The synchronized and long-lastingstructural change on commodity markets:

evidence from high frequency data

Nicolas Maystre(joint work with David Bicchetti)

UNCTADBank of England, London, 25 May 2012

Disclaimer: the views and opinions expressed herein are those of the author and do not necessarily reflect those of the United Nations Conference on Trade and Development.

MotivationIntense debate regarding the causes of the recent sharp

price movements of many primary commodities

• Economic fundamentals– Rising global demand (rapid and steady growth in large

developing economies)– Supply shocks (adverse weather; export bans)

• ‘Financialization’ of commodity marketsi.e. the increasing role of financial motives, financial markets and financial actors in the operation of commodity markets(UNCTAD, Trade and Development Report, 2009)

Non-exhaustive literature review

Economic fundamentals ‘Financialization’

Vansteenkiste (2009) Silvennoinen & Thorp (2010)

Irwin & Sanders (2010) Tang & Xiong (2011)

Büyükşahin, Haigh & Robe (2010) UNCTAD (2011)

Stoll & Whaley (2010, 2011) Büyükşahin & Robe (2011)

Some limitations of previous studies• Use of daily data (at best)• Look at passive investment strategy (index funds)• Unrecorded participants

What happens during the day?• Need to look at intraday data• Consider actual trades & most liquid futures

• Identify a synchronized structural change across commodities in the course of 2008 (sharp increase after Lehman’s collapse)

• Link to high frequency and algo trading

Main findings

Thomson Reuters Tick History database

• Provides millisecond-time stamped tick data since 1996– Trades and Quotes messages (level 1)– Market depth (level 2)

• All asset classes– Equities– Fixed Income– FX– Futures– Options

• Covers more than 45 million unique instruments across 400+ exchanges

Electronic trading caused trading activities to increase dramatically…

10'000

100'000

1'000'000

10'000'000

100'000'000

'96 '97 '98 '99 '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11

Oil (WTI) Corn Soybean Sugar Wheat Live CattleNote: The y-axis is a logarithmic scale of base 10.Source: Bicchetti & Maystre (2012) calculations based on Thomson Reuters Tick History database

The presence of HFT & AlgoMonthly WTI front month contract volumes and tick, as well

as the ratio between the two, 2007m1-2011m12

11.5

22.5

33.5

4R

atio (

volu

me/tic

k)

010

20

30

40

50

60

Exch

an

ge

d v

olu

me

an

d n

um

be

r o

f ticks

(mu

ltip

le o

f 1

00

,00

0)

2007m1 2008m1 2009m1 2010m1 2011m1 2012m1

Volume TickRatio (volume/tick)

Source: Bicchetti & Maystre (2012) calculations based on Thomson Reuters Tick History database

1. We compute the log returns of the mean prices at 1-hour, 5-minute, 10-second and 1-second intervals. 2. We calculate a moving-window correlation coefficient (MWC) at time (t) between two series (rx and ry) at frequency (f) with a window width set to 15:

where and

N.B. We exclude weekend observations (avoid composition effect, no trade in the years prior to the introduction of electronic trading)

Methodology

14

0

115

0

2

14

0_,_

i

f

iitit

iitit

ryryrxrx

ryryrxrxtryrxMWC

)ln(1

t

tt z

zrz

15

14

0

iitrz

rz

Distribution of the MWC coefficients WTI and the E-mini S&P 500 futures (front month)

1-hour 5-minute 10-second 1-second1997* 0.67 0.71 0.00 -1998 3.24 2.62 0.06 -1999 3.93 2.89 0.16 -2000 4.10 2.91 0.20 -2001 4.63 2.59 0.22 -2002 4.71 2.45 0.35 -2003 7.37 2.80 0.34 -2004 8.28 3.60 0.62 -2005 8.60 4.71 0.43 -2006 8.89 6.35 2.04 0.072007 9.05 10.97 13.16 4.312008 9.13 13.39 19.53 21.702009 9.15 14.32 19.39 22.322010 9.12 14.82 20.38 24.272011 9.13 14.89 23.11 27.34# obs 60,753 402,183 2,546,114 788,625

Source: Thomson Reuters Tick History database

Source: Thomson Reuters Tick History database

Time of day, hour GMT 1-hour 5-minute 10-second 1-second0 4.03 1.83 0.03 -1 3.92 2.81 0.04 0.002 3.86 3.01 0.04 0.003 3.71 2.73 0.02 -4 3.59 2.53 0.02 -5 3.44 2.79 0.02 -6 4.18 2.93 0.11 0.007 4.13 3.33 0.76 0.018 4.14 4.04 1.15 0.019 4.16 4.55 0.75 0.00

10 4.24 4.81 0.74 0.0111 4.38 4.91 1.31 0.0612 4.48 5.19 4.87 2.1413 4.39 5.20 11.02 18.3914 4.25 4.38 15.67 31.2315 4.28 6.82 16.37 20.5216 4.29 9.56 14.23 8.7417 4.27 9.90 13.38 5.1418 4.24 8.92 12.21 9.4019 4.50 6.07 6.03 4.2820 4.70 2.37 1.13 0.0621 4.43 0.45 0.09 0.0122 4.24 0.02 0.00 -23 4.14 0.85 0.00 -

Annual distribution of rolling correlationsreturns on the WTI and the E-mini S&P 500 futures (front month)

1997-2011

-1-.5

0.5

1

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

-1-.5

0.5

1

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

frequency: daily frequency: 1-hour

Source: Bicchetti & Maystre (2012) calculations based on Thomson Reuters Tick History database

Monthly median of rolling correlationsreturns on the WTI and the S&P 500 futures, 2007-2011

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

'07

m1

'07

m4

'07

m7

'07

m1

0

'08

m1

'08

m4

'08

m7

'08

m1

0

'09

m1

'09

m4

'09

m7

'09

m1

0

'10

m1

'10

m4

'10

m7

'10

m1

0

'11

m1

'11

m4

'11

m7

'11

m1

0

1-second 10-second 5-minute 1-hourSource: Bicchetti & Maystre (2012) calculations based on Thomson Reuters Tick History database

Monthly medians of 5-min rolling correlationsreturns on selected soft commodities and the E-mini S&P 500

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

2007m

1

2007m

4

2007m

7

2007m

10

2008m

1

2008m

4

2008m

7

2008m

10

2009m

1

2009m

4

2009m

7

2009m

10

2010m

1

2010m

4

2010m

7

2010m

10

2011m

1

2011m

4

2011m

7

2011m

10

Corn Soybeans Wheat Sugar Live Cattle

Source: Bicchetti & Maystre (2012) calculations based on Thomson Reuters Tick History database

Results discussion

• The structural break remarkable in many aspects:

1. The wide range of commodities involved

2. The synchronization of this phenomenon

3. The similarity of the evolution across commodities

Similarities on non-commodity marketsMonthly distribution of the 5-minute rolling correlations between the returns on the EUR/USD and the E-mini S&P 500 futures (front month), 2007m1-2011m12

-1-.

50

.51

2007m1 2008m1 2009m1 2010m1 2011m1Source: Bicchetti & Maystre (2012) calculations based on Thomson Reuters Tick History database

We identify 3 inflection points on the EURUSD/E-mini S&P500:

•2007m8, 2008m3, 2008m9

•Coincides with major financial shocks:

• Country Wide Financial/Subprime burst

• Bear Stearns

• Lehman Brothers

•Investigate by looking at WTI/EURUSD relation

Monthly distribution of the 5-minute rolling correlations returns on the EUR/USD and the WTI futures

-1-.

50

.51

2007m1 2008m1 2009m1 2010m1 2011m1Source: Bicchetti & Maystre (2012) calculations based on Thomson Reuters Tick History database

Similarities with the EURUSD futures• Increasing correlation between WTI and EURUSD starts around

summer 2007• Gradual change, not sharp like WTI & SP500• Temporary decline before Libyan uprising. Probably due to a new

phase of the eurozone crisis starting in Nov 2010• Return to positive correlation between EURUSD&WTI afterwards

• Root cause of the structural change beyond stock & commodity markets

• Although commodities traded in USD, it is unlikely that commodity traders have a significant and permanent effect on FX markets. Daily turnover on currency markets was estimated to be $3.98 trillion (BIS, 2010).

What does that mean?

What does that mean?•Questions to consider:

• Why do the median correlation depart from zero and become negative at the end of the 2008Q1, and why this trend then switch into positive territories in late Sept 2008?

• Why do the median correlations remain so high from Sept 2008 onwards?

• What is the driving force behind this structural change?

Several hypothesis:

• Decoupling? Not very convincing

• Risk on/risk off? Maybe, but why at such high frequency?

• Inflation fears? unlikely

• Liquidity/volatility changes? Marginal, cf. VIX chart. Again, why at such high frequency?

• Shift from supply to demand shocks? why at such high frequency? Why does it last?

• Shift in composition of markets participants? Probably

• Driven by Algo & HFT? Very likely, especially when one consider the 1-second correlation

What does that mean?

2007m12007m2 2007m3

2007m4

2007m52007m6

2007m7

2007m8

2007m92007m10 2007m112007m12

2008m1

2008m2

2008m32008m4

2008m5

2008m62008m7

2008m8

2008m9

2008m10

2008m11

2008m122009m1

2009m22009m3

2009m4

2009m5

2009m62009m7

2009m8

2009m92009m10

2009m11

2009m122010m1

2010m2

2010m3

2010m4

2010m52010m6

2010m72010m8

2010m9

2010m102010m11

2010m122011m1

2011m2

2011m3

2011m4

2011m5

2011m6

2011m7

2011m8

2011m9

2011m10

-.2

0.2

.4.6

.8

Month

ly m

edia

n o

f th

e 5

-min

ute

rolli

ng c

orr

ela

tions

betw

een the W

TI and the E

-min

i S

&P

500 futu

res

10 20 30 40 50 60Monthly average of the VIX levels

95% CI Fitted values

Source: Bicchetti & Maystre (2012) calculations based on Thomson Reuters Tick History database

Europe catching up-1

-.5

0.5

1

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22-05Hours of the day (GMT)

Note: outside values excluded

October 2008 - December 2009

January 2010 - December 2011

Source: Bicchetti & Maystre (2012) calculations based on Thomson Reuters Tick History database

Reactions to Bicchetti & Maystre (2012)

HFT fund in NY:

“HFT can refer to any of a very broad range of strategies, most of which are faster versions of non-fundamental value strategies which have been employed in the markets for decades”

Reactions to Bicchetti & Maystre (2012)

•HFT fund in Hong Kong:

“Yes, you are probably right that the increase in short term correlation is caused by HFT! I do think that also there, there is some kind of a herd behaviour or trend following. In my opinion, due to some trigger (maybe the introduction of electronic exchanges, like u mentioned) commodities start to be more correlated, so some HFT try to take advantage, because of that there is more correlation which attracts more HFT, this again drives up correlation and draws in more HFT etc etc”

Conclusion

• Recent financial innovations on commodity futures exchanges have an impact on the price discovery process

• This result questions the diversification strategy and portfolio allocation in commodities pursued by financial investors

• Trend following strategiesShift market away from fundamentals (Frankel & Froot 1990)

• Positive feedback characteristics (Smith 2010)

• As commodity markets become financialized, they can be more prone to external destabilizing effects. Deviation from their fundamentals exposed them to sudden and sharp corrections.