Download Volume-Synchronized Probability of Informed

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.
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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
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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
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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
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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
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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
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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
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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[VS  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
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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.
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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
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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
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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.
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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
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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
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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.
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