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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.