price shocks and the role of hft
Post on 16-Jan-2017
136 Views
Preview:
TRANSCRIPT
Financial market instability: price shocks and the role of high-frequency trading
Sergey Ivliev
IASC-ARS 2015, 17 December 2015
Motivation
Financial markets are intrinsically unstable and are characterized by many “large” price fluctuations at all time scales
Many of these shocks are not explained by public news:
Joulin et al. (2008) investigated one minute returns of a large set of liquid US stocks. They detected eight 4-sigma jumps per stock per day and one 8-sigma jump every one day and a half per stock. Moreover they find that “neither idiosyncratic news nor market wide news can explain the frequency and amplitude of price jumps”
Financial market stability is a growing concern of regulators, investors, exchanges, media and politicians
Definition
Market shock – marker equilibrium disruption, caused by demand or supply change, leading to abrupt price change
4
“FLASH DUMP” В АКЦИЯХ APPLE 25.01.2013
AAPL “Flash Dump” 25.01.2013
5
“FLASH DUMP” В АКЦИЯХ APPLE 25.01.2013
http://www.nanex.net/aqck2/4689.html
Dollar Flash Crash 18.03.2015
6
USD FLASH CRASH
http://www.nanex.net/aqck2/4689.html
CFTC Regulation 40.6(a) Certification. New CME Rule 589 (“Special Price Fluctuation Limits”) http://www.cftc.gov/filings/orgrules/rule120514cmedcm001.pdf
ICE Dollar Index Futures
Research questions
What are the characteristics of price shocks on equity market?
How are they interrelated?
What is the behavior of HFT at times of price shocks?
M.Frolova, S.Ivliev, F.Lillo. Price Shocks on Multiple Time Scales: the Russian Stock Market case study.
Historical data sample (1):
We use database recording of all transactions and orders submission/ cancellation events of 29 blue chip stocks (included in MICEX Index in 2010) traded on the MICEX in 2010 (82 trading days).
The selected stocks are the most capitalized and liquid on the Russian market, yielding 80% of the total market cap and most of the traded volume.
Sample: 310 mln orders; 32 mln trades.
Historical data sample (2):
We use database recording of all transactions and orders submission/ cancellation events of 30 blue chip stocks traded on liquid equity market in Asia in 2012 (250 trading days).
Sample : 62 mln orders; 10 mln trades.
Data sample
Identification of market shocks
Time scales & Filters:
Hours scale (macro): Absolute & Relative filters
Source: G.Mu, W.Zhou, W.Chen and J. Kertesz (2010). Order flow dynamics around extreme price changes on an emerging stock market
Minutes scale (meso): Minute returns exceeds S times local volatility
Source: A.Joulin, A.Lefevre, D.Grunberg, J-P Bouchaud (2010). Stock price jumps: news and volume play a minor role
Tick scale (micro): NANEX filter
Source: N. Johnson, B.Tivnan (2011). Flash Crash Phenomena and a Taxonomy of Extreme Behaviors, Eur. Phys. J. Special Topics 205 65-78
Yttp
ttptptr
)(
)()()(
Macro scale
n
i
i Ttvn
Mtr1
),(1
)(
Relative filter:
Cumulative intraday returns exceeding M times the average volatility in the same period of the day in the sample:
),( Ttvi – intraday volatility (s.d.) at moment t for a time window ∆𝑇at day i
M=6
Y=4%
Δt=∆𝑇=60 min
Parameters:
Shocks identified: 1820. On average: 16 shocks/month per stock Consistent with Kertesz et.al. (2010) results for Shenzhen Stock Exchange in 2003: 20 shocks/month per stock
Absolute filter:
Cumulative intraday mid-price returns exceeding threshold Y within time window Δt:
Macro scale: example
1.62
1.64
1.66
1.68
1.7
1.72
1.74
1.761
1:3
0:0
0
11
:41
:00
11
:52
:00
12
:03
:00
12
:14
:00
12
:25
:00
12
:36
:00
12
:47
:00
12
:58
:00
13
:09
:00
13
:20
:00
13
:31
:00
13
:42
:00
13
:53
:00
14
:04
:00
14
:15
:00
14
:26
:00
14
:37
:00
14
:48
:00
14
:59
:00
15
:10
:00
15
:21
:00
15
:32
:00
15
:43
:00
15
:54
:00
16
:05
:00
16
:16
:00
16
:27
:00
16
:38
:00
16
:49
:00
17
:00
:00
17
:11
:00
17
:22
:00
17
:33
:00
17
:44
:00
17
:55
:00
18
:06
:00
18
:17
:00
18
:28
:00
18
:39
:00
pri
ce, r
ub
time
Shock at macro scale in HYDR stock on 4.06.2010 at 17:11 (-4.1%)
t
Tt
rT
Str
)(1
)(
Meso shocks filter:
1-minute return of mid-prices exceeds S times moving average of T previous returns:
S=8
∆𝑇=60 min
Parameters:
Shocks identified: 13368. On average: 5,6 shocks/day per stock 3-times more frequent than recorded in Bouchaud et.al. (2010) for NASDAQ, NYSE in 2006-2010: 0,75 shocks/day per stock
Meso scale
Meso scale: example
Shock at meso scale in HYDR stock on 14.04.2010 at 13:32, 15:16, 17:27
1.775
1.785
1.795
1.805
1.815
1.825
1.835
1.845
1.855
11
:30
:00
11
:42
:00
11
:54
:00
12
:06
:00
12
:18
:00
12
:30
:00
12
:42
:00
12
:54
:00
13
:06
:00
13
:18
:00
13
:30
:00
13
:42
:00
13
:54
:00
14
:06
:00
14
:18
:00
14
:30
:00
14
:42
:00
14
:54
:00
15
:06
:00
15
:18
:00
15
:30
:00
15
:42
:00
15
:54
:00
16
:06
:00
16
:18
:00
16
:30
:00
16
:42
:00
16
:54
:00
17
:06
:00
17
:18
:00
17
:30
:00
17
:42
:00
17
:54
:00
18
:06
:00
18
:18
:00
18
:30
:00
pri
ce, r
ub
.
time
Price path @ shocks Macro
0.95
0.96
0.97
0.98
0.99
1
1.01
-120 -100 -80 -60 -40 -20 0 20 40 60 80 100 120
Mar
ket
pri
ce (
=1
at
sho
ck t
ime
)
Time before and after the identified shock, minutes
0.999
1.004
1.009
1.014
1.019
1.024
1.029
1.034
1.039
1.044
1.049
-120 -100 -80 -60 -40 -20 0 20 40 60 80 100 120
Mar
ket
pri
ce (
=1
at
sho
ck t
ime
)
Time before and after the identified shock, minutes
Meso
0.995
0.996
0.997
0.998
0.999
1
1.001
-60 -40 -20 0 20 40 60
Mar
ket
pri
ce (
=1
at
sho
ck t
ime
)
Time before and after the identified shock, minutes
0.999
1
1.001
1.002
1.003
1.004
1.005
-60 -40 -20 0 20 40 60
Mar
ket
pri
ce (
=1
at
sho
ck t
ime
)
Time before and after the identified shock, minutes
Trading volume @ shocks Macro
1
2
3
4
5
6
7
8
9
10
11
-120 -100 -80 -60 -40 -20 0 20 40 60 80 100 120
Trad
ing
volu
me
Time before and after the identified shock, minutes
1
2
3
4
5
6
7
-120 -100 -80 -60 -40 -20 0 20 40 60 80 100 120
Trad
ing
volu
me
Time before and after the identified shock, minutes
0
1
2
3
4
5
6
7
8
-60 -40 -20 0 20 40 60
Trad
ing
volu
me
Time before and after the identified shock, minutes
0
1
2
3
4
5
6
7
-60 -40 -20 0 20 40 60
Trad
ing
volu
me
Time before and after the identified shock, minutes
Meso
Relative bid-ask spread @ shocks Macro
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
-120 -100 -80 -60 -40 -20 0 20 40 60 80 100 120
Re
lati
ve b
id-a
sk s
pre
ad
Time before and after the identified shock, minutes
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
-120 -100 -80 -60 -40 -20 0 20 40 60 80 100 120
Re
lati
ve b
id-a
sk s
pre
ad
Time before and after the identified shock, minutes
0.80
0.85
0.90
0.95
1.00
1.05
1.10
1.15
1.20
-60 -40 -20 0 20 40 60
Re
lati
ve b
id-a
sk s
pre
ad
Time before and after the identified shock, minutes
0.85
0.90
0.95
1.00
1.05
1.10
1.15
-60 -40 -20 0 20 40 60
Re
lati
ve b
id-a
sk s
pre
ad
Time before and after the identified shock, minutes
Meso
Buy imbalance @ shocks
sb
b
VV
VI
Vb – buy orders quantity in the limit order book at the end of each time moment; Vs – sell orders quantity in the limit order book at the end of each time moment
Macro
Meso
0,99
1,04
1,09
1,14
1,19
1,24
1,29
1,34
-60 -40 -20 0 20 40 60
Bu
y im
bal
ance
(o
rde
rs)
Time before and after the identified shock, minutes
0,70
0,75
0,80
0,85
0,90
0,95
1,00
-60 -40 -20 0 20 40 60
Bu
y im
bal
ance
(o
rde
rs)
Time before and after the identified shock, minutes
0,990
0,995
1,000
1,005
1,010
1,015
1,020
1,025
1,030
1,035
-60 -40 -20 0 20 40 60
Bu
y Im
bal
ance
(o
rde
rs)
Time before and after the identified shock, minutes
0,96
0,97
0,98
0,99
1,00
1,01
1,02
-60 -40 -20 0 20 40 60
Bu
y Im
bal
ance
(o
rde
rs)
Time before and after the identified shock, minutes
Micro scale
http://www.nanex.net/FlashCrashEquities/FlashCrashAnalysis_Equities.html
Micro scale
NANEX filter:
down-draft event: the stock had to tick down at least 10 times before ticking up - all within 2 seconds and the price change had to exceed 0.8%
up-draft event: tick up at least 10 times before ticking down - all within 2 seconds and the price change had to exceed 0.8%
Shocks identified: 369. On average: 4,5 shocks/per day Comparable to Nanex frequency of 4.13 shocks per day in 2010.
Micro scale
103
104
105
106
107
108
109
15
:00
:00
15
:05
:00
15
:10
:00
15
:15
:00
15
:20
:00
15
:25
:00
15
:30
:00
15
:35
:00
15
:40
:00
15
:45
:00
15
:50
:00
15
:55
:00
16
:00
:00
16
:05
:00
16
:10
:00
16
:15
:00
16
:20
:00
16
:25
:00
16
:30
:00
16
:35
:00
16
:40
:00
16
:45
:00
16
:50
:00
16
:55
:00
Micro scale: example
Shocks at micro scale in RTKM stock on 20.05.2010 at 16:00:07 and 16:00:26 …embedded into Meso shock at 16:01
Meso shock at 16:01
Micro shock at 16:00:07
103
104
105
106
107
108
109
15
:59
:00
16
:00
:00
16
:01
:00
16
:02
:00
16:00:0716:00:26
mid [16:00] = 103,895
mid [16:01] = 105,03
Micro scale: example
Shocks at micro scale in RTKM stock on 20.05.2010 at 16:00:07 and 16:00:26…
104
105
106
107
108
109
1 11 21 31 41 51 61
Ticks @ 16:00:07
104
105
106
107
108
109
1 6 11 16 21
Ticks @ 16:00:26
Shocks intensity differs among stocks…
0,1
1
10
100
100 1 000 10 000 100 000
0
1
10
100
1000
100 1 000 10 000 100 000
Correlation with trades: less shocks in liquid stocks
y = 21141x-0,672
R² = 0,7442
1
10
100
1000
100 1 000 10 000 100 000
Meso/month
Trades/day
Macro/month
Trades/day
Trades/day
Micro/month
shocks embedded?
* Only included those events with the time interval more than 60 minutes to previous event
Meso into Macro
Event type Macro events
quantity* Time bounds
Meso events in time bounds
Total meso events quantity
Share of embedded
events
Up-draft 93 ±1 hour 35 3354 1%
Down-draft 68 ±1 hour 35 3406 1%
Event type Meso events
quantity Time bounds
Micro events in time bounds
Total micro events quantity
Share of embedded
events
Up-draft 3354 ±1 min 42 157 26%
Down-draft 3406 ±1 min 44 212 21%
Micro into Meso
Conclusion (1)
A large number of shocks are present in financial markets
Shocks frequency is pretty consistent across markets
Less shocks in liquid stocks
Micro shocks are likely to be embedded into Meso shocks
Meso shocks are not embedded into Macro shocks and have different nature
Next research questions:
Are HFTs involved?
Can shocks be predicted?
In 30 liquid stocks of one of the equity markets in Asia…
1091 8-sigma 50bp shocks identified on 1-minute time scale, this correspond on average to one shock per 7 trading days per stock
1029 of “isolated” shocks (501 up / 529 down)
no seasonal effects during trading year
shocks are more frequent at the opening and closing of the trading day (U-shape)
only 1 systemic shock (4 stocks jump together) recorded
average price change during upshocks and downshocks is approximately 0.7%, there is a 0.2% price-relaxation after shock (relative to shock price)
0.990
0.992
0.994
0.996
0.998
1.000
1.002
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Mar
ket
pri
ce (p
=1
at
sho
ck t
ime
)
Time before and after the identified shock, minutes
0.998
0.999
1.000
1.001
1.002
1.003
1.004
1.005
1.006
1.007
1.008
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Mar
ket
pri
ce (p
=1
at
sho
ck t
ime
)
Time before and after the identified shock, minutes
Shocks identification
Shock profiles
relative bid-ask widens on average 2x times at the shock minute and doesn’t fully relax to pre-shock level after one hour
trading volume peaks at shock time, exceeding by 12x times normal trading activity for positive events and 10x times for negative events
0
2
4
6
8
10
12
14
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Trad
ing
volu
me
Time before and after the identified shock, minutes
0
2
4
6
8
10
12
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Trad
ing
volu
me
Time before and after the identified shock, minutes
0.500
0.700
0.900
1.100
1.300
1.500
1.700
1.900
2.100
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Re
lati
ve s
pre
ad
Time before and after the identified shock, minutes
0.500
0.700
0.900
1.100
1.300
1.500
1.700
1.900
2.100
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Re
lati
ve s
pre
ad
Time before and after the identified shock, minutes
Shock profiles
number of trades increases more than 12 times compared to pattern value during up shocks and more than 9 times during down shocks
number of submitted orders increases 8 times compared to pattern for up shocks and 7 times for down shocks
0
2
4
6
8
10
12
14
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 600
1
2
3
4
5
6
7
8
9
10
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
0
1
2
3
4
5
6
7
8
9
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
0
1
2
3
4
5
6
7
8
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
False positive rate
Tru
e p
osi
tive
ra
te
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
AUC= 61.01 %
Shock profiles
significant imbalance of buy orders in the effective market orders flow up to 80% during positive events and down to 20% during negative events
Though there are precursors in buy imbalance behavior, buy imbalance has poor predictive power and generate a lot of noisy signals
0.30
0.40
0.50
0.60
0.70
0.80
0.90
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Bu
y Im
ba
lan
ce
Time before and after the identified shock, minutes
0.10
0.20
0.30
0.40
0.50
0.60
0.70
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Bu
y Im
bal
ance
Time before and after the identified shock, minutes
Predicted
Actual SHOCK NO SHOCK
SHOCK 156 112
NO SHOCK 626 209 883 513
Predictive power of buy imbalance for down shocks at minute t=-1
HFT behavior during shocks
High Frequency Traders…
increase trading volume before and during the shock, and decrease it after the shock
increase the cancellation rate before and during the shock as well as the ratio between cancellation and limit orders
increase the average distance of the new limit orders from the corresponding best price (or the spread) before, during, and after the shock
On an average number of buy aggressive orders submitted by HFTs during up shocks exceeds pattern value 4.5 times, number of sell aggressive orders submitted during down shocks exceeds pattern value 3.5 times
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 600.0
0.5
1.0
1.5
2.0
2.5
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 600.0
0.5
1.0
1.5
2.0
2.5
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Aggressive orders submitted by HFTs (buy orders during up shock - left, sell orders during down shocks – right)
HFT behavior during shocks
The empirical study of the HFT share on the best bid and ask shows that HFT tend to modify their liquidity provision strategy after the shocks, but not prior to them.
When the price goes abruptly up, the share of HFT volume at the best ask declines, while it increases at the best bid
When the price goes abruptly down, the share of HFT volume at the best bid declines, while it increases at the best ask
One possible explanation is that HFTs withdraw the liquidity from the side of the book affected by price shock (caused by big trades), being concerned about the possible informativeness of these trades; another explanations is that they are trend-following.
25%
30%
35%
40%
45%
50%
-240 -180 -120 -60 0 60 120 180 240Minute (shock at t=0)
HFT Share at Best Bid HFT Share at Best Ask
25%
30%
35%
40%
45%
50%
-240 -180 -120 -60 0 60 120 180 240Minute (shock at t=0)
HFT Share at Best Bid HFT Share at Best Ask
Down shocks Up shocks
We studied the 8-sigma 50bp+ market shocks on 1-minute time scale in 30 liquid stocks in one of the Asian market
We found more than 1000 of these events per year, approximately 1 per week per stock (3x less frequent than in US, 20x less frequent than in Russia)
There is significant imbalance of orders prior to the shock, but it is considered to be weak precursor of shocks
High Frequency Traders increase trading volume before and during the shock, and decrease it after the shock, increase the average distance of the new limit orders from the corresponding best price
Market taking HFTs submit on average 4x times more aggressive orders than normal at the moment of shock
Market making HFTs tend to remove liquidity from the book from the side affected by shock (i.e. rebalancing their position)
Deliverable (iii). Report on HFT
Conclusions (2):
Perm Winter School
Perm Winter School 2016 Dates: 4-6 Feb 2016 Key-note speakers:
- Prof. Dr. Rosario Mantegna (CEU)
- Dr. Richard Olsen (Lykke)
- Dr. Ann-Christine Lange (Copenhagen Business School)
More information:
permwinterschool.ru
top related