of CSI 500 Stock Index Futures Based on Intraday Effect. Open Journal of Business and Management, 7, 1095-1111. https://doi.org/10.4236/ojbm.2019.73075