1. Impact of Economic Data on Microstructural
Parameters in Crude Oil Futures
Sai Krishna Poddutur
May 14, 2015
Abstract
This paper is another attempt to analyze and understand how information translate into commodity future
markets. The primary question is whether the economic changes get translated into future markets and
get realized as changes in microstructural parameters such as estimates of liquidity costs, volatility, adverse
selection costs, volume etc. During last several months (2014-2015) crude oil markets have been subjected to
plenty of activity due to economic changes in the space. This presents a perfect avenue to analyze such a
relationship. The results provide clear evidence that economic conditions do impact microstrutural parameters
and that they tend to have an immediate effect. However finer patterns are yet to be analysed.
Introduction
Future markets are subjected to changes in their microstructure with advancement of technology and formation
of new regulations. Although lot of research has been conducted to understand how economic data translates
into markets, it is still an economist’s endeavour to explain clearly how information flows into markets
on a day to day basis. This becomes even more challenging with changes in market microstructure. It is
interesting to understand if there exists a relationship between economic changes and intra-day microsecond
level microstructural parameters. The obvious question that arises is what is the impact of economic changes
on microstructural parameters such as liquidity costs, volatility, adverse selection costs, volume on a intraday
basis ?. The paper tries to study this relationship. Last year (2014) set the stage for saga which was about to
unfold in the crude oil markets with prices reaching historical lows. This was a result of lot of global economic,
political and technological changes that occurred related to crude oil markets. These several developments
meant constant information flow into the markets, thus providing an almost perfect avenue to understand the
mechanism of information translation into commodity futures. The paper analyzes the intra level data at
microsecond scale for all transactions in CME Globex Crude Oil Futures from Jan 2015 to March 2015. Each
day is classified as an information day or non-information day based on whether there have been significant
economic developments with respect or affecting crude oil markets. Microstructural parameters are estimated
based on previous models developed by researchers such as Hasbrock and Madavan and changes in these
estimates are analyzed based on previously noted classification.
Economic Timeline
The oil industry, with its history of booms and busts, is in a new downturn. United States domestic production
has nearly doubled over the last six years, pushing out oil imports that need to find another home. Saudi,
Nigerian and Algerian oil that once was sold in the United States is suddenly competing for Asian markets,
and the producers are forced to drop prices. Canadian and Iraqi oil production and exports are rising year
after year. Even the Russians, with all their economic problems, manage to keep pumping. On the demand
side, the economies of Europe and developing countries are weakening and vehicles are becoming more
energy-efficient. So demand for fuel is lagging a bit. A central factor in the sharp price drops, analysts say, is
the continuing unwillingness of OPEC, a cartel of oil producers, to intervene to stabilize markets that are
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2. widely viewed as oversupplied. Iran, Venezuela and Algeria have been pressing the cartel to cut production
to firm up prices, but Saudi Arabia, the United Arab Emirates and other gulf allies are refusing to do so.
As noted previously, in order to understand what is happening in oil markets it is important rewind back and
understand what events led to the current situation of the market (in the year 2014),
• June ($112.36/barrel): The Middle East was ablaze, with Libya in chaos and Isis, the militant
Islamist movement, seizing territories across Syria and Iraq. Investors calculated the turmoil would
cut supplies and push oil prices towards $130 a barrel. Isis failed to capture the country’s main oil
producing regions in Iraq.Libya saw a turnaround.
• July ($106.20/barrel): Growth in the US was stellar. Monthly US crude production hit a near
30-year high, averaging around 8.5m b/d and would later surpass 9m b/d. But the Saudis doubted the
sustainability of shale, given its production costs — break-even prices range between $30 and $90 a
barrel for shale, compared with Saudi oil, which costs less than $10 a barrel to produce
• August ($103.19/barrel): As demand fell more than expected in Europe, Riyadh had been slow to
pick up on weakness elsewhere: China. A series of steep cuts to export prices for Asian buyers followed.
Chinese imports from Saudi Arabia, which stood at 1.3m b/d in January 2013, fell to around 900,000
b/d by August 2014.
• September ($96.46/barrel): Mr Naimi and other officials appeared sanguine. In September alone
Brent dropped almost $9 as capital flight from risky commodities to more secure investments such as
US treasuries hit the oil price.
• October ($85.86/barrel): Decision-making in Riyadh is often slow and lacks transparency. But the
first hint of a shift came with Mr Dossary’s remarks which filtered through the investment community.
• November ($70.15/barrel): The run-up to the November 27 Opec meeting in Vienna was dominated
by calls from the cartel’s poorest members, from Venezuela to Iraq, to cut production. But Saudi
Arabia was not prepared to shoulder the lion’s share of any Opec cuts. The Saudi oil minister is said to
have told the Russians that with both countries producing roughly 10m b/d, any potential cuts should
be equal. The Russians refused.
• December ($57.33/barrel): The Saudi stock market had slumped and business leaders openly
questioned the wisdom of allowing prices to fall.
The sample period from Jan 2015 to March 2015 is classified into information and non-information days.
Here is the list of information days long economic significance, the headlines.
• Jan 4: Obama sets stage for debate over US oil export ban.
• Jan 5: Brent oil falls below $53 for first time in over five years.
• Jan 7: Eurozone falls into deflation for first time since October 2009 and Drilling company Helmerich
& Payne told investors it would shut down 40 to 50 rigs over the next month amid softening crude
prices.
• Jan 12: Oil traders eye floating storage options.
• Jan 13: China’s oil imports climb above 7m barrels a day for first time.
• Jan 14: Shock Swiss move triggers market turmoil.
• Jan 20: BHP cuts shale investment amid drop in oil price.
• Jan 23: Saudi oil policy back in focus after King Abdullah’s death.
• Jan 28: BP scales back on two Gulf of Mexico fields.
• Feb 2: Nine US oil refineries hit by strike in pay dispute.
• Feb 3: BP slashes capital spending by 20% and BG in $8.9bn writedown of assets after oil price fall.
• Feb 9: Crude prices rally as Opec predicts rise in demand for its oil.
• Feb 11: Shell and BP in stand-off with Abu Dhabi over signing-on fee.
• Feb 13: Brent above $60 on oil company cutbacks.
• Feb 17: BP report predicts Opec comeback.
• Feb 23: Oil fall may trigger Opec emergency meeting.
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3. • Feb 25: UK chancellor eyes tax rate cut on North Sea oil producers.
• March 3: Iran hit by Indian crude purchase cuts.
• March 6: Oil retreats as prospects rise for Iran’s return.
• March 12: Caution on oil stocks as Brent remains below $60.
• March 15: US shale industry shows remarkable resilience.
• March 16: US oil falls to six-year low on rising stocks.
Microstructural Models
Structural Model
Microstructural Models are well documented in literature and one such model is considered. The efficient
price is denoted by mt, we assume that mt = mt−1 + wt , where wt as before the ut are i.i.d. zero-mean
random variable. At time t, there is a trade at transaction price pt , which may be expressed as below. We
also assume that buys and sells are equally likely, serially independent (a buy this period does not change the
probability of a buy next period), and that agents buy or sell independently of wt (a customer buy or sell is
unrelated to the evolution ofmt ). This model is most clearly explicated and analyzed in Roll (1984), and
will henceforth be referred to as the Roll model, but certain elements of the analysis were first discussed by
Niederhoffer and Osborne (1966). The Roll model has two parameters, c and variance of shock. These are
most conveniently estimated from the variance and first-order autocovariance of the price changes pt.
• pt is the observed trade price
• mt is the effective price
• qt is the direction of the trade (+1 for buy and -1 for sell).
• c are the non informational costs (transaction fee or other trading costs, fraction of bid-ask spread)
• λt is total information at in the last trade
• ρt correlation of trade direction with lag 1
• wt is information change (which is modelled in the following section)
pt = mt + c ∗ qt
mt = mt−1 + wt
wt = λ ∗ (qt − ρ ∗ qt−1) + ut
Hence the model
∆pt = (c + λ) ∗ qt − (c + ρ ∗ λ) ∗ qt−1 + ut
Information Asymmetry
Leverage effect is well understood in financial markets. Allowing for a leverage effect is just one way to extend
the basic GARCH model. I wanted to measure the information assymmetry θ using NGARCH Model. + σt
is varince in price changes observed till t. + Model
σ2
t = φ + α ∗ (∆pt−1 − θ ∗ σt−1)2
+ β ∗ σ2
t−1
Infomational Intensity
Informational Intensity can be measured by number of trades per unit time or volume trade per unit time. I
considered 1 min intervals and calculated volume traded in the interval and finally average of this vector
(will later consider a weighted average). All are considered in event time (not clock time, event-when a trade
happens)
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4. Price Impact
Current executable price is determined by the volume on wants to trade. As different volume amounts are
distributed at buy/ask sides of the order book, volume and direction determined effective executable price.
The relationship is not very easy to explain. There sound reason to consider an informational content in
the volume trade of previous transactions and affects the price. Just like the trade direction influences the
price change, there is evidence that previous volume traded influences the price change. I considered a simple
MA model with 1 lag to study one volume traded effects the price change. To be critical this is a linear
model (bad approximation) in reality the relationship is (step wise-quadratic) and well documented by Joel
Hasbrouck and other researchers. I considered a simple MA model with 1 lag to study one volume traded
effects the price change.
• Vt is the volume traded at t.
∆pt = λ1 ∗ Vt + λ2 ∗ Vt−1
Data
The data considered is Light Crude Oil Future (CL) traded on CME Globex. The data consists of all
transactions at the microsecond scale. The time period is from Jan 3, 2015 to March 20, 2015. All are
considered in event time (not clock time, event-when a trade happens).
For the analysis is important to establish the initial bid and ask levels at the beginning of the data. It is
slightly tricky to determine this by simply observing the transactions price. But as we know that transactions
happens only at best bid and best ask and the granular level (microsecond scale) it is unlikely that these
levels change immediately as comparatively lot of volume gets accumulated at best prices. So for each day I
identified the initial setting of best bid and best ask by comparing the first 10 transactions and imagining a
simple rolls model.
Results & Conclusions
• Histogram for prices changes (Jan 2, 2015 - Random selected day) - distributed around 0 (positve
skew and very high kutoris)- uneven tails (also need to consider the non continuity due to tick size
increments).
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5. • 1-lag autocorellation considered with transaction data. Time considered is event time.
• Buys/Sells come in clusters - very significant auto corellation in trade direcion
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6. • Following are the model estimates, black represents non-informational days and blue
represent informational days.
• All mean summary statistics are plotted with respect to information days vs non-
information days.
• Structural Model
• Lambda values - which implies the information in the previous trade are more significant in non-
information days than information days which indicates that on non-information days, the markets are
more drive by previous trades rather than fundamental information.
• Rho values - rho is the 1-lag autocorrelation among price changes, there is not a significant demarca-
tion when compared to information vs non-information days although its slightly higher during the
information days.
• c values - which represents half of spread is greater during the information days which is due to
the fact that, market makers would need to be compensated for going against the information flow.
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8. • Information Asymmetry and Informational Intesity
• Leverage effect (theta values) - are greater on non-informational days as compared to information days
which is consistent with common intuition. Generally on the information days, markets either rally or
move down significantly hence the value of theta estimated is so significant.
• Informational Intensity (Volume / Trade Intensity) - is again consistent with common intu-
ition and is significantly higher on information days when compared with non-information days.
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10. • Price Impact
• Lambda1 and Lambda2 values which are the impact due to lag 0 volume and lag 1 volume and do not
seem to be very different.
• The results are not conclusive of anything apart from that the price impact is greater on days which
are non-information (which is in a way related to the fact the volume traded is significantly lower on
non-informational days.)
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12. • Several other patterns seem to emerge like n-lag day information transition after the economic
event/change which need to be further analyses.
• Futher work could be conducted by classifying economic data more precisely as positive, negative and
neutral. We could futher attach other attributes.
• Price Impact model may to revised as a conclusive evidence could not be found.
• The paper provides good evidence that economic data is immediately transfered to markets as mi-
crostructural parameters shifts. Some economic changes are more significant that others and there is a
scope for further study.
References
• “Empirical Market Microstructure” by Joel Hasbrock
• “Public information arrival: Price discovery and liquidity in electronic limit order markets” by Ryan
Riordan Andreas Storkenmaier, Martin Wagener b, S. Sarah Zhang
• “Outlook vs. Futures: Three Decades of Evidence in Hog and Cattle Markets” by Colino E.V and
S.H.Irwin
• “A Computational View of Market Efficiency” by Jasmina Hasanhodzic, Andrew W. Lo, and Emanuele
Viola.
• “Latency, liquidity and price discovery” by Ryan Riordan, Andreas Storkenmaier
• http://www.ft.com/cms/s/2/25f2d7d6-c3f8-11e4-a02e-00144feab7de.html#ixzz3a1xjbiGP
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