daniel ahn speculation commodity prices

Speculation and Commodity Prices Anatomy of a quasi-bubble from 2006 to 2008

January 2009Daniel Ahn

Confidential Presentation

Speculation and Commodity Prices

A commodity price boom…WTI peaked up 93.4% and HH natural gas up 141.2% in July 2008. Corn peaked up 124.1% and Soybeans 185.8% in June.

Price Behavior of six selected commodities since Sep-07

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1

…or a speculative bubble?The coincidence of a broad commodity price appreciation and large financial index inflows have raised questions about causality

The stellar return performance of commodities as other traditional asset classes, such as equities and bonds, have wilted, has attracted a large number of purely financial investors into commodities markets.In particular, index funds have emerged as a primary channel for strategic allocation by large institutional investors. – Indices typically operate via total return swap agreements between the investor and the counterparty financial

institution, often banks. By engaging through a financial intermediary using swaps, these indices exploit the so- called swap loophole that exempts these investments from speculative position limits imposed by the CFTC.

Estimates of large index flows in commodities have coincided with dramatic increases and decreases in the price. Many market commentators and lawmakers have taken this as prima facie evidence for the role of speculative activity in driving up commodity prices, notably for crude oil.

Commodities as an asset class

AUM and Cumulative Inflow since 2006 Index Inflow into WTI and WTI prices

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Speculation and Commodity PricesDespite simplified ideological extremes, the topic is highly complex and requires careful and nuanced analysis

– Political constituents in the US, frustrated by record high food and energy prices, have blamed speculators and pressured Congress to crack down on financial activity.

– But speculators play an essential role in providing liquidity and information in markets so commercial participants can conduct effective risk management, bringing business costs and ultimately prices down.

– Many market observers and academics have also argued against the ability of purely financial actors to affect a futures price linked to physical markets. • One commonly cited “folk wisdom” is that every buyer needs a seller.

– But a more nuanced analysis is necessary before passing judgment.

Cost analysis and oil price forecasting

3

The “Financialization” of Commodity MarketsDaily trading volume on NYMEX crudes is over 6 times total global consumption; Trading volume on ICE crudes is over 5 times total global consumption.

Daily Trading Volume in WTI and Brent Crude Oil on NYMEX and ICE vs. World Oil Consumption

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World Crude Oil Consumption

Add OTC trading and the daily trading volumemay be >20 times global production

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A Discussion on Financial Activity and PricesWhile theory starts from perfectly efficient markets, reality allows financial activity to impact price behavior.

– Academic literature has a benchmark view of perfectly efficient markets:• Prices perfectly and instantaneously reflect all available information• Financial supply and demand curves are vertical

– Short-term liquidity constraints determine a bid-ask spread and sloped short-run supply curve• Hence, large orders can move along this curve and cause intraday price shifts

– Also, market participants continuously update expectations from inflow of public and private information.

– Opinions not unanimous, but differing beliefs are formed.– Trading activity, on top of its short-term liquidity effect, can also impact

expectations by signaling superior information.

Theoretical Ideal and Imperfect Reality

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A Discussion on Financial Activity and Prices (cont’d)Commodity markets are inelastic with respond to price and suffer from informational opacity.

– Skeptics argue that if speculators drive market prices above equilibrium, consumers would demand less than producers are supplying, and the excess supply must appear in inventories.

– But data on inventories is available only for the US and a few other OECD countries, not the entire world. Also, these numbers are subject to noise and microeconomic shocks, such as credit.

– Also, physical markets are weakly responsive to financial prices. Estimates of elasticities are low, as a large physical response requires high upfront fixed costs, e.g. to open a new producing basin or upgrade efficiency of equipment. This causes a nonlinear “kinked” reaction curve and an immediate inventory response will be weak.

Physical markets and financial prices are weakly linked

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Kinked Supply and Demand Curves Large fixed costs required for a substantial physical response from consumers and producers drive extreme short-run inelasticity to price.

Kinked supply and demand curves

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Nonlinear kinks in the supply and demand curve can arise due to the sunk costs required for a large supply or demand response

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Demand

Within price band, supply and demandinelastic

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A Discussion on Financial Activity and Prices (cont’d)Commodity markets are prone to market herding and clustering due to informational imperfections and illiquidity.

– Fundamentals of commodity markets can be opaque. More than 2/3 of proven reserves of crude oil are held by non-transparent national oil companies (NOCs). No credible data available on key aspects of global balance, such as Saudi spare capacity or Chinese inventories.

– Hence, market participants look to the behavior of others for guidance. While individually rational, this results in herding behavior.

– They also cluster around the handful of available signals, such as US inventories, even if they may not reflect global reality.

– Herding creates an opportunity for less-informed “noise” traders to distort expectations. A large order that drives prices can be misinterpreted as a bullish statement by a trader with superior information.

– Prices move to a new “sunspot” equilibrium.

Informational opacity and herding behavior

8

Commodity Index Flows and Price BehaviorIndex flows, by being large and idiosyncratic, can impact both returns and volatility in the short-term

– Index investors are:• Large in size and thus have substantial liquidity impact• Are almost entirely long-biased• Hold little new information not already known to traders

– They generally invest in commodities at idiosyncratic times for broader strategic exposure, hence are “noise” traders. (Because of this, they are the closest one has to “exogenous” inflow into markets.)

– But anonymous markets makes traders misinterpret an index inflow as a bullish statement by a trader with superior information.

– Index inflows can thus impact both prices and volatility.

Index flows and price behavior

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Financially-driven over- and undershooting Herding behavior can cause prices to deviate from true long-run equilibrium

Lehman Brothers Global Supply and Demand Balance

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Markets can only observe smallI + large error e due to imperfect Information and model uncertainty.

Hence, conditional expectation Pmkt ˜ E[Ptrue| I + e ] volatile and can persistently deviate from Ptrue.

Inventory build I due to market imbalance small due to priceinelasticity

Market pricePmkt

10

How large are index investors?Refined estimation methodology based on CFTC supplementary reports on Commodity Index Trading (CIT)

Our refined methodology allows us to estimate the total Assets Under Management (AUM) in commodity indices. (Details of the methodology are in the Commodities Special Report, “Index Inflows and Commodity Price Behavior).We estimate AUM rose from a negligible amount in 2002, to $77bn in Jan-06, to peak at $297bn in Jul-08. Of this increase, about $97bn was additional financial inflow. But since Jul-08 to Sep-08, AUM has decreased to $187bn, of which $52bn was financial outflow.

How large are index investors?

Estimated AUM and cumulative inflow into commodity indices

________________Source: CFTC, Bloomberg, Lehman Brothers Commodities Research

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How large are index investors? (cont’d)

– For WTI crude oil, we estimate there has been a cumulative index inflow of $33bn into a total WTI AUM of about $93bn in Jun-08. To put in perspective, the entire market capitalization of all WTI open interest on the NYMEX exchange is about $380bn. Hence, index investment accounted for about a quarter of all long interest in paper oil.

– Furthermore, index investment is almost purely long positions. The bottom right hand figure shows the breakdown of long/short positions of soybeans by category of investor.

Index investment, at its peak, accounted for roughly a quarter of all long interest in WTI paper oil.

History of Index Net Inflows Index investment is long-biased

________________Source: CFTC and Lehman Brothers Commodities Research

AUM and long-biased flow in WTI

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Turning poin

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Relative size of index investors

– We have seen how AUM in commodity indices have grown dramatically in absolute size.– However, our discussion above suggests we must understand this in the context of

explosive growth of overall market. – As a percentage of the overall size of the market and relative to non-index non-

commercial traders, relative index sizes remain generally flat with only scattered increases in some commodities.

While the commodity index investment has grown dramatically in absolute terms, its relative growth compared to the overall market is less impressive.

Breakdown of Open Interest in Cocoa Breakdown of Open Interest in Wheat


Relative size of index investors

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Individual trading size of index investors

– But from an individual perspective, index investors are the elephants in the trading pits. – Given there are only a handful of index vehicles making large strategic portfolio

allocations on behalf of large index investors, it is not surprising the average position size of index investors surpasses both commercial and non-commercial.

– In fact, they are so large that they can exceed the speculative position limit imposed by the CFTC on non-commercial traders.

While the relative size of index investors remained flat, index investors are still the largest individual traders by average position size.

Average open interest size by category Average size as % of speculative limit


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What drives index flows?

Commodities have good diversification properties, and serve as excellent hedges against the dollar and equity weakness, and higher inflation.WTI is generally negatively correlated to dollar, uncorrelated with the S&P, and positively correlated with 5-year breakeven inflation (a common measure of inflation expectations)Commodities also deliver a short-term risk premium, though in the long-run, commodity prices are cyclical and weakly mean-reverting.

We observe both strategic diversification purposes, but also tactical momentum-trading in commodity index flows.

60-day correlation of WTI to dollar, S&P, inflation Inflation-adjusted WTI, S&P, Dow Jones, 1861-2007


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1861 1871 1881 1891 1901 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001Crude Oil Spot Price Dow Jones 30 Stock Index S&P500 Stock Index

WTI, 1861=100; Dow , 1900=100; S&P, 1927=100

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What drives index flows? (cont’d)Signs of tactical momentum-chasing is worrisome, as a key building block of an unsustainable asset bubble

However, strategic motivations have been blurred with tactical reasons, as investors turned off by weak performance of traditional asset classes have sought higher “alpha.”We search econometrically, and find that dollar weakness, higher breakeven inflation, weaker S&P returns, higher VIX levels (a measure of market risk aversion), and notably past performance of the commodity index drive positive inflow into indices, some with statistical significance.We account for autocorrelation with a one-period lag (chosen by AIC) and use Newey-West HAC t-stats (details in report).

Statistical drivers of index flow

Causes of Index Inflows to GSCI and DJ-AIG

GSCI Inflow (R2=9%) Coeff. p-value DJ-AIG Inflow (R2=11%) Coeff. p-value

Dollar Returns -7.57 0.000 Dollar Returns -6.48 0.000 Breakeven Inflation +3.98 0.000 Breakeven Inflation +0.32 0.678

S&P Returns -1.37 0.499 S&P Returns -0.54 0.698 VIX levels +0.30 0.359 VIX levels +0.22 0.324

GSCI Past Performance +0.56 0.432 DJ-AIG Past Performance +3.14 0.000

Source : CFTC, S&P GSCI, DJ-AIG, Lehman Brothers estimates (Note: Bold represents statistical significance at the 1% level of confidence and italics at the 5% level of confidence.)

16

What impact do index flows have on returns and volatility?Using absolute measures of index flows, we generally find a positive and at times statistically significant impact of inflow on returns.

Regression on returns using absolute measures– We find upon regressing our absolute measures of index flows, this predicts a positive and at times statistically significant impact on weekly returns.

– Again, use one-period lag and HAC t-stats.

– In words, this result can be restated as: A $100mn inflow into the soybean oil market by index investors within a week predicts a 2.11% increase in the weekly return of soybean oil prices.

– The largest magnitude effects occur in less liquid commodity markets such as soybean oil, silver, and coffee.

– Interestingly, the sign of the WTI crude oil coefficient is negative, though not statistically significant.

Commodity Coeff. p-value

Soybean Oil 21.12 0.000 Corn 4.86 0.029 Cocoa 4.97 0.755 WTI Crude Oil -0.57 0.266 Cotton 7.45 0.036 Feeder Cattle -6.96 0.652 Gold 6.17 0.010 Heating Oil 4.70 0.011 Coffee 10.68 0.037 Kansas Wheat 4.13 0.759 Live Cattle 0.54 0.856 Lean Hogs 1.61 0.786 Copper 3.39 0.141 Natural Gas -0.06 0.959 RBOB Gasoline 0.68 0.557 Soybeans 6.64 0.000 Sugar 4.19 0.289 Silver 14.91 0.061 Wheat 3.98 0.312

Source: CFTC, S&P GSCI, DJ-AIG, Bloomberg, Lehman Brothers estimates (Note: Bold and underlined represents statistical significance at the 1% level of confidence, bold at the 5% level of confidence, and italics at the 10% level of confidence.)

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Absolute flow measures and impact on price volatilityStudying the impact on volatility, we find even more consistently positive coefficients, also at times with statistical significance.

Regression on volatility using absolute measures– We find the absolute value of index flows predicts a positive and at times statistically significant impact on weekly volatility as well.


– In words, this result can be restated as: A $100mn inflow into the soybean oil market by index investors within a week predicts a 5.14% increase in the annualized volatility of soybean oil prices.

– Again, the large magnitudes occur in less liquid commodity markets such as soybean oil, cocoa.


Soybean Oil 54.17 0.012 Corn 10.20 0.222 Cocoa 117.83 0.037 WTI Crude Oil 2.82 0.153 Cotton 26.67 0.022 Feeder Cattle 34.63 0.490 Gold -0.32 0.974 Heating Oil 11.76 0.068 Coffee 32.92 0.144 Kansas Wheat 25.73 0.603 Live Cattle 5.65 0.508 Lean Hogs 14.10 0.420 Copper 8.21 0.698 Natural Gas 1.77 0.823 RBOB Gasoline 0.87 0.012 Soybeans 16.98 0.014 Sugar 6.95 0.707 Silver 13.20 0.676 Wheat 66.24 0.000


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Relative flow measures and impact on price returnsHowever, using relative measures to adjust for the “liquidity” effect returns a dramatic reversal of results

Regression on volatility using absolute measures– One might argue that the previous returns are to be expected, as the same $100mn inflow naturally has a larger impact on smaller and less liquid commodity markets.

– To adjust for this and eliminate this “liquidity” effect, we try using a relative measure, considering the flow not in absolute terms but as a percentage size of the total open interest in the market.


– When we do this, the results change dramatically. Most coefficients become negative.

– A one bp increase in the index share of total open interest in corn within a weak is associated with a 1.47% decrease in returns.


Soybean Oil -21.47 0.458 Corn -146.96 0.002 Cocoa -158.78 0.003 WTI Crude Oil -47.96 0.181 Cotton -67.25 0.001 Feeder Cattle -2.17 0.857 Gold -101.15 0.000 Heating Oil -17.65 0.338 Coffee -65.44 0.031 Kansas Wheat -40.47 0.282 Live Cattle -22.18 0.098 Lean Hogs -45.17 0.078 Copper -11.47 0.542 Natural Gas 21.89 0.841 RBOB Gasoline -5.30 0.325 Soybeans -77.91 0.002 Sugar -29.11 0.349 Silver -167.94 0.001 Wheat -82.89 0.003


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Relative flow measures and impact on price volatilityUsing relative measures, the impact on volatility remains positive but statistical significance is lost

Regression on volatility using absolute measures– Unlike the results for price

returns, the coefficients on the volatility impact generally remain positive.

– However, they are now rarely statistically significant (with no coefficient significant at the 1% level of confidence.)

– The only coefficient at the 5% confidence level is for corn, with a one basis point increase in the relative index share predicting a 4.44% increase in annualized price volatility


Soybean Oil 104.30 0.360 Corn 444.20 0.011 Cocoa 198.54 0.369 WTI Crude Oil 127.99 0.434 Cotton 81.93 0.230 Feeder Cattle 56.97 0.169 Gold 86.20 0.507 Heating Oil 145.13 0.057 Coffee -201.60 0.229 Kansas Wheat 0.60 0.997 Live Cattle -1.06 0.982 Lean Hogs -53.21 0.563 Copper 129.18 0.215 Natural Gas -68.96 0.886 RBOB Gasoline 13.64 0.437 Soybeans 61.18 0.599 Sugar 149.60 0.317 Silver 344.57 0.165 Wheat 19.03 0.862


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Potential flaws in the relative measure

– If we look at a history of this relative measure, the negative results are to be expected. – For many commodities, the relative measure shrinks even as their absolute size and prices increase.

This asymmetry occurs due to the exogenous maturation of commodity futures markets, as both commercial and non-commercial participants also increase exposure.

– In fact, the CFTC and other analysts point to the stable or declining relative measure as evidence against index investors’ having any relation to price movements.

While the relative size of index investors remained flat, index investors are still the largest individual traders by average position size.

Absolute and relative measure in corn Absolute and relative measure in WTI


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Index Net Length as a Percentage of Total OI (lhs)

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Panel approach to price and volatility impact

– The exogenous growth in market size due to exogenous maturation of commodity futures markets poses problems to the relative measure. The previous absolute measure, while failing to adjust for liquidity, nevertheless captures the story of the increasing frequency and presence of large index buy orders in markets. We face a trade-off.

– The potential flaws in the relative measure encourage us to seek an alternative econometric approach. Happily, we can exploit the multiple commodity panel structure of the CIT data.

– Intuitively, rather than studying increased inflows over time and observing their impact on prices and volatility, we consider differences in index investment across commodities and try to statistically observe differences in return and volatility behavior.

By adopting a panel approach and studying cross-sectionally, we can use the relative measure and adjust for both the liquidity and exogenous growth effect

Panel approach to studying return and volatility impact

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Panel approach to price and volatility impact (cont’d)Signs of tactical momentum-chasing is worrisome, as a key building block of an unsustainable asset bubble

– Because time trends are captured in time dummy variables, we can avoid spurious correlation of the increasing trend in returns and negative trends in the relative measure.

– By using the relative measure, we also have adjusted for the liquidity effect.– However, the nature of panel regression precludes any commodity-specific analysis.– We find positive and statistically significant coefficients on both returns and

volatility, though the actual magnitudes are small.– A 1% increase in relative index size predicts a 22bp increase in the weekly returns

and a 54bp increase in annualized volatility.

Statistical drivers of index flow

Panel regression on price returns and volatility using relative measures

Model Coeff. t-statistic p-value

Returns 0.22 2.92 0.00 Volatility 0.54 2.10 0.04


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Debate over speculation and commodity prices continue

– During the peak of the price surge, difficult questions were raised about any possible causality between index flows and energy and other commodity prices.

– This has become a very politically charged debate, with several testimonies in Congressional hearings arguing for a causal link.

– On the other side of the debate, many observers argued that there is no evidence, either empirical or theoretical, linking speculative activity to prices.

– On 6/26/08, the House passed legislation to curb “excessive speculation” in energy markets, which was subsequently stalled in the Senate. Regulators have also moved to close the “London” loophole, extending position limits on the ICE exchange.

– On 9/11/08, the CFTC released a study with recommendations on regulation, but with some dissent and concern over the quality and completeness of data.

• Among the recommendations, they suggested reviewing the bona fide hedge exemptions given to swap dealers.

Political tensions rose high in the debate over speculation and commodity prices


24


– Our analysis suggests reality is considerably more complex and does not align with either extreme of the debate. We feel while prices may ultimately reflect long-term fundamentals, imperfections due to illiquidity and asymmetric information allows financial activity to drive short-term deviations.

– The empirical evidence returns a mixed bag:• Estimates of price impact depend on whether one uses relative or absolute

measures of inflow• Positive effect is concentrated in smaller agricultural and precious metals

markets, not the energy markets which have received so much attention• For volatility, picture more consistent but magnitudes small

– Debate clouded by short history and doubts about accuracy of available data. Weekly freq. may be too low to capture significant intra-day effects.

– Commodities markets are not theoretical ideals but imperfect human constructs. – The complexity of issue warrants intelligent and judicious regulatory consideration.

Political tensions rose high in the debate over speculation and commodity prices Debate over speculation and commodity prices continue

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Concluding thoughts

– It is easier in hindsight to call a price trajectory a speculative bubble (and the recent dramatic fall in energy and other commodity prices is suggestive).

– But there is a thin line between an irrational bubble and an overshooting by rational but ill-informed markets with herding behavior.

– Even for the NASDAQ in the 1990s, considered by many to be a classic example of an asset bubble, economists have struggled to conclusively prove that speculators drove prices upwards cognizant that prices were unreflective of fundamental value.

– As commodity markets continue to mature, distortions from speculative activity and commercial hedging should diminish. The global economy and its consumers will reap accumulating benefits from improved risk management and efficient trade.

– However, in interim, devoting more thought and resources to improving transparency in commodity markets is required. Extending supplementary index reports to all commodities, not just agriculturals, is an excellent first step.

– On fundamental side, more transparency is required in critical but opaque regions, such as Saudi Arabia and China.

Improvements in transparency can diminish distortions without affecting market ability to manage risk

Concluding thoughts on the debate over speculation and commodity prices

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Analyst Certification and Disclosures

Analyst CertificationI, Daniel Ahn, hereby certify (1) that the views expressed in this research report accurately reflect my / our personal views about any or all of the subject securities or issuers referred to in this report and (2) no part of my compensation was, is or will be directly or indirectly related to the specific recommendations or views expressed in this report.“To the extent that any of the views expressed in this research report are based on the firm’s quantitative research model, Lehman Brothers hereby certify (1) that the views expressed in this research report accurately reflect the firm’s quantitative research model and (2) that no part of the firm’s compensation was, is or will be directly or indirectly related to the specific recommendations or views expressed in this report.”Important DisclosuresLehman Brothers, Inc. and / or an affiliate thereof (the “firm”) regularly trades, generally deals as principal and generally provides liquidity (as market maker or otherwise) in the debt securities that are the subject of this research report (and related derivatives thereof). The firm’s proprietary trading accounts may have either a long and / or short position in such securities and / or derivative instruments, which may pose a conflict with the interests of investing customers.Where permitted and subject to appropriate information barrier restrictions, the firm’s fixed income research analysts regularly interact with its trading desk personnel to determine current prices of fixed income securities. The firm’s fixed income research analyst(s) receive compensation based on various factors including, but not limited to, the quality of their work, the overall performance of the firm (including the profitability of the investment banking department), the profitability and revenues of the Fixed Income Division and the outstanding principal amount and trading value of, the profitability of, and the potential interest of the firms investing clients in research with respect to, the asset class covered by the analyst. Lehman Brothers generally does and seeks to do investment banking and other business with the companies discussed in its research reports. As a result, investors should be aware that the firm may have a conflict of interest. To the extent that any historical pricing information was obtained from Lehman Brothers trading desks, the firm makes no representation that it is accurate or complete. All levels, prices and spreads are historical and do not represent current market levels, prices or spreads, some or all of which may have changed since the publication of this document. Lehman Brothers’ global policy for managing conflicts of interest in connection with investment research is available at www.lehman.com/researchconflictspolicy. To obtain copies of fixed income research reports published by Lehman Brothers please contact Valerie Monchi ([email protected]; 212-526-3173) or clients may go to https//live.lehman.com//.Legal DisclaimerThis material has been prepared and / or issued by Lehman Brothers, Inc., member SIPC, and / or one of its affiliates (“Lehman Brothers”). Lehman Brothers Inc. accepts responsibility for the content of this material in connection with its distribution in the United States. This material has been approved by Lehman Brothers International (Europe), authorised and regulated by the Financial Services Authority, in connection with its distribution in the European Economic Area. This material is distributed in Japan by Lehman Brothers Japan Inc., and in Hong Kong by Lehman Brothers Asia Limited. This material is distributed in Australia by Lehman Brothers Australia Pty Limited, and in Singapore by Lehman Brothers Inc., Singapore Branch (“LBIS”). 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daniel ahn speculation commodity prices

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