price shocks and the role of hft

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

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:00

13

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: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

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15

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15

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15

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:00

16

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:00

16

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16

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:00

16

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:00

16

:49

:00

17

:00

:00

17

:11

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17

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17

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17

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: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

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12

:42

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12

:54

:00

13

:06

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13

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13

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13

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13

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:00

14

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14

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14

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14

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14

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:00

15

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15

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15

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15

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15

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:00

16

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16

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16

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17

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17

:54

:00

18

:06

:00

18

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: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

)


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

)


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

)


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


1

2

3

4

5

6

7

-120 -100 -80 -60 -40 -20 0 20 40 60 80 100 120

Trad

ing

volu

me


0

1

2

3

4

5

6

7

8

-60 -40 -20 0 20 40 60

Trad

ing

volu

me


0

1

2

3

4

5

6

7

-60 -40 -20 0 20 40 60

Trad

ing

volu

me


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


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


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


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


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)


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)


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)


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)


Micro scale

http://www.nanex.net/FlashCrashEquities/FlashCrashAnalysis_Equities.html

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

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15

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15

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15

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15

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:00

16

:00

:00

16

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:00

16

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:00

16

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:00

16

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: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

)


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

)


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


0

2

4

6

8

10

12

-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60

Trad

ing

volu

me


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


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


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


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


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

http://permwinterschool.ru/

price shocks and the role of hft

Economy & Finance