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Volume-Synchronized Probability of Informed Trading (VPIN) and Price Movements of Index Futures: Empirical Analysis Based on Price Movements between May and July, 2015 in Chinese Index Futures Market Ouyang Hongbinga, Yan Yanb a.Professor, Department of Finance, School of Economics, Huazhong University of Science and Technology b.PHD Candidate, Department of Finance, School of Economics, Huazhong University of Science and Technology Abstract We use Volume-Synchronized Probability of Informed Trading (VPIN) to study price movements of Chinese index futures market. VPIN is the improvement of traditional PIN. It can effectively alert strong price movements caused by order flow toxicity. We use VPIN to predict the price movements of index future and study the relationship between order flow toxicity and future price movements as well. We show that VPIN can effectively predict the short-term price movements of index future. Investors and Regulators could adjust their investment strategies and trade behavior through monitoring VPIN value to avoid potential risk and stabilize the market. Key words: VPIN, Index future, price movements, Microstructure, Alert 1. Background Economists always try to explain what cause the high volatility in financial markets and how to predict and possibly avoid those strong fluctuation. While with the development of technology, high-frequency trading is prevail across markets. However, traditional theories or methods are not suitable for analyzing market phenomenon in high-frequency environment due to the unique features of high-frequency trading. Easley, de Prado, O'Hara M (2011) first introduce Volume-Synchronized Probability of Informed Trading (VPIN) in their study of ‘Flash Crash’ happened on 6 May, 2010. Compared to traditional method, VPIN is a better method in high-frequency environment. They prove that VPIN could effectively predict short-term price movements caused by order flow toxicity. VPIN is an effective risk management tool in high-frequency trading Error! Reference source not found.. Investors and regulators could adjust their investment strategies and trade behavior through monitoring changes of VPIN value to avoid potential risk and stabilize the market. One presentation of high volatility in stock market is price reversal, that is, the sudden shift of price trend direction. It just like roller-costar which moves up and down frequently. Shkilko, Van Ness and Van Ness (2009) study the price reversal emerged on non-news day in stock market. They believe that aggressive short selling could produce excess price pressure and help the increasingly downtrend of stock prices. While big price reversal is more likely showed on stocks which have no selling constraint. Brunnermeierand Pedersen (2005) prove that, on trading days without any information, sudden price reversal is a signal of predatory trading. Since market is easily influenced by sudden liquidity risk, short-selling of predators may accelerate the downtrend of stock prices. When predatory trading ceases, price would increase. Studies on reasons of high volatility in stock market and prediction of price movements mainly focus on famous events. One of them is ‘Black Monday’ which happened on 19 Nov, 1987. 2 On that day, Dow Jones Industrial Average dropped sharply and caused panics in American financial markets. Latter the panics spread around the world and economic recession came. Novak and Beirlant (2006) study the crisis and prove the effectiveness of extreme value theory in predicting high volatility of prices. Also studying stock crisis, Bates (1991) believes that the cost of put option could be used in predicting price fluctuation. While Leland and Rubinstein (1988) believe that imbalance between large traders and portfolio insurance is a signal of strong price fluctuation. Another real case of high volatility in developed markets is ‘Flash Crash’ happened on 6 May, 2010. ‘Flash Crash’ is the biggest event in recent years. It generates a series of studies analyzing the reason of ‘Flash Crash’ and prediction of price fluctuation. McInish, Upson and Wood (2013) compare the market behavior of ISOs (Intermarket Sweep Orders) on ‘Flash Crash’ day and trading days before and after ‘Flash Crash’. They think that abnormal trading behavior of ISOs on 6 May, 2010 leads to sudden price rise and fall. Lee, Cheng and Koh (2010) construct virtual market including multiple types of traders. They think that ‘Flash Crash’ is caused by similar trading strategies used by systematic traders. While other researchers believe high-frequency trading induces ‘Flash Crash’. Muthuswamy, Palmer, Richie and Webb (2011) point out that high-frequency trading increases information asymmetry in ‘Flash Crash’. It damages non-computerized traders. Menkveld and Yueshen (2013) see high-frequency traders as the market intermediary which decrease asymmetry between buyers and sellers. Their study proves that high-frequency trading deepens the influence of ‘Flash Crash’. However, Kirilenko, Kyle, Samadi and Tuzun (2011) disagree with those opinions. In their opinion, high-frequency trading does not induce ‘Flash Crash’. While high-frequency trade does increase market volatilityError! Reference source not found.. While for price movements prediction, Easley de 3 Prado, O'Hara (2011) improve traditional PIN method in measuring order flow toxicity and introduce VPIN method which is more suitable for high-frequency world. In their study of ‘Flash Crash’, they prove that VPIN method could effectively predict short-term price movements caused by order flow toxicity. VPIN method is an effective risk management tool in high-frequency trading. On 12 June, 2015, Shanghai Composite Index increased to 5178.19 points which is the highest point after 2008 financial crisis. However, beginning from 13 June, 2015, the index encountered dramatic crash in the following several weeks. On 9 July, 2015, the close price of Shanghai Composite Index was 3709.33 points. Within a month, Shanghai Composite Index dropped 1468.86 points,a 28.3% loss in less a month. In the mid-July, with government bailouts, stock market had a small-range improvement. While from 18, Aug, stock prices had been decreasing for five consecutive days. The lowest point within those days is 2850.71. During that period, intraday price boom and slump happened frequently. High volatility of stock market deteriorates the whole trade environment and induces a series of un-rational trade behaviors. Huge wealth evaporated. Enterprise reform has been impacted as well as the development of macro-economy. Many researchers believe that one of the main reasons of stock crisis is using index futures to short-sell the market in a high price. It is well known that two main functions of index future are price discovery and risk aversion. However, its high liquidity and leverage would also attract speculators. Those extra noise would generate more volatility in spot market through information conducting mechanism. Whether the order toxicity in index future markets induce this strong price fluctuation or not? How to monitor high volatility of prices? What regulators should do to avoid 4 crisis like this one? We hope to find answers for those questions by using VPIN method. We use high-frequency trade data of Shanghai Stock Exchange 50 index future between May to July, 2015 for predicting the price movements of index future and analyze the relationship between order flow toxicity and future price movements. We find that Order flow toxicity is one of the sources which induces strong price movements; VPIN could effectively predict short-term price movements caused by order flow toxicity. Different from current relative studies in China, we try to focus on market micro-structure, using high-frequency data and VPIN method, to predict price movements in index future markets. While our study based on real market events, market participants could learn from our analysis. Investors and regulators could adjust their investment strategies and trade behavior by monitoring VPIN value to avoid potential risk and stabilize the market. 2. Methodology According to the study of Ealsey, et.al (2010), when market maker who provide liquidity is adverse selected by order flow while unaware of the loss, order flow is toxic. Order flow toxicity relates closely to price movements. Measuring order flow toxicity could be used in predicting short-term price movements. Meanwhile, order flow toxicity could be the measurement of order flow imbalance. Order flow imbalance is the gap between buy orders and sell orders. In other words, it is the imbalance between buy pressure and sell pressure. Measuring order flow toxicity helps explaining market behaviors and actions relates to order flow imbalance. Though measuring order flow toxicity is important for explaining multiple market phenomenon, in high-frequency environment, extracting information from order flow is quite 5 complex. PIN (Probability of Informed Trading), which raised by Easley, Kiefer, O’Hara and Paperman (1996), is widely used in extracting information. While Easley et al. (2010) further introduce VPIN for measuring order flow toxicity. VPIN is the improvement for PIN, especially suitable for high-frequency world. 2.1 PIN Method PIN views trading as a game between liquidity providers (informed traders) and traders (uninformed traders) that is repeated over trading periods i=1,…,I. At the beginning of each period, nature chooses whether an information event occurs or not. These events occur independently with probability α. If the information is good news, then informed traders know that by the end of the trading period the asset will be worth S i ; and, if the information is bad news, that it will be Si and S i S i .Good news occurs with probability (1-δ) and bad news occurs with the remaining probability, δ. After an information event occurs or does not occur, trading for the period begins with traders arriving according to Poisson processes throughout the trading period. During periods with an information event, orders from informed traders arrive at rate μ. These informed traders buy if they have seen good news, and sell if they have seen bad news. Every period orders from uninformed buyers and uninformed sellers each arrive at rate ε [2]. The key part of PIN method is the probability that an order is from an informed trader, which is called PIN. The calculation of PIN is as following: PIN 2 Where 2 is the arrival rate for all orders and is the arrival rate for 6 information-based orders. PIN is thus a measure of the fraction of orders that arise from informed traders relative to the overall order flow, and the spread equation shows that it is the key determinant of spreads ( 2 (S i Si ) ). Liquidity providers could change their quotes by estimating PIN to avoid potential loss. While PIN model uses maximum likelihood to estimate the unobserved parameters driving the stochastic process of trades and then derives PIN from these parameter estimates. However, in high-frequency market, computing PIN is hard. For solving the calculation problem and estimation problem caused by noises related with trade intention and trade data in highly active markets, Ealesy, et. al(2010) improve PIN method, providing a simple metric for measuring order toxicity in a high frequency environment called VPIN method. For constructing same trade volume, VPIN method could get the estimation of order flow toxicity directly instead of estimating parameters. Meanwhile, VPIN method could reduce volatility-cluster in sample and improve estimation accuracy. 2.2 VPIN Method One key characteristic of high-frequency trade data is that it arrives at irregular frequency. While some trade is more important than others for different information it shows. Different information would exert different influence on the market. For presenting speed of new information arrives in market, VPIN method uses trade-time instead of clock-time for the measurement. Ealsey and O’Hara believe that interval between two trades is related to the existence of new information. That is to say, more important information it is, more trade volume 7 it attracts [2]. In order to simulate arrival process of some important information, VPIN method divides sample into multiple volume buckets which contains constant volume V. 2.2.1 Volume Bucket Volume bucket is consist of volume bars. A volume bar is a number of trades that are close to each other in time and having a prescribed total volume. Multiple bars aggregates a bucket. Each bar is treated as a single trade with a time stamp, a volume, and a price. Following the convention used in the published works, we declare the time stamp and price of the last trade in the bar as the time stamp and price for the bar [1]. 2.2.2 Volume Classification For determining directions of trades, i.e., classifying each trade as either as buyer-initiated or seller-initiated (Easley et al., 1996), or simply as a buy or a sell, VPIN method uses BVC (Bulk Volume Classification) (Easley et al., 2012). Compared to traditional Lee-Ready trade classification algorithm, BVC is more suitable for high-frequency environment. This is how Lee-Ready classifies volumes: it assigns a trade as buy if its price is higher than the preceding trade, as sell if its price is lower than the preceding trade, and the same type (either buy or sell) as the preceding trade if the prices remain the same. This classification is heavily dependent on the sequential order of trades. Typically, the order of trades can be determined from the time stamps. However, in high-frequency trading data, there are frequently trades with the same time stamp, which makes it hard to decide how to order these trades. Another source of timing issue is that the time stamps on trades executed at multiple matching engines may not be synchronized to the precision used to record the time stamps. BVC assigns a fraction of the volume as buys and the 8 remainder as sells based on the normalized sequential price change. The aggregation of volume could reduce the influence of order segmentation. While using standard sequential price change allows probabilistic volume classification. Chakeabarty, Pascual and Shkilko (2012) prove that BVC significantly reduces the computational cost for trade classification. While Easley (2012) prove that, compared to Lee-Ready method, BVC could better predict order flow toxicity. Specifically, VPIN method uses following models for classifying volumes: V B V S t ( ) i t ( 1) 1 t ( ) i t ( 1) 1 Vi Z ( Vi ( 1- Z ( Pi Pi 1 P Pi Pi 1 P ) )) V V B Where t ( ) is the time stamp of the last volume bar in th bucket. Z is the cumulative distribution function of Normal distribution. P is the standard deviation of price movements between volume bars. If there is no price change from the beginning to the end of the volume bar, we split the volume equally. Alternatively, if the price increases, the volume is weighted more toward buys than sells and the weighting depends on how large the price change is relative to the distribution of price movements. 2.2.3 VPIN S B We know that from Easley, et.al (2008), expected trade imbalance E[ V V ] and the expected total trade volume E[VS V B ] 2 . Since the volume of buckets is the same, total trade volume is nV . The VPIN is: 9 n VPIN 1 2 nV | V S V B | Where V is the volume of a bucket (in our study, V=5000) and n is the number of buckets. VPIN is updated after each volume bucket. Thus, when a new bucket is filled, we drop the first bucket and calculate the new VPIN based on second buckets to the n+1 bucket. Though this average method may lead to a high autocorrelation in VPIN estimates, Ealsey (2012) prove that VPIN is still highly stable. CDF (VPIN) could still estimate the probability of future price movements. 3. Data Our sample is composed of 500 millisecond data of Shanghai Stock 50 Index future (IH) between 29 April, 2015 and 31 July, 2015. Data comes from exchange Quotation system. Table 1 is the descriptive statistical analysis. Table 1 Descriptive Statistical Analysis of the Sample Standard Mean Deviatio Maximum Skewnes Kurtosi Observati s s ons Minimum n Price 3008.4560 297.4917 3565.0000 2355.2000 -0.3597 -1.0608 2120220 Volume 10.6400 11.9800 586.0000 0.0000 3.4153 26.1895 2120220 Table 1 shows that price fluctuates drastically during May to July, 2015. In the sample, the 10 highest point is 3565, showed in both 9:21:35 and 9:21:37 on 9 June, 2015. While the lowest point is 2355.2 which showed in 9:21:26 on 9 July, 2015. The difference between highest and lowest price is 1209.8 points. Standard deviation of price is quite large (297.4917), which also illustrate the strong fluctuation of price. Meanwhile, the highest volume is 586 showed in 11:16:55, 17 June, 2015. The difference of the highest and the lowest volume is 586. The standard deviation of volume is 11.98. High-low difference and high volatility both show strong fluctuation of volume. Figure 1 presents the time-series of volume. Figure1 Time-series of Volume Using VPIN method, we have daily average 68 VPIN value. The descriptive statistical analysis of VPIN value is showed in table 2. Table 2 Descriptive Statistical Analysis of VPIN Value Standard Mean Observatio Skewness Kurtosis JB Value Deviation VPIN 0.2942 0.0689 ns 0.6490 11 1.1324 571.0178 4610 As shown in table 2, the distribution of VPIN value is right-skewed and has a heavy tail. It does not fit normal distribution. Figure 2 shows the empirical cumulative distribution of VPIN value. In figure 2, 80% value is lower than 0.3528, namely, CDF (0.3528) =0.8. In other words, VPIN value which higher than 0.3528 is just 20% of the whole value. It indicates that the appearance of abnormal value is rare. In most time, trade is in the normal range. Figure2 Empirical distribution of VPIN Value Figure 3 and figure 4 show the time-series of shanghai stock 50 index future and its VPIN value. In figure 3, the highest point of shanghai 50 index future showed on 9 June, 2015(3565 points) while the lowest point showed on 9 July, 2015(2355.2 points). That is to say, within a month, the price decreased 66%. In figure 4, we could see that the VPIN value of June and July, 2015 is significantly higher than May, 2015. The highest VPIN value showed on 29 June, 2015. For observing clearly, we mark some other time points of high VPIN value in figure 4. 12 Figure 3 Time-series of Shanghai stock 50 Index future Figure 4 Time-series of VPIN 4. Empirical Study Generally, VPIN is usually the measurement of order flow toxicity. While Easley (2010) points out that liquidity providers would be gradually changed to liquidity consumers when VPIN is high. In markets which high-frequency liquidity providers dominate, such as index future market, this change would induce strong volatility of markets. In such situation, VPIN could be seen as the tool of monitoring liquidity risk [3]. For proving that VPIN could alert liquidity risk in 13 markets, in this section, we analyze VPIN changes in price upsurge day and in price plunge day each. We also study the relationship between VPIN and future price movements for better proving the alarm function of VPIN. 4.1 Individual Day Study 4.1.1 Price Plunge Day(29 June, 2015) Figure 4 shows that the highest point of VPIN in the sample occurs on 29, June. On 29 June, 2015, stock price fell sharply across markets. Shanghai composite index closed at 4053.03 points, decreased 236.74 points. Shenzhen Component Index closed at 13566.7 points, decreased 1137.7 points. Shanghai-Shenzhen 300 index closed at 4053.03, decreased 236.74 points. Shanghai stock 50 index closed at 2802.77 points, decreased 124.4 points. For analyzing the alert function of VPIN, we focus on observing and analyzing VPIN value changes on the event day and one trading day before and after the event day. In this section, we analyze VPIN value changes on 26 June, 29 June and 30 June, 2015. In figure 5, we can see changes of VPIN value and corresponding CDF in those three days. It is clear that on one trading day before the event day, namely, 26 June, 2015, Shanghai stock 50 index future had a small rise after opening. While beginning from 11:28, price went down continuously. CDF (VPIN) started increasing from opening, once reached at 0.95, and finally kept around 0.9. The high CDF shows high order flow toxicity. On 30 June, 2015, one trading day after the event day, price decreased a little at the opening. While from 10:42, price went up increasingly and finally closed at 2847.8, increased 132.6 points. CDF (VPIN) kept around 0.9 from the opening for a while. While after lunch break, CDF went down continuously. Order flow toxicity 14 decreased and finally back on relative normal level. Figure 5 Changes of Stock Index and VPIN Value during 26 June, 2015 to 30 June, 2015 (Note: Two red lines each present the opening and closing time of 29 June, 2015) Figure 6 shows changes of VPIN value and corresponding CDF on the event day. On 29 June, index future price went down a little at the opening and went up latter. Around 11:20, price went down again. The lowest point of the day, which is 2499 points, occurred at 13:20. Within 20 minutes, price decrease 211 points. Price went up latterly. At 13:40, price increased at 2717.6 points, rising 218.6 points within 20 minutes. While beginning from 14:17, price fell again and finally closed at 2654.8 point. VPIN value shows a high order flow toxicity before the plunge. We can see from figure 6 that CDF (VPIN) kept above 0.8 on the event day. At 11:13, CDF increased above 0.9 and kept rising. At 13:19:40, CDF was above 0.95. It kept above 0.95 until the closing. 15 Figure 6 Changes of VPIN Value on 29 June, 2015 4.1.2 Price Upsurge Day (23 June, 2015) On 23 June, 2015, stock price was rising across markets. Shanghai composite index closed at 4576.49 points, increased 104.88points. Shenzhen Component Index closed at 16045.99 points, increased 325.67points. Shanghai-Shenzhen 300 index closed at 4786.09, increased 144.66 points. Shanghai stock 50 index closed at 2982.6 points, increased 94 points. For observing changes of VPIN value, we also select one trading day before and after the event day for the analysis. In this section, we analyze the changes of VPIN value and corresponding CDF on 19 June, 23 June and 24 June, 2015. On 19 June, 2015, index future price was continuously decreasing from the opening. VPIN value rise a little. CDF (VPIN) rose sharply from 0.3 to 0.8 near the closing and showed a high order flow toxicity. On 23 June, VPIN kept increasing after the opening. CDF (VPIN) kept above 0.8. On 24 June, VPIN started decreasing. CDF (VPIN) fell from 0.9 to 0.3 and finally back on normal level. 16 Figure 7 Change of Index Future Price and VPIN Value On 23 June, 2015, index future price started rising from the opening. At 10:34, price began falling. The lowest point of the day is 2833 point occurred at 11:19. Then price rise sharply. At 11:27, price reached to 2952.8 points. 119.8 points were increased within 8 minutes. Price kept rising until 14:54, reached at 2818 points. While price went down near the closing and closed at 2982.6 points. CDF (VPIN) was above 0.9 at 11:17:40 and kept rising. At 11:27, CDF (VPIN) was above 0.95. Figure 8 Changes of VPIN Value on 23 June, 2015 17 4.2 VPIN and Future Price Movements Conclusion In above section, we already prove that VPIN could alert strong price fluctuation. VPIN is the measurement of order flow toxicity. So what is the relationship between order flow toxicity and future price movements? Whether order flow toxicity is the cause of strong price fluctuation during May to July, 2015 or not? For answering above questions, in this section, we study the relationship between VPIN and price movements. Through our study, investors and regulators could know the relationship between order flow toxicity and future price movements. Then they can adjust their investment decisions and regulations for avoiding or reducing potential risk. In this section, we use absolute return for measuring price movements. Table 3 and table 4 each presents one conditional probability distribution got from joint distribution of VPIN and absolute return. The header of columns are absolute return divided in 0.05% interval. While the header of rows are VPIN quantiles divided in 5% interval. 4.2.1 Pr ob( P 1 | VPIN 1 ) P 1 Table 3 is the distribution of absolute return on condition of VPIN, namely, prob( P 1 | VPIN 1 ) . VPIN -1 is the estimation of - 1 th volume bucket. Absolute return is P 1 P 1 , which is the absolute value of the ratio of price different between th and - 1 th P 1 volume bucket. Seen from the conditional distribution presented in table 3, we could make following conclusions: Firstly, if VPIN value is low, then the absolute return of the next volume bucket is low as well. For example, when VPIN value is in the lowest 5% interval, the probability of next absolute return in 0% to 0.25% interval is 73.36%. Secondly, if VPIN value is high, the 18 distribution of absolute return is relative disperse. Lastly, if VPIN value is very high (>0.9), the probability of high absolute return of next volume bucket is low. >=2.00 0.25% 0.50% 0.75% 1.00% 1.25% 1.50% 1.75% 2.00% % 0.05 73.36% 19.65% 5.68% 0.87% 0.00% 0.44% 0.00% 0.00% 0.00% 0.10 70.43% 22.17% 6.52% 0.87% 0.00% 0.00% 0.00% 0.00% 0.00% 0.15 53.68% 36.36% 7.79% 1.73% 0.00% 0.00% 0.00% 0.00% 0.00% 0.20 58.26% 31.74% 8.26% 1.30% 0.43% 0.00% 0.00% 0.00% 0.00% 0.25 55.84% 30.30% 6.06% 3.90% 2.16% 0.00% 0.87% 0.43% 0.00% 0.30 59.39% 26.20% 10.48% 1.31% 2.18% 0.44% 0.00% 0.00% 0.00% 0.35 51.95% 29.87% 10.39% 5.19% 1.73% 0.00% 0.00% 0.43% 0.43% 0.40 60.92% 28.15% 7.14% 2.10% 0.00% 0.84% 0.84% 0.00% 0.42% 0.45 54.98% 30.74% 9.52% 1.30% 2.16% 0.87% 0.00% 0.00% 0.43% 0.50 48.70% 32.17% 12.17% 2.17% 1.30% 0.43% 0.43% 0.43% 2.17% 0.55 50.87% 30.43% 13.91% 3.04% 0.43% 0.87% 0.00% 0.00% 0.43% 0.60 56.71% 24.24% 9.96% 5.63% 2.60% 0.00% 0.00% 0.43% 0.43% 0.65 48.26% 33.91% 9.57% 3.48% 3.04% 1.30% 0.00% 0.00% 0.43% 0.70 48.92% 34.63% 12.12% 0.87% 1.73% 0.43% 0.00% 0.00% 1.30% 19 0.75 50.87% 33.04% 10.43% 3.91% 0.87% 0.00% 0.43% 0.00% 0.43% 0.80 41.56% 33.33% 15.15% 5.19% 2.16% 0.87% 1.30% 0.43% 0.43% 0.85 37.83% 34.35% 13.91% 7.83% 2.61% 2.17% 0.43% 0.43% 0.43% 0.90 33.33% 33.77% 16.45% 7.36% 5.19% 2.60% 0.87% 0.00% 0.43% 0.95 34.78% 32.61% 18.70% 6.96% 3.91% 2.17% 0.43% 0.00% 0.43% 1.00 29.15% 22.87% 18.39% 9.87% 8.52% 4.48% 3.59% 0.90% 2.24% Table 3 Conditional Probability Distribution of VPIN and Absolute Return ( P( P 1 | VPIN 1 ) ) P 1 There are two situations of high VPIN value. One is abruptly rising. In this situation, price fluctuates strongly. Another one is keeping in high level. In this situation, price fluctuates slightly. We take empirical examples of the first situation. On 29 June, 2015, at 11:01:40, the CDF (VPIN) is 0.7648 and starts rising. At 11:13:00 it reach at 0.9223. Few minutes later, at 11:20:00, price starts falling and falls heavily. Another case is 23 June, 2015. At 11:09:40, the CDF (VPIN) is 0.8514. 8 minutes later, at 11:17:00, CDF rise to 0.9638. 2 minutes after price falls strongly. Two cases all indicate that VPIN could effectively predict strong price fluctuation in short-term. 20 Figure 9 Short-term Changes of CDF (VPIN) on 29 June and 23 June, 2015 4.2.2 Pr ob(VPIN 1 | P 1) P 1 Table 4 shows the VPIN distribution on condition of absolute return between - 1 th and th volume bucket, namely, prob(VPIN 1 | P 1 ) . Notably when absolute return is high, VPIN P 1 value of the former bucket is not very low. While when absolute return is low, the VPIN value of the former bucket is not very high. It is to say that VPIN could alert extreme price movements. However, we notice that though absolute return is high, the VPIN value of the former bucket is not very high. It means that not all price fluctuation caused by order flow toxicity. Order flow toxicity is just one cause of price fluctuation. >=2.00 0.25% 0.50% 0.75% 1.00% 1.25% 1.50% 1.75% 2.00% % 0.05 7.15% 3.25% 2.54% 1.16% 0.00% 2.44% 0.00% 0.00% 0.00% 0.10 6.89% 3.68% 2.93% 1.16% 0.00% 0.00% 0.00% 0.00% 0.00% 21 0.15 5.27% 6.06% 3.52% 2.33% 0.00% 0.00% 0.00% 0.00% 0.00% 0.20 5.70% 5.27% 3.71% 1.74% 1.06% 0.00% 0.00% 0.00% 0.00% 0.25 5.49% 5.05% 2.73% 5.23% 5.32% 0.00% 9.09% 12.50% 0.00% 0.30 5.78% 4.33% 4.69% 1.74% 5.32% 2.44% 0.00% 0.00% 0.00% 0.35 5.10% 4.98% 4.69% 6.98% 4.26% 0.00% 0.00% 12.50% 4.17% 0.40 6.17% 4.83% 3.32% 2.91% 0.00% 4.88% 9.09% 0.00% 4.17% 0.45 5.40% 5.12% 4.30% 1.74% 5.32% 4.88% 0.00% 0.00% 4.17% 0.50 4.76% 5.34% 5.47% 2.91% 3.19% 2.44% 4.55% 12.50% 20.83% 0.55 4.98% 5.05% 6.25% 4.07% 1.06% 4.88% 0.00% 0.00% 4.17% 0.60 5.57% 4.04% 4.49% 7.56% 6.38% 0.00% 0.00% 12.50% 4.17% 0.65 4.72% 5.63% 4.30% 4.65% 7.45% 7.32% 0.00% 0.00% 4.17% 0.70 4.81% 5.77% 5.47% 1.16% 4.26% 2.44% 0.00% 0.00% 12.50% 0.75 4.98% 5.48% 4.69% 5.23% 2.13% 0.00% 4.55% 0.00% 4.17% 0.80 4.08% 5.56% 6.84% 6.98% 5.32% 4.88% 13.64% 12.50% 4.17% 0.85 3.70% 5.70% 6.25% 10.47% 6.38% 12.20% 4.55% 12.50% 4.17% 0.90 3.28% 5.63% 7.42% 9.88% 12.77% 14.63% 9.09% 0.00% 4.17% 0.95 3.40% 5.41% 8.40% 9.30% 9.57% 12.20% 4.55% 0.00% 4.17% 22 1.00 2.76% 3.68% 8.01% 12.79% 20.21% 24.39% 36.36% 25.00% 20.83% Table 4 Conditional Probability Distribution of VPIN and absolute return ( prob(VPIN 1 | P 1 ) ) P 1 4.3 Conclusion In this section, we analyze the alert function of VPIN in both price upsurge day and price plunge day. Seen from figure 6 and figure 8, on 23 June and 29 June, price fluctuated strongly. In other words, price rise sharply and fall steeply in a short time. Studying the VPIN changes in one trading day before and after the event day, we can see that on one trading day before the event day, VPIN value was rising to abnormal ranges (CDF(VPIN)>0.8) near the closing. While on one trading day after the event day, VPIN value was back on relative normal levels. On the event day, few minutes before price upsurge or plunge, high abnormal VPIN value (CDF>0.9 or CDF>0.95) occurs. The performance of VPIN on those day shows that VPIN method could effectively monitor order flow toxicity in the market and alert strong price fluctuation. We also study the relationship between order flow toxicity and price movements. In table 3 we see that if VPIN value is low, then the price movements in next bucket is small. While if VPIN value is high, the degree of price movements of next bucket is uncertain. Table 4 shows that if price moves slightly, VPIN value of the former bucket is not very high. While when price moves strongly, VPIN value of the former bucket is not very low. From distribution presented in table 3 and table 4, we can make following conclusions: order flow toxicity is just one source of strong price fluctuation. VPIN could effectively predict short-term strong price movements caused by order flow toxicity. 23 5.Summary and Suggestion We use high-frequency data of index future market for analyzing strong volatility in markets during May to July, 2015 by using VPIN method. Through studying changes of VPIN estimations on 23 June and 29 June, 2015, we prove the effectiveness of VPIN method in alerting strong price fluctuation. Meanwhile, by studying the relationship between VPIN value and future price movements, we illustrate the effectiveness of VPIN method in predicting short-term strong price movements. It is noticed that not all price fluctuation is caused by order flow toxicity. Order flow toxicity is just one source of price fluctuation. However, observing the changes of VPIN value in empirical analysis, we notice that before strong price movements, order flow toxicity would always rise and abnormal VPIN value shows. It means that intraday strong price fluctuation is caused by toxicity. In other words, imbalance between buy orders and sell orders induce strong price movements. Estimating order flow toxicity is useful. For investors, especially liquidity providers, VPIN could be used as real-time risk management tool. By monitoring changes of VPIN value, investors could predict liquidity risk in markets. Investors could control their risk ahead of time and keep active in high volatility situation according to their prediction. For regulators, VPIN could be used as monitoring changes of market liquidity. When liquidity is limited, alert signal would show. Regulators could prevent strong volatility in markets by stopping trading or set market limitations. Currently Chinese index future market is still in its early stage. Compared with developed markets, price discovery and risk management function of index future are not used well. While 24 high leverage and liquidity in index future market would attract speculators. Speculation may induce high volatility in stock markets and accelerate market crash. After 2 September, 2015, Shanghai and Shenzhen 300 index future trading is limited by China Financial Futures Exchange for avoiding speculation and stabilizing markets. It is the strictest regulation policy after strong price fluctuation happened during May to August, 2015. However, it does not work very well. Intraday index future trading volume does decrease quickly after 2 September, 2015, but financial markets still perform badly. Shanghai composite index started falling from 23 December, 2015. On 27 January, 2016, it reached to the lowest point of recent years, 2638.60 points. For better using index future, regulators need enhance market regulation and take preventive actions, such as improving trading mechanism and regulation system in index future market, using multiple types of monitoring tools or educating investors and improving information transparency for avoiding non-rational panic. Reference: [1]Kesheng Wu, E.Wes Bethel, Ming Gu (2013), A Big Data Approach to Analyzing Market Volatility, Algorithmic Finance [J],2:3-4,241-267. [2]Easley D, López de Prado M, O'Hara M (2011), The Microstructure of the ‘Flash Crash': Flow Toxicity, Liquidity Crashes and the Probability of Informed Trading, the Journal of Portfolio Management [J], 37( 2) : 118-128. [3]Easley D, López de Prado M, O'Hara M (2011), The Exchange of Flow Toxicity, The Journal of Trading[J], 6( 2) : 8-13. 25 [4]CFTC-SEC, Preliminary Findings Regarding the Market Events of May 6, 2010, May 18, 2010. [5]CFTC-SEC, Findings Regarding the Market Events of May 6, 2010, September 30, 2010. [6]Robert Wood, James Upson, Thomas H.McInish (2013), The Flash Crash: Trading Aggressiveness, Liquidity Supply, and the Impact of Intermarket Sweep Orders, Financial Review, 49(3):481-509. [7]Brunnermeier, M. and L. H. Pedersen, 2005, Predatory trading, Journal of Finance 60, 1825-1863. [8]Andriy Shkilko, Bonnie Van Ness, Robert Van Ness, 2008, Aggressive short selling and price reversals, Social Science Electronic Publishing. [9]Bernard Lee, Shih-fen Cheng, Annie Koh, An Analysis of Extreme Price Shocks and Illiquidity Among Systematic Trend Followers, Review of Futures Markets 18:4, pp.385-419. [10]Muthuswamy, J., Palmer, J., Richie, N. and R. Webb (2011). High-frequency trading: implications for markets, regulators, and efficiency. Journal of Trading, Vol. 6, No. 1, 87–97. [11]Menkveld, A.J. and B.Z. Yueshen (2013). Middlemen interaction and its effect on market quality. Working paper, VU University Amsterdam. [12]Kirilenko, A., Kyle, A.S., Samadi, M. and T. Tuzun (2011). The flash crash: the impact of high frequency trading on an electronic market. Working paper, University of Maryland. [13]Novak, S.Y. and J. Beirlant (2006). The magnitude of a market crash can be predicted. Journal of Banking and Finance, Vol. 30, 453–462. [14]Bates, D. (1991). The crash of '87: was it expected? The evidence from options markets. 26 Journal of Finance, Vol. 46, No. 3, 1009–1044. [15]Leland, H. and M. Rubinstein (1988). Comments on the market crash: six months after. Journal of Economic Perspectives, Vol. 2, No. 3, 45– 50. 27