the volatility is the message_jp morgan_jan2011

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Commodity Markets Outlook and Strategy

Global Commodities Research

JPMorgan Chase Bank, NA

January 23, 2011

www.morganmarkets.com

Contents

Commodity Strategy

Commodity Correlations with Equities 1

Imputed Volatility Distributions 1

Risks Scorecard 1

Implications for Valuation and Risk 2

Price Forecasts and Sector Outlooks 2

Appendix - Asset Returns by Country 3

The volatility is the message:How commodities protect 51 country benchmark portfolios from inflation

Overview: On January 14, we shifted our tactical posture on several promptcommodity markets (precious metals, softs, steel inputs) from bullish to bearish

for four main reasons: (1) an expected slackening in physical demand with the

start of the Chinese New Year holiday on February 3, (2) overextended price

momentum, (3) universally bullish sentiment, and (4) approaching expiry for the

Jan-11 and Mar-11 contracts, where many of our recommended trades were

positioned. Since our call, declines in spot markets have been 6.3% in silver,

3.3% in gold, and 5.4% in zinc. We expect this soft patch to last for another 4-

to-8 weeks and view it as a normal correction. A more important theme for strategic

risk management in the commodity space is the rising likelihood that volatility

will increase and cross-asset correlations will weaken from their current cyclical

extremes, enhancing the value of commodity allocations as hedges in financial

portfolios over the next few years. This outlook spotlights the importance of the

expected variances and catalysts that underlie our average price forecasts.

In this issue, we plot the impact of a 10% allocation to commodities in balanced

portfolios for 51 countries, variously testing the S&P GSCI, S&P GSCI Enhanced,

DJ-UBS, and JPMCC Total Return Indices over the last five years, tracking the

effect on both month-to-month and cumulative returns. We also provide charts

showing where volatility stands relative to normal for 24 commodities and the

rolling path of correlations for the S&P GSCI against 8 regional equity price

indices. A table of European producer prices for vegetables shows that food

price inflation is slackening on the margin, for now.

Risks: Risk managers and policymakers have grown complacent about volatil-ity, which has been depressed across asset classes by the application of trillionsof dollars of monetary stimulus. Implied volatility is below normal in 23 of 36

markets in the JPMCCI. Yet, challenges to these easy policies are building in Asia

and Europe, as fears about inflation mount. We believe the risk of policy mistakes

(fiscal, regulatory, monetary, and trade) is rising worldwide. Other geopolitical

uncertainties are also building. The past few weeks have brought a revolution in

Tunisia and food riots in Algeriadisturbances which hold low-probability, but

high-impact potential for contagion effects in energy through supply disruptions

in Africa and the Middle East. At the same time, this weeks high-level discus-

sions between Beijing and Washington on a number of strategic partnerships

could contribute to cooling food price inflation and lower global LNG prices,

while lifting North American gas prices through increased Chinese investment in

US export liquefaction capacity.

Strategy: Tactical risk managers should have already started moving to harvestgains and preserve capital, especially precious metals producers. Strategic

investors and hedgers should use the evolving weakness to initiate or add to

positions. We expect S&P GSCI energy total returns to be 22% over the next 12

months. Petroleum will likely lead all other commodity sectors, offering the best

overall hedge to inflation. As such, oil-dominated commodity indices will likely

beat indices with a lighter energy focus.

Colin P. Fenton(1-212) [email protected]

Lawrence E. Eagles(1-212) [email protected]

Michael J. Jansen(44-20) [email protected]

Scott C. Speaker(1-212) [email protected]

Jeff G. Brown(65) 6882-2215

[email protected]

David G. Martin(44-20) 7777-0211

[email protected]

Peter K. Nance(1-713) [email protected]

Lewis A. Hagedorn(1-212) [email protected]

Jonah D. Waxman, CFA(1-212) 834-2203

[email protected]

Tobin Gorey(44-20) [email protected]

Sung K. Yoo(1-212) [email protected]

Ryan F. Sullivan(1-212) [email protected]

Shikha Chaturvedi(1-212) [email protected]

Elizabeth M. Volynsky(1-212) [email protected]

See back page for important disclosures, including investment banking

relationships.


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Colin P. Fenton (212) [email protected]



January 23, 2011

Exhibit 2: Commodity subindex total return forecast table

Percent

Source: S&P, J.P. Morgan Commodities Research. Note: total returns are gross returns before fees. Data as of

close on January 21, 2011.

Exhibit 1: Commodity total return forecast table

Percent

Source: S&P, J.P. Morgan Commodities Research. Note: total returns are gross returns before fees. Data as of

close on January 21, 2011.

Source: Bloomberg, S&P, J.P. Morgan Commodities Research. Data as of close on January 21, 2011.

Forecast

2008 2009 2010 2011YTD Next 12 Months

S&P GSCI -46.5 13.5 9.0 0.3 17.0

S&P GSCI Enhanced -41.1 21.6 12.2 1.3 19.0

DJ-UBS -35.7 18.9 16.8 -0.3 13.0

JPMCCI -35.0 20.5 13.8 0.4 19.0

Forecast

2008 2009 2010 2011YTD Next 12 Months

S&P GSCI -46.5 13.5 9.0 0.3 17.0

Energy -52.38 11.2 1.9 0.1 22.0

Non-Energy -31.11 16.9 26.3 0.8 7.1

Industrial Metals -49.02 82.4 16.7 -1.7 9.0

Precious Metals 0.48 25.1 34.5 -6.5 4.1

Agriculture -28.88 3.8 34.2 3.4 10.0

Livestock -27.42 -14.1 10.5 0.7 -5.2

80

85

90

95

100

105

110

115

120

125

31-Dec-09

28-Jan-10

25-Feb-10

25-Mar-10

22-Apr-10

20-May-10

17-Jun-10

15-Jul-10

12-Aug-10

9-Sep-10

7-Oct-10

4-Nov-10

2-Dec-10

30-Dec-10

S&P 500DJUBS

JPMCCIS&P GSCI EnhancedS&P GSCI

Exhibit 3: Total returns paths

Indexed to 100 on 31-Dec-09

The volatility is the message

The bullish strategic outlook for commodities remainsintact. Financial returns, especially in bond markets, are at

rising risk of disappointment as the business cycle enters

its next phase. We continue to forecast 19% 12-month total

returns for the JPMCCI and 22% for the S&P GSCI Energy

subindex.

Ten days ago, we turned cautious on the 2M horizon,especially in precious metals (gold, silver, palladium) and

steel inputs (nickel, zinc), on overextended price momentum

and the approaching Chinese New Year festivities (February

3). Oil, corn, and copper will likely traverse the soft patch

better.

Our analysis of how strategic commodity allocations in 51countries fared in 2006-10 concludes that the asset class

provided significant benefits in protecting investment

performance exactly when it was needed most (2007-09), at a

low long-run cost for the insurance. Commodities worked.

Despite perceptions of strong commodity price moves,implied volatility is below normal in 23 of 36 JPMCCI

commodities. This cannot last.

In the US West, oil and gas producers are underestimatingtheir F/X and credit risk; oil and gas consumers are

overestimating commodity supply reliability. Expect equity/commodity correlations (now +0.68 between

the S&P 500 and S&P GSCI price indices) to weaken from

here over the course of the rest of the cycle, reaching -0.25

within 4-to-6 years.

An eminent biologist in the prime of his life was diagnosed with

an extremely rare and aggressive form of cancer. Told by his

physicians that his expected lifespan was brief, the scientist

began furiously researching his condition. He learned that the

average survival rate from time of diagnosis was eight months.

Armed with this brutal empirical fact, he felt despair.

However, his depression soon lifted, when he realized that the

reported average was the median survival rate, meaning that

half the population with his condition lived longer than eight

months. Moreover, he suddenly understoodand confirmed

upon further researchthat the odds of survival were not nor-

mally distributed and in fact had a long right tail, meaning that

some people (the youngest and healthiest at time of diagnosis)

could live for years. The young scientist was moved by relief

and a sense of duty to explain to other cancer patients what he

had experienced and learned. He penned a famous essay to

encourage others not to give up when told the odds of average

life expectancy. He called it: The median isn't the message(http://people.umass.edu/biep540w/pdf/Stephen%20Jay%20Gould.pdf ).

He lived for another 20 years, ultimately dying from an unre-

lated cancer. His framing of the initial illness echoes his most

famous scientific contribution: the concept that evolution of

species is not a smooth process of change, but rather one punc-

tuated by long pauses and sudden periods of advance and

decline.

We have been thinking about this true story as we have watched

commodity markets over the past few months. Like the scientist's


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January 23, 2011

sources, conventional commodity analysis is focused on aver-

ages: average prices, average demand and supply, averagetrade flows. And yet, as the scientist so poignantly learned, it

is the dispersion around the average, not the average itself,

which is more important for risk managers today. After two

years of zero interest rate policy, the volatility is the message.

Volatility across asset classes is unsustainably low. The driver

of the very low vol within commodities and across asset classes

is not much in dispute: it is the consequence of trillions of

dollars of stimulus by fiscal and monetary authorities. The

stimulus has been reflating all asset prices at more or less a

consistent pace, while also broadly reducing fears about the

big macro factors that could go wrong.

To help frame just how powerful the latent inflationary pres-

sures of these maneuvers may be, we present a novel way of

showing the impact of the Fed's balance sheet acrobatics on

the money supply (Exhibit 4). Rather than looking at a conven-

tional measure of y/y growth in M2, this exhibit plots the value

of various equity and commodity benchmark valuations as a

percentage of the Fed's total liabilities. If one were to accept

the pre-crisis regime in this chart as a reasonable portrait of

baseline valuation, then in the current regime moves of more

than 200% would be needed to get back to pre-crisis levels in

equities and 66% in commodities. Now, we do not believe that

the Fed would tolerate this scale of reflation in commodities

within a short period of time, nor do we think the prior level isreally the objective. Stability around the new baseline is the

objective. But the chart does provide a useful way of quantify-

ing and contextualizing the asset price deflation of 2008-09 and

the potential inflationary pressures that have been unleashed

Source: Bloomberg, S&P, J.P. Morgan Commodities Research. Data as of January 14, 2011.

Exhibit 4: Open interest of 43 listed commodity futures markets and

0

10

20

30

40

50

60

70

2004 2005 2006 2007 2008 2009 2010 2011

Listed Commodity Futures Markets S&P 500 NYSE BBG World Equity Index

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4.940.42

17.27

by the policies to combat deflation. And maybe we are wrong:

maybe this chart is a roadmap where nominal asset prices mayultimately go, if USD debasement follows the path that some

inflation super-hawks fear.

Recently, a wide range of data has supported the view of rising

inflation risks. The central bankers' intentional reflation

supplemented by powerful, weather-driven price spikes in agri-

cultural marketsis already showing up in measures of core

inflation in Europe, where recent readings have exceeded the

upper boundary of targeted bands in the UK and Eurozone.

Since November, fears about food price inflation have been

mounting, prompting a number of countermeasures in Asia. The

inflation in Asia is in turn generating consternation about

whether the ECB should or will follow with rate hikes, and ifthey do, whether the Fed will be forced to follow earlier than it

might otherwise have, especially if the US unemployment rate

declines at the rate of, say, 0.1%-points per month.

We too have shared some of these concerns. However, our

research finds that over the past three weeks, data for European

producer prices for 17 cash vegetable markets show that all but

two markets (onions and potatoes) have slackened sharply (Ex-

hibit 5). While prices for lettuce, peppers, fennel, and spinach

are all more than 40% higher than a year ago, the lettuce price

has dropped by 22% this month. This evidence of a downshift

in non-exchange-traded ingredient prices is comforting from an

inflation-management perspective and reinforces our convic-tion that consumer pricing pressures are still better thought of,

even in EM, as beneficial reflationary signals rather than sus-

tained inflationary threats. By this statement, we mean that the

inflation that is entering and propagating through the global

price network is indeed powerful but it still looks to be well

controlled and centered in upstream producer price channels,

not yet aggregate final prices.

Recall that garlic was one of the ingredient markets that drove

the inflation scare in China in November. Our European data set

confirms meaningful price inflation in this cash commodity mar-

ket: garlic prices are still about 9% higher than at the end of

November, 21% higher since the Fed's Jackson Hole conference

at the end of August, and 68% higher than a year ago. However,

the European data also show that garlic prices have dropped by

about 3% ytd. Zucchini prices have been falling since Septem-

ber and, at 0.68 per kg, are now reported to be 34% below a

year ago. Even in the vegetable markets where prices are rising,

the trend is decelerating price appreciation. Moreover, in fruit

and nut markets, the surges that occurred in the harvest season

of September and October 2010 have generally leveled out at

those higher levels. This looks to us more like weather-related

pricing rather than a monetary effect.

various index market capitalizations against Federal Reserve Liabilities

Percent


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Exhibit 5: Tracking food price inflation in non-exchange traded ingredients

Producer prices, Italy

Source: ISMEA, J.P. Morgan Commodities Research

Current Price

(EUR/KG) Dec Nov Oct Sep Aug Year Ago

Fruits

Apricot 0.78 0.0% 0.0% 0.0% 0.0% 0.0% -37.6%

Apple 0.59 1.7% 3.5% 7.3% 7.3% 0.0% 28.3%

Granny Smith Apple 0.57 1.8% 1.8% 1.8% 78.1% 32.6% 103.6%

Cherries 2.72 0.0% 0.0% 0.0% 0.0% 5.8% 47.0%

Grapes 0.40 0.0% 0.0% 29.0% -14.9% -24.5% -20.0%

Kiwi 0.85 -3.4% 4.9% 34.9% 3.7% 3.7% 32.8%Lemon 0.34 -2.9% -2.9% -35.8% -46.9% -29.2% 6.3%

Melon 0.80 0.0% 0.0% 0.0% 0.0% 166.7% 233.3%

Nectarine 0.46 0.0% 0.0% 0.0% 0.0% 12.2% 58.6%

Orange 0.24 -7.7% -17.2% -20.0% 9.1% 9.1% 0.0%

Pear 0.96 1.1% 3.2% 11.6% 23.1% 57.4% 31.5%

Nuts

Almonds 3.14 -0.3% 1.6% 1.6% 1.6% 23.1% 26.1%

Hazlenuts 1.98 0.5% 1.5% 8.2% 8.8% -8.3% 2.1%

Vegetables

Aubergine 0.50 -47.9% -26.5% -43.2% 194.1% 194.1% -2.0%

Carrots 0.14 -12.5% 55.6% 55.6% 40.0% -44.0% -17.6%

Celery 0.26 -21.2% 13.0% -3.7% -35.0% -18.8% -10.3%

Cucumber 0.65 -38.1% 8.3% 85.7% 261.1% 261.1% 71.1%

Endive 0.32 -5.9% 18.5% 23.1% 23.1% -15.8% 28.0%

Fennel 0.40 0.0% 53.8% 8.1% -25.9% -7.0% 42.9%

Garlic 3.11 -2.8% 8.7% 2.3% 14.3% 21.0% 68.1%

Green Beans 1.10 0.0% 0.0% 64.2% 42.9% 0.0% -26.7%

Lettuce 0.46 -22.0% 35.3% 70.4% 70.4% 39.4% 43.8%

Onion 0.38 11.8% 11.8% 18.8% 46.2% 35.7% 22.6%

Peppers 0.70 -39.1% 14.8% -4.1% 180.0% 180.0% 40.0%

Potato 0.37 2.8% 8.8% 12.1% 15.6% 37.0% 54.2%

Radicchio 0.54 -5.3% 63.6% 74.2% 42.1% -6.9% -11.5%

Spinach 0.88 -3.3% 79.6% 76.0% 125.6% 125.6% 46.7%

Tomato 0.61 -41.9% 17.3% 27.1% 144.0% 190.5% -12.9%

Cherry Tomato 1.30 -42.2% 51.2% 88.4% 154.9% 170.8% 47.7%

Zucchini 0.68 -39.8% -8.1% -1.4% 78.9% 78.9% -34.0%

Price change (%) since end of month in:


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-20

-10

0

10

20

30

40

50

60

99 00 01 02 03 04 05 06 07 08 09 10

China CPI - Meat China CP I - Food

100

150

200

250

00 01 02 03 04 05 06 07 08 09 10

-40

-20

0

20

40

60

80

2003 2004 2005 2006 2007 2008 2009 2010

Perhaps a better signal to track in monitoring Chinese food

price inflation riskand whether Beijing's administrative mea-sures are appropriately or excessively curbing price momen-

tumis the price ofpork. Pork is widely consumed in Asia, as

it is elsewhere. The relatively short life cycle of pigs and the

relatively low capital investment per pig allow pork production

to adjust swiftly to market prices, making it a useful indicator

for analysis.

Pork prices in China poked their head above zero annual growth

in 3Q2010 for the first time in two years (Exhibit 6), and US pork

prices are also back to trend (Exhibit 7). This price appreciation

in China and the US return to trend is important because of

pork's relatively high weights in CPI measures. The apprecia-

tion in meat prices is clearly helping guide China's food CPI intoits third period of inflation in the past 10 years (Exhibit 8). This

trend will need to be monitored closely. Our JP Morgan col-

leagues on our sales and trading desks report anecdotally they

have been seeing a pickup in inquiries about livestock futures

contracts and livestock index swaps from institutional inves-

tors around the world. This anecdotal seems to be a valid

indicator of rising inflation expectations and a well-reasoned

attempt to hedge this risk.

Yet, consensus expectations about volatility remain very com-

placent, even among those who say they are worried about

inflation. The NYM natural gas market, normally second only

to non-storable electricity as the most volatile commodity, hasbeen behaving as if there are no limits to North America's stor-

age and pipeline capacities. We understand that the term struc-

ture for NYM gas implied volatility this week does not have a

level above 40% along the entire length of the curve: CY2011

averages about 34%, CY2012 averages is valued at about 21%.

In oil, even as the market has moved into a global deficit, draw-

ing inventories and lifting Brent by $15/bbl to nearly $100/bbl

in the space of about two months, average implied volatility is

only at about two-thirds of the normal saddle of the vol smile, a

measure of neutral (Exhibit 9). Petroleum products' prompt im-

plied volatilities are about 15 vols below their normal of 37%-to-

39%. In aluminum, the largest base metal market by quantity,

prompt implied vol is about 21.7%, or 6 vols lower than normal.

In copper, the base metal most tied to Chinese import demand

and power capex, prompt implied vol is about 28.4%, or about 8

vols lower than normal. In the few markets that are showing

elevated volmostly commodities in the agricultural spaceit

is weather driven and far above normal. Prompt sugar implied

vol is at about 55% versus 34% normally (1.6X); cotton is near

50% versus 27% normally (1.8X).

Source: National Bureau of Statistics, J.P. Morgan Commodities Research

Exhibit 6: China PPI - hogs

Y/Y percentage change, quarterly

Source: Bureau of Labor Statistics, J.P. Morgan Commodities Research

Exhibit 7: US CPI - pork

Index: 1982-84 = 100

Source: CEIN, J.P. Morgan Commodities Research

Exhibit 8: China CPI - meat and food

Y/Y percentage change


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We can tease out what is likely on the minds of investors by

using Google's Trends tool, which plots a search volume index

and a news reference volume index for any keyword. We ran six

queries to try to get a sense of global consensus impressions

about three themes: (1) Chinese investment in North America,

(2) sovereign debt risk around the world, and (3) pressure points

in inflation (Exhibits 10-15).

The results were illuminating, offering some empirical evidence

to back up our views. Our first test was CNOOCand Unocal.

More than five years ago, CNOOCs unsuccessful attempt to

acquire California-based Unocal (now a subsidiary of Chevron)

generated much political noise and huge headlines, which isreadily evident in the spike in search and news volume in 2005.

However, CNOOCs more recent strategic partnership with an-

other North American energy producer has gone virtually un-

noticed by the general public and even by the mainstream me-

dia. There are almost no fluctuations in the search and news

reference data at all, even though this publicly-announced

CNOOC venture allows for shale gas technology transfer and

may very well evolve into one, among many, channels to export

US gas to China in coming decades. We notice, for example,

that over the past three months several energy companies have

applied to the US government for licenses to export natural gas

in the form of LNG, with a special interest on processing Mid-

continent shales and floating the cargoes out of Louisiana ports.

As we have pointed out before, in the past year the US (with its

20% world production share) has overtaken Russia as the larg-

est gas producer in the world, but the US still exports only 4%

of its production. The inevitable increase in this export volume

will have titanic implications for commodities, currencies, and

rates markets.

The data suggest to us that the Chinese energy industry learned

an important lesson in the Unocal experience. Chinese invest-

ment into North America is now successfully pursuing a more

Exhibit 9: Daily price volatility, prompt NYM WTI crude oil (CL1)

Percent

Source: NYMEX, J.P. Morgan Commodities Research. Data as of 21-Jan-2011.

0

10

20

30

40

50

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

May-10

Jun-10

Jul-10

Aug-10

Sep-10

Oct-10

Nov-10

Dec-10

Implied Actual

24.06

18.39

low-key partnering strategy that will ultimately work in meeting

China's objectives of securing more energy supplies and moreenergy-related technologies. We see parallels with the strate-

gic route taken by the Japanese automakers in the 1980s and

1990s, when they finally successfully penetrated the North

American car market through investments in manufacturing

plants in Kentucky and Tennessee (not Michigan) that em-

ployed American workers. One Japanese automaker soon

climbed to the top ranks in US public satisfaction surveys and

stayed there unchallenged for more than a decade, until a con-

troversy over brakes emerged over the past couple of years.

Beijing has a similar opportunity to recycle its vast paper trove

of US Treasuries into more productive junior ownership shares

in hard asset liquefaction and export infrastructure, helpingresolve some of the more contentious disputes with Washing-

ton and other sovereign allies about trade flow and currency

valuations. We acknowledge that the knee-jerk response of the

US public may be to protest the export of domestic US gas to

China; however, this supply channel makes enormous economic

sense. A sizable Chinese investment in the Port of New Or-

leans, marketed with an emphasis on the sheer number of jobs

that would be created in helping revitalize that great world city,

would likely quickly gain traction on the US Gulf Coast and

even more closely integrate these two great economies, to the

net benefit of all countries, in our view.

Somewhat contrary to the subtlety of this China investmenttheme, F/X is increasingly a front-and-center issue. The search

volume index for RMB shows a broadly rising trend since

2005, with recent observations nearly matching the intensity of

interest in mid-2005. News references show a similar pattern.

This result reinforces our conviction that China, Canada, and

the US will in time come to understand that structuring long-

run gas supply contracts (of several bcf per day) is in their

mutual interest and will help alleviate a number of their indi-

vidual challenges. If this happens as we envisage, then Aus-

tralia-origin and Qatari-origin LNG prices will likely fall and US

Henry Hub gas prices will likely rise, from current levels.

Turning to our second theme (sovereign debt risk around the

world), public interest in quantitative easing surged in

4Q2010, with the search volume index surpassing 25.0 against a

late 2008/early 2009 peak of about 8.0. However, the Google

data now show that QE2 is no longer the focus it was just a few

months ago: the news reference volume index has returned to

its baseline, while the 4Q2010 spike in search volume has now

been nearly erased. Similarly, the Google search data for credit

default swaps, a term that one could not escape in late 2008,

now imply no special interest from the general public or even

the media. A flurry of stories related to Greece and the EU/IMF


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January 23, 2011

Source: Google, J.P. Morgan Commodities Research

Exhibit 10: Google Trends (CNOOC and UNOCAL)

Exhibit 11: Google Trends (Quantitative Easing)



Exhibit 12: Google Trends (Inflation)


Exhibit 14: Google Trends (Credit Default Swaps)


Exhibit 13: Google Trends (RMB)

Exhibit 15: Google Trends (Food Inflation)



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Did commodities work as strategic

portfolio hedges during the 2007-to-2009collapse in asset prices?

As inflation expectations begin to increase, it is a good time to

revisit whether commodity index strategies worked as advertised

in the 2008 financial crisis and the early stages of the economic

recovery. Examining the empirical evidence across 49 countries

in local currencies and 51 countries in US dollars, testing against

four benchmark commodity indices, we find that commodities

did in fact protect portfolios when they needed it most. The

summary tables on the following pages (Exhibits 16 and 17)

show returns and volatility for all of the country portfolios

during three intervals: pre-crisis (Jan-06 through Jun-07), crisis

(Jul-07 through Feb-09), and post-crisis (Mar-09 through Dec-

10). The numbers in blue indicate that a 10% allocation to oneof the commodity indices either outperformed the balanced

portfolio and/or lowered the overall volatility. We also present

a visual summary of the month-on-month effects and cumulative

impacts through time, by country and commodity index product,

in the Appendix on pages 37-86.

A number of lessons came out of the analysis:

Between January 2006 and October 2008, 31 of the 51 country

portfolios (in US dollars) with a 10% allocation to the S&P

GSCI total return index outperformed the balanced portfolio

with a median benefit of more than 200bp. An even greater

number of countries benefited from allocations to the S&P

GSCI enhanced total return index (49 of 51) as well as the

JPMCCI total return index (47 of 51), while 32 of 51 country

portfolios gained from an allocation to the DJ-UBS total return

index (Table 1). The benefits were also evident in local

currency terms over the same interval, as 90% of country

portfolios with an allocation to the JPMCCI total return index

and 96% with an allocation to the S&P GSCI enhanced total

return index outperformed the balanced portfolio (Table 2).

bailout pushed up the news volume index last spring, but the

latest reading in that index is presently back to its long-runbaseline even with the recent incremental concerns about Ire-

land and Spain. We suspect these results would likely surprise

most investment professionals.

Finally, we also ran Google trend tests on inflation and food

inflation. The charts that resulted reveal two main facts: (1)

journalists are more worried about consumer price inflation than

the general public: news reference volume has been increasing

since the end of 3Q2010 and appears poised to challenge the

2008 highs, while search volumes are substantially below the

average from 2004 to 2008, (2) interest in food price inflation has

surged in both searches and news reports, scaling to new highs

above the 2008 levels. The data confirm in stark relief whatmany portfolio managers and analysts (including us) seem to

believe: there is presently a differentiated focus on food price

inflation, rather than overall inflation. Should the former con-

cern continue to lift the latter concern, as measured by this

data, then we would expect other measures of inflation expecta-

tions to rise, putting pressure on policymakers to raise interest

rates. Rising US interest rates will in turn likely help drive

deterioration in the presently very high correlations between

equity and commodity prices.

Looking forward, the timing of increases in commodity price

volatility and consumer price inflation is debatable. But the

primary risk for portfolio returns is clear: it is inflation. Ibbotsondata for US large-cap equities back to 1926 reveal that the turn

from falling CPI inflation to rising CPI inflation significantly

curbs expected equity total returns. When CPI inflation moves

to above trend and rising, which is likely not imminent, ex-

pected annualized blue chip equity total returns fall close to

zero. If commodity index investments are to be a tool to manage

food price inflation specifically and the portfolio damage caused

by consumer price inflation more generally, it is important to

ask whether they can be expected to work.

S&P GSCI totalreturn index

S&P GSCIenhanced

return index

DJ-UBS totalreturn index

JPMCCI totalreturn index

% COUNTRIES HELPED 61% 96% 63% 92%

QUANTIFYING THE BENEFIT, IF ONE EXISTED

MEDIAN 2.1% 2.5% 1.6% 2.2%MAX 17.2% 18.1% 16.8% 18.0%MIN 0.2% 0.1% 0.1% 0.2%

QUANTIFYING THE COST, IF ONE EXISTED

MEDIAN -1.1% -0.9% -0.6% -0.3%MAX -0.1% -0.6% -0.1% 0.0%MIN -3.4% -1.2% -2.5% -1.1%

Table 1: Impact of commodity allocations on country portfolios, USD

Quantifying monthly portfolio benefits from Jan-06 through Oct-08, percent

Source: S&P, Dow Jones, J.P. Morgan Commodities Research. Note: Results are monthly median

benefits not cumulative over the interval..


9/89

9





January 23, 2011

Source: S&P, Dow Jones, J.P. Morgan Commodities Research. Note: return is the average annualized return based on monthly observations. Volatility is the annualized standard deviation of the monthly returns.

Exhibit 16: Impact of adding commodity allocations to country benchmark portfolios, USD

Original Original Original

Country 60/40 S&P GSCI S&P GSCI En DJ-UBS JPMCCI 60/40 S&P GSCI S&P GSCI En DJ-UBS JPMCCI 60/40 S&P GSCI S&P GSCI En DJ-UBS JPMCCI

Return 2.2% 1.9% 2.7% 2.0% 2.4% -78.7% -74.3% -73.3% -73.0% -72.9% 60.8% 56.3% 56.5% 56.6% 56.6%

Volatility 9.9% 8.0% 8.1% 8.4% 8.2% 58.6% 55.5% 55.4% 54.2% 54.7% 24.0% 22.9% 22.7% 22.4% 22.5%

Return 22.1% 19.2% 20.5% 20.3% 20.8% -35.1% -35.5% -34.5% -34.5% -34.3% 36.1% 33.9% 34.1% 34.2% 34.1%

Volatility 7.8% 7.9% 8.0% 7.8% 7.9% 28.5% 29.1% 28.9% 28.1% 28.4% 18.1% 17.8% 17.7% 17.4% 17.5%

Return 19.0% 16.5% 17.7% 17.6% 18.0% -59.2% -57.2% -56.1% -56.0% -55.9% 33.9% 32.0% 32.2% 32.3% 32.2%

Volatility 8.4% 7.9% 8.1% 7.9% 8.0% 38.6% 38.1% 38.1% 37.0% 37.4% 22.5% 21.6% 21.4% 21.2% 21.3%

Return 21.4% 18.6% 19.9% 19.7% 20.1% -54.6% -52.9% -51.9% -51.8% -51.7% 27.7% 26.4% 26.6% 26.7% 26.7%

Volatility 6.3% 6.0% 6.2% 6.0% 6.2% 37.1% 36.4% 36.4% 35.4% 35.8% 17.5% 16.8% 16.7% 16.6% 16.7%

Return 39.4% 35.4% 36.2% 35.5% 35.9% -23.4% -25.0% -24.0% -23.9% -23.8% 33.6% 31.7% 31.9% 32.0% 31.9%

Volatility 8.5% 7.2% 7.4% 7.4% 7.4% 34.8% 34.9% 34.8% 33.9% 34.2% 18.9% 18.5% 18.3% 18.2% 18.2%

Return 21.2% 21.2% 21.9% 20.1% 21.3% -64.5% -61.9% -60.8% -60.7% -60.6% 16.8% 16.6% 16.8% 16.9% 16.9%

Volatility 9.3% 8.9% 8.9% 8.8% 8.8% 45.2% 44.2% 44.0% 43.0% 43.3% 20.0% 18.9% 18.8% 18.7% 18.8%

Return 15.3% 13.1% 14.3% 14.2% 14.6% -24.9% -26.4% -25.4% -25.4% -25.2% 29.0% 27.5% 27.7% 27.8% 27.8%

Volatility 7.7% 8.0% 8.0% 7.8% 8.1% 23.4% 24.8% 24.7% 23.8% 24.1% 15.4% 15.3% 15.2% 15.0% 15.1%

Return 30.7% 27.5% 28.3% 27.6% 28.0% -16.9% -18.9% -17.9% -18.0% -17.8% 35.9% 33.7% 33.9% 34.0% 34.0%

Volatility 7.3% 6.4% 6.5% 6.4% 6.6% 23.0% 23.2% 23.2% 22.4% 22.8% 10.8% 11.1% 10.9% 10.7% 10.8%

Return 33.6% 29.6% 30.9% 30.7% 31.2% -20.5% -22.1% -21.1% -21.1% -20.9% 25.5% 24.4% 24.6% 24.7% 24.6%

Volatility 9.9% 9.3% 9.4% 9.1% 9.2% 31.2% 30.5% 30.4% 29.8% 30.0% 13.7% 14.0% 13.8% 13.5% 13.6%

Return 22.6% 20.3% 21.1% 20.4% 20.8% -19.4% -21.1% -20.1% -20.1% -20.0% 41.5% 38.9% 39.1% 39.2% 39.2%

Volatility 13.4% 11.4% 11.7% 11.5% 11.5% 29.2% 28.9% 28.8% 28.0% 28.3% 17.5% 16.4% 16.3% 16.4% 16.3%

Return 19.9% 17.2% 18.5% 18.3% 18.8% -30.1% -31.1% -30.0% -30.0% -29.8% 28.1% 26.8% 27.0% 27.1% 27.1%

Volatility 11.4% 10.9% 11.1% 10.8% 10.9% 32.0% 32.4% 32.3% 31.3% 31.7% 19.7% 18.8% 18.7% 18.5% 18.6%

Return 21.5% 18.7% 20.0% 19.8% 20.2% -34.4% -34.9% -33.9% -33.8% -33.7% 30.8% 29.2% 29.4% 29.5% 29.4%

Volatility 7.7% 7.2% 7.3% 7.4% 7.4% 28.1% 28.6% 28.5% 27.5% 27.9% 16.9% 16.5% 16.3% 16.1% 16.2%

Return 18.4% 16.4% 17.2% 16.5% 16.9% -29.5% -30.5% -29.5% -29.4% -29.3% 25.4% 24.4% 24.6% 24.7% 24.7%

Volatility 9.4% 8.6% 8.9% 8.5% 8.7% 29.6% 30.1% 30.0% 29.1% 29.4% 17.3% 16.3% 16.3% 15.9% 16.1%

Return 27.5% 24.8% 25.7% 24.9% 25.3% -68.4% -65.4% -64.4% -64.3% -64.1% 38.2% 36.1% 36.3% 36.4% 36.3%

Volatility 20.0% 17.3% 17.2% 17.4% 17.2% 40.3% 39.6% 39.5% 38.5% 38.9% 27.3% 25.3% 25.2% 25.0% 25.1%

Return 22.3% 19.4% 20.7% 20.5% 21.0% -42.6% -42.1% -41.1% -41.1% -40.9% 28.8% 27.4% 27.6% 27.7% 27.7%

Volatility 8.8% 8.3% 8.4% 8.3% 8.5% 26.9% 27.0% 26.9% 26.2% 26.4% 21.3% 20.1% 20.0% 19.9% 19.9%

Return 19.1% 16.5% 17.8% 17.6% 18.1% -35.0% -35.3% -34.3% -34.3% -34.1% 24.1% 23.1% 23.4% 23.4% 23.4%

Volatility 6.2% 6.2% 6.3% 6.0% 6.3% 24.4% 24.9% 24.8% 23.9% 24.2% 19.2% 18.5% 18.4% 18.3% 18.3%

Return 23.3% 20.4% 21.6% 21.4% 21.9% -37.1% -37.2% -36.2% -36.2% -36.0% 29.2% 27.8% 28.0% 28.1% 28.0%

Volatility 7.0% 6.7% 6.9% 6.7% 6.9% 27.5% 27.8% 27.7% 26.8% 27.1% 17.7% 17.3% 17.1% 17.0% 17.1%

Return 20.9% 18.2% 19.5% 19.3% 19.7% -52.3% -51.0% -49.9% -49.8% -49.7% 2.9% 4.2% 4.4% 4.6% 4.5%

Volatility 9.3% 8.3% 8.5% 8.5% 8.5% 35.9% 35.5% 35.4% 34.3% 34.7% 30.3% 28.5% 28.3% 28.1% 28.2%

Return 18.1% 15.6% 16.9% 16.7% 17.2% -23.1% -24.5% -23.5% -23.6% -23.4% 32.6% 30.7% 30.9% 31.0% 31.0%

Volatility 5.7% 5.5% 5.6% 5.4% 5.5% 25.8% 26.1% 26.1% 25.2% 25.5% 15.2% 15.1% 15.0% 14.8% 14.8%

Return 33.0% 29.7% 30.4% 29.8% 30.1% -65.0% -62.3% -61.2% -61.1% -61.0% 34.5% 32.6% 32.8% 33.0% 32.9%

Volatility 13.1% 11.9% 12.1% 11.5% 11.8% 44.0% 42.9% 42.7% 41.6% 42.1% 28.4% 26.7% 26.7% 26.4% 26.5%

Return 42.5% 40.4% 41.1% 39.3% 40.5% -103.6% -96.0% -95.0% -94.5% -94.5% 32.8% 31.0% 31.2% 31.3% 31.3%

Volatility 8.5% 7.4% 7.6% 8.1% 7.9% 72.4% 67.3% 67.2% 65.8% 66.3% 19.8% 19.2% 19.0% 18.7% 18.8%

Return 21.9% 19.7% 20.5% 19.8% 20.2% -35.4% -35.5% -34.5% -34.4% -34.2% 39.2% 36.7% 36.9% 37.0% 37.0%

Volatility 13.4% 11.6% 11.6% 11.9% 11.9% 36.9% 35.8% 35.7% 34.9% 35.2% 19.6% 19.1% 18.9% 18.7% 18.8%

Return 20.2% 18.1% 18.9% 18.2% 18.6% -48.6% -47.5% -46.5% -46.3% -46.2% 52.8% 49.1% 49.3% 49.4% 49.4%

Volatility 10.4% 9.0% 9.3% 9.2% 9.3% 50.3% 48.5% 48.4% 47.4% 47.8% 21.1% 19.9% 19.9% 19.7% 19.8%

Return 17.1% 14.8% 16.0% 15.8% 16.3% -64.8% -62.2% -61.2% -61.2% -61.0% 24.9% 23.9% 24.1% 24.2% 24.1%

Volatility 8.7% 7.7% 8.0% 7.9% 8.0% 27.5% 27.9% 27.9% 27.0% 27.3% 19.5% 18.6% 18.5% 18.3% 18.4%

Return 16.7% 14.4% 15.6% 15.5% 15.9% -42.5% -42.1% -41.1% -41.1% -40.9% 19.6% 19.2% 19.4% 19.5% 19.4%

Volatility 6.3% 6.1% 6.2% 6.2% 6.3% 25.7% 26.2% 26.1% 25.2% 25.5% 22.5% 21.4% 21.3% 21.2% 21.2%

Return 4.7% 3.5% 4.8% 4.6% 5.1% -23.7% -25.1% -24.1% -24.2% -24.0% 20.5% 19.8% 20.0% 20.2% 20.1%

Volatility 5.9% 5.9% 6.0% 5.8% 5.9% 17.7% 18.6% 18.6% 17.9% 18.1% 9.3% 9.8% 9.6% 9.2% 9.4%

Return 18.6% 16.6% 17.4% 16.7% 17.1% -53.0% -51.4% -50.3% -50.3% -50.1% 25.0% 24.2% 24.4% 24.5% 24.4%

Volatility 9.0% 8.1% 8.0% 7.9% 8.0% 32.9% 31.9% 31.7% 31.1% 31.3% 23.5% 21.5% 21.5% 21.3% 21.4%

Return 18.7% 16.7% 17.5% 16.8% 17.2% -57.8% -56.0% -55.0% -54.8% -54.7% 37.9% 35.7% 35.9% 36.0% 35.9%

Volatility 8.4% 7.2% 7.2% 7.0% 7.0% 41.0% 40.3% 40.3% 39.3% 39.6% 20.4% 19.1% 18.9% 18.9% 18.9%

Return 52.1% 46.9% 47.6% 47.0% 47.3% -21.1% -22.7% -21.7% -21.7% -21.6% 41.0% 38.4% 38.6% 38.7% 38.6%

Volatility 15.9% 14.9% 14.9% 14.5% 14.8% 38.5% 37.5% 37.4% 36.6% 36.9% 15.4% 14.9% 14.8% 14.7% 14.8%

Return 27.2% 24.4% 25.2% 24.5% 24.9% -33.8% -34.3% -33.3% -33.3% -33.1% 35.2% 33.1% 33.3% 33.4% 33.3%

Volatility 10.7% 9.1% 9.2% 9.4% 9.3% 27.3% 27.8% 27.7% 26.8% 27.1% 13.6% 13.8% 13.6% 13.4% 13.5%

Return 20.3% 17.7% 18.9% 18.7% 19.2% -36.2% -36.4% -35.4% -35.4% -35.3% 27.9% 26.6% 26.8% 26.9% 26.8%

Volatility 6.8% 6.3% 6.5% 6.3% 6.5% 28.4% 28.6% 28.5% 27.6% 27.9% 17.8% 17.3% 17.2% 17.0% 17.1%

Return 18.0% 15.6% 16.9% 16.7% 17.1% -37.6% -37.6% -36.6% -36.6% -36.4% 30.3% 28.7% 28.9% 29.0% 28.9%

Volatility 7.9% 7.2% 7.2% 7.4% 7.4% 22.1% 22.7% 22.6% 21.8% 22.1% 15.6% 15.6% 15.5% 15.2% 15.3%

Return 24.6% 21.4% 22.7% 22.5% 23.0% -47.7% -46.8% -45.8% -45.7% -45.6% 35.6% 33.5% 33.7% 33.8% 33.7%

Volatility 10.5% 10.5% 10.5% 10.3% 10.5% 39.5% 38.8% 38.8% 37.8% 38.1% 21.5% 20.8% 20.7% 20.4% 20.5%

Return 54.0% 48.5% 49.3% 48.7% 49.0% -22.3% -23.8% -22.8% -22.8% -22.6% 42.2% 39.5% 39.7% 39.7% 39.7%

Volatility 16.0% 14.9% 15.0% 14.6% 14.9% 39.9% 38.8% 38.7% 37.9% 38.2% 16.4% 15.8% 15.7% 15.6% 15.7%

Return 36.4% 32.7% 33.5% 32.8% 33.2% -27.4% -28.3% -27.3% -27.4% -27.2% 39.5% 37.0% 37.2% 37.3% 37.3%

Volatility 8.3% 6.4% 6.4% 6.9% 6.5% 24.1% 24.1% 24.0% 23.2% 23.5% 14.5% 14.4% 14.3% 14.1% 14.2%

Return 29.2% 26.1% 26.9% 26.2% 26.6% -52.7% -51.3% -50.2% -50.1% -50.0% 34.2% 32.3% 32.5% 32.6% 32.6%

Volatility 10.2% 9.6% 9.5% 9.3% 9.6% 33.2% 33.1% 32.8% 31.9% 32.3% 22.1% 20.8% 20.8% 20.6% 20.7%

Return 26.1% 22.9% 24.1% 24.0% 24.4% -37.8% -37.8% -36.8% -36.8% -36.6% 19.0% 18.6% 18.8% 18.9% 18.8%

Volatility 6.9% 6.3% 6.5% 6.3% 6.4% 27.6% 27.6% 27.6% 26.7% 27.0% 19.5% 18.6% 18.4% 18.4% 18.4%

Return 19.4% 19.6% 20.3% 18.6% 19.8% -98.1% -91.6% -90.4% -90.3% -90.2% 40.4% 38.0% 38.2% 38.3% 38.3%

Volatility 16.6% 15.4% 15.3% 14.8% 15.1% 51.5% 48.7% 48.3% 47.6% 47.9% 28.5% 26.8% 26.7% 26.4% 26.6%

Return 0.2% 0.0% 0.8% 0.2% 0.5% -51.3% -50.2% -49.1% -49.0% -48.9% 38.6% 36.3% 36.5% 36.6% 36.5%

Volatility 10.6% 10.0% 10.1% 9.3% 9.7% 37.4% 37.5% 37.3% 36.2% 36.6% 22.1% 21.1% 20.9% 20.8% 20.8%

Return 26.3% 23.0% 24.3% 24.1% 24.6% -34.6% -35.0% -34.0% -34.0% -33.8% 37.8% 35.4% 35.6% 35.7% 35.7%

Volatility 7.6% 6.9% 7.1% 7.0% 7.1% 29.5% 29.8% 29.7% 28.8% 29.1% 16.6% 16.5% 16.3% 16.1% 16.2%

Return 2.3% 1.9% 2.7% 2.0% 2.4% -17.1% -19.1% -18.1% -18.1% -18.0% 9.8% 10.5% 10.7% 10.9% 10.8%

Volatility 9.6% 9.3% 9.4% 8.8% 9.1% 39.7% 38.7% 38.6% 37.8% 38.1% 25.1% 22.9% 22.8% 22.5% 22.7%

Return 18.4% 16.5% 17.3% 16.6% 17.0% -26.7% -27.7% -26.7% -26.8% -26.6% 35.7% 33.6% 33.8% 33.9% 33.8%

Volatility 9.8% 8.5% 8.8% 8.7% 8.9% 29.6% 29.4% 29.4% 28.6% 28.9% 14.7% 14.6% 14.5% 14.3% 14.4%

Return 16.4% 14.1% 15.4% 15.2% 15.7% -40.0% -39.8% -38.8% -38.7% -38.6% 38.3% 36.0% 36.2% 36.3% 36.2%

Volatility 10.1% 9.8% 9.8% 9.5% 9.7% 31.1% 30.9% 30.9% 30.0% 30.3% 19.3% 18.4% 18.4% 18.2% 18.3%

Return 22.1% 19.2% 20.5% 20.3% 20.8% -30.9% -31.6% -30.6% -30.6% -30.4% 18.2% 17.9% 18.1% 18.2% 18.1%

Volatility 6.6% 5.8% 6.0% 6.2% 6.2% 26.8% 27.0% 26.9% 26.1% 26.4% 24.1% 22.8% 22.7% 22.6% 22.6%

Return 21.8% 19.0% 20.3% 20.1% 20.5% -42.4% -42.0% -41.0% -41.0% -40.8% 39.5% 37.0% 37.2% 37.4% 37.3%

Volatility 9.8% 8.9% 9.1% 9.0% 9.1% 28.6% 28.8% 28.7% 27.8% 28.1% 19.3% 18.7% 18.5% 18.3% 18.4%

Return 14.7% 12.6% 13.9% 13.7% 14.1% -24.9% -26.2% -25.2% -25.3% -25.1% 27.8% 26.5% 26.7% 26.8% 26.7%

Volatility 6.8% 6.6% 6.7% 6.6% 6.8% 18.0% 19.0% 19.0% 18.2% 18.5% 13.5% 13.4% 13.2% 13.0% 13.1%

Return 15.3% 13.6% 14.4% 13.7% 14.1% -27.9% -28.9% -27.9% -27.9% -27.7% 35.3% 33.3% 33.5% 33.6% 33.5%

Volatility 8.2% 7.7% 7.7% 7.1% 7.3% 25.0% 25.3% 25.2% 24.5% 24.8% 17.8% 17.3% 17.2% 17.1% 17.1%

Return 13.7% 12.1% 12.9% 12.2% 12.6% -26.2% -27.4% -26.4% -26.4% -26.2% 43.9% 41.1% 41.3% 41.3% 41.3%

Volatility 12.2% 11.9% 11.7% 11.6% 11.8% 33.8% 33.6% 33.5% 32.7% 33.0% 13.9% 13.5% 13.4% 13.3% 13.3%

Return 36.0% 32.3% 33.1% 32.4% 32.8% -41.0% -40.5% -39.4% -39.3% -39.2% 42.6% 40.0% 40.2% 40.3% 40.2%

Volatility 6.6% 5.5% 5.5% 5.9% 5.8% 42.9% 41.2% 41.2% 40.3% 40.6% 22.5% 21.1% 21.1% 20.9% 21.0%

Return 16.8% 14.5% 15.8% 15.6% 16.0% -34.9% -35.3% -34.3% -34.3% -34.1% 27.9% 26.5% 26.7% 26.8% 26.8%

Volatility 5.4% 5.6% 5.7% 5.5% 5.7% 20.8% 22.1% 22.0% 21.1% 21.4% 14.9% 14.9% 14.7% 14.5% 14.6%

Return 10.3% 8.6% 9.9% 9.7% 10.1% -23.3% -24.7% -23.7% -23.8% -23.6% 22.7% 21.9% 22.1% 22.2% 22.1%

Volatility 4.4% 4.4% 4.5% 4.5% 4.6% 15.2% 16.7% 16.5% 15.6% 16.0% 11.6% 11.8% 11.7% 11.5% 11.6%

Turkey

United Kingdom

United States

Sweden

Switzerland

Taiwan

Thailand

Slovakia

South Africa

South Korea

Spain

Portugal

Romania

Russia

Singapore

Norway

Peru

Philippines

Poland

Malaysia

Mexico

Netherlands

New Zealand

Italy

Japan

Latvia

Lithuania

Iceland

India

Indonesia

Ireland

Germany

Greece

Hong Kong

Hungary

Egypt

Estonia

Finland

France

China

Colombia

Czech Republic

Denmark

Brazil

Bulgaria

Canada

Chile

Argentina

Australia

Austria

Belgium

Post-Crisis (Mar-09 through Dec-10)

Portfolio with 10% allocation to:Portfolio with 10% allocation to:

Pre-Crisis (Jan-06 through Jun-07) Crisis (Jul-07 through Feb-09)

Portfolio with 10% allocation to:


10/89

10





January 23, 2011

Exhibit 17: Impact of adding commodity allocations to country benchmark portfolios, local currency

Source: S&P, Dow Jones, J.P. Morgan Commodities Research. Note: return is the average annualized return based on monthly observations. Volatility is the annualized standard deviation of the monthly returns.

Original Original Original

Country 60/40 S&P GSCI S&P GSCI En DJ-UBS JPMCCI 60/40 S&P GSCI S&P GSCI En DJ-UBS JPMCCI 60/40 S&P GSCI S&P GSCI En DJ-UBS JPMCCI

Return 2.9% 2.5% 3.3% 2.6% 3.0% -70.7% -67.2% -66.2% -66.0% -65.9% 64.2% 59.4% 59.6% 59.7% 59.7%

Volatility 10.1% 8.2% 8.2% 8.6% 8.3% 52.1% 49.7% 49.7% 48.6% 49.0% 23.5% 22.5% 22.3% 22.0% 22.1%

Return 16.6% 14.3% 15.5% 15.3% 15.8% -22.2% -23.7% -22.7% -22.8% -22.6% 22.0% 21.2% 21.4% 21.5% 21.5%

Volatility 5.2% 5.0% 5.1% 5.0% 5.1% 16.5% 17.7% 17.6% 16.7% 17.0% 10.5% 10.6% 10.5% 10.4% 10.4%

Return 13.7% 11.7% 13.0% 12.8% 13.2% -55.0% -53.4% -52.4% -52.3% -52.2% 33.3% 31.5% 31.7% 31.8% 31.7%

Volatility 8.2% 7.2% 7.4% 7.3% 7.4% 31.6% 31.7% 31.6% 30.6% 31.0% 16.1% 15.6% 15.5% 15.3% 15.4%

Return 16.1% 13.9% 15.1% 14.9% 15.4% -50.6% -49.3% -48.3% -48.2% -48.1% 26.8% 25.6% 25.8% 25.9% 25.8%

Volatility 6.4% 5.3% 5.6% 5.5% 5.6% 30.3% 30.0% 30.0% 29.1% 29.4% 11.6% 11.2% 11.1% 11.1% 11.1%

Return 15.6% 16.1% 16.9% 15.1% 16.3% -59.2% -57.3% -56.2% -56.1% -56.0% 15.7% 15.7% 15.9% 16.0% 16.0%

Volatility 8.7% 8.3% 8.2% 8.1% 8.1% 36.8% 36.6% 36.5% 35.5% 35.8% 15.8% 14.6% 14.6% 14.5% 14.6%

Return 11.7% 9.8% 11.1% 10.9% 11.4% -17.1% -19.2% -18.2% -18.3% -18.1% 21.9% 21.1% 21.3% 21.4% 21.3%

Volatility 6.2% 6.7% 6.7% 6.5% 6.7% 15.5% 17.5% 17.4% 16.6% 16.9% 9.6% 10.1% 10.0% 9.8% 9.9%

Return 30.7% 27.5% 28.3% 27.6% 28.0% -10.5% -13.1% -12.1% -12.2% -12.0% 28.3% 27.0% 27.2% 27.2% 27.2%

Volatility 6.8% 5.7% 5.7% 5.8% 5.8% 10.6% 12.3% 12.2% 11.3% 11.7% 9.4% 9.5% 9.3% 9.3% 9.2%

Return 6.0% 5.3% 6.1% 5.5% 5.8% -7.9% -10.7% -9.8% -9.8% -9.6% 33.3% 31.5% 31.7% 31.8% 31.7%

Volatility 12.6% 11.0% 11.1% 10.7% 10.8% 21.1% 21.3% 21.3% 20.5% 20.8% 11.5% 11.1% 11.0% 11.0% 11.0%

Return 14.2% 12.1% 13.4% 13.2% 13.7% -26.9% -28.0% -27.0% -27.0% -26.9% 23.8% 22.9% 23.1% 23.2% 23.1%

Volatility 10.5% 9.7% 9.8% 9.6% 9.7% 25.0% 25.7% 25.6% 24.7% 25.0% 12.9% 12.3% 12.3% 12.1% 12.2%

Return 16.1% 13.9% 15.1% 14.9% 15.4% -30.9% -31.6% -30.6% -30.6% -30.4% 29.7% 28.3% 28.4% 28.5% 28.5%

Volatility 7.5% 6.4% 6.5% 6.7% 6.7% 22.4% 23.2% 23.1% 22.1% 22.5% 11.8% 11.5% 11.4% 11.2% 11.3%

Return 17.9% 16.0% 16.7% 16.1% 16.4% -29.9% -30.8% -29.8% -29.8% -29.6% 26.8% 25.7% 25.9% 26.0% 25.9%

Volatility 9.5% 8.7% 9.0% 8.7% 8.8% 27.8% 28.5% 28.4% 27.5% 27.8% 16.8% 15.9% 15.8% 15.5% 15.7%

Return 25.3% 22.9% 23.7% 23.0% 23.4% -63.8% -61.2% -60.2% -60.2% -60.0% 37.3% 35.3% 35.5% 35.6% 35.5%

Volatility 21.8% 18.8% 18.7% 19.0% 18.8% 32.8% 32.7% 32.7% 31.8% 32.1% 23.8% 21.7% 21.7% 21.6% 21.7%

Return 17.0% 14.7% 15.9% 15.8% 16.2% -36.8% -36.8% -35.8% -35.8% -35.6% 27.9% 26.6% 26.8% 26.9% 26.9%

Volatility 8.8% 7.9% 8.0% 7.9% 8.0% 21.2% 21.4% 21.4% 20.7% 20.9% 16.4% 15.3% 15.3% 15.2% 15.2%

Return 13.8% 11.8% 13.1% 12.9% 13.3% -30.9% -31.5% -30.5% -30.6% -30.4% 23.3% 22.4% 22.6% 22.7% 22.6%

Volatility 5.5% 4.9% 5.1% 4.9% 5.0% 17.9% 18.7% 18.6% 17.7% 18.0% 13.1% 12.7% 12.6% 12.6% 12.6%

Return 18.0% 15.6% 16.9% 16.7% 17.1% -33.4% -33.8% -32.8% -32.8% -32.6% 28.2% 26.9% 27.1% 27.2% 27.1%

Volatility 6.8% 5.9% 6.0% 6.0% 6.0% 20.4% 21.1% 21.1% 20.2% 20.5% 12.5% 12.1% 12.0% 12.0% 12.0%

Return 15.5% 13.4% 14.6% 14.4% 14.9% -51.3% -50.0% -49.0% -48.9% -48.8% 2.6% 3.9% 4.1% 4.2% 4.2%

Volatility 9.9% 8.4% 8.6% 8.7% 8.6% 28.8% 29.0% 28.9% 27.8% 28.2% 24.6% 23.2% 23.1% 22.9% 22.9%

Return 18.4% 15.9% 17.2% 17.0% 17.5% -23.4% -24.8% -23.8% -23.8% -23.7% 32.6% 30.7% 31.0% 31.1% 31.0%

Volatility 5.7% 5.4% 5.5% 5.4% 5.5% 25.9% 26.2% 26.2% 25.4% 25.6% 15.0% 15.0% 14.9% 14.6% 14.7%

Return 27.1% 24.3% 25.1% 24.5% 24.8% -51.0% -49.8% -48.8% -48.7% -48.6% 32.5% 30.8% 31.0% 31.1% 31.0%

Volatility 10.3% 9.0% 9.2% 8.9% 9.0% 30.7% 30.8% 30.7% 29.7% 30.0% 18.9% 18.0% 17.9% 17.6% 17.8%

Return 28.5% 27.8% 28.5% 26.7% 27.9% -86.7% -81.1% -80.0% -79.6% -79.6% 32.6% 30.8% 31.0% 31.1% 31.1%

Volatility 4.1% 3.7% 3.9% 4.2% 4.1% 67.9% 63.3% 63.1% 61.7% 62.2% 16.1% 15.9% 15.6% 15.3% 15.4%

Return 12.7% 11.4% 12.2% 11.5% 11.8% -25.6% -26.7% -25.7% -25.7% -25.5% 35.5% 33.4% 33.6% 33.7% 33.6%

Volatility 10.5% 8.9% 8.9% 9.2% 9.1% 32.6% 31.9% 31.8% 31.0% 31.2% 16.7% 16.2% 16.1% 16.0% 16.0%

Return 18.9% 16.9% 17.7% 17.0% 17.4% -33.8% -34.3% -33.3% -33.3% -33.1% 44.6% 41.6% 41.8% 41.9% 41.9%

Volatility 8.2% 7.3% 7.5% 7.2% 7.5% 38.6% 38.0% 38.0% 37.0% 37.4% 17.5% 16.7% 16.6% 16.5% 16.5%

Return 11.7% 10.0% 11.2% 11.0% 11.5% -63.5% -60.9% -59.8% -59.9% -59.7% 23.8% 23.0% 23.2% 23.3% 23.2%

Volatility 8.7% 7.2% 7.5% 7.6% 7.5% 21.8% 22.5% 22.4% 21.6% 21.9% 14.9% 14.0% 13.9% 13.8% 13.9%

Return 11.4% 9.6% 10.9% 10.7% 11.2% -42.2% -41.7% -40.7% -40.7% -40.6% 19.0% 18.6% 18.8% 18.9% 18.8%

Volatility 5.5% 4.6% 4.8% 4.9% 4.9% 19.7% 20.5% 20.4% 19.5% 19.8% 16.5% 15.7% 15.6% 15.6% 15.5%

Return 6.4% 5.1% 6.4% 6.2% 6.7% -32.8 % - 33.3% -32.3% -32.3% -32.2% 14.2% 14.2% 14.4% 14.5% 14.5%

Volatility 7.1% 6.6% 6.6% 6.4% 6.5% 20.5% 21.5% 21.4% 20.6% 20.9% 11.7% 11.8% 11.7% 11.2% 11.4%

Return 16.5% 14.7% 15.6% 14.9% 15.2% -48.8% -47.5% -46.5% -46.5% -46.3% 23.8% 23.2% 23.4% 23.5% 23.4%

Volatility 8.2% 7.3% 7.0% 7.1% 7.0% 28.5% 27.6% 27.4% 26.8% 27.1% 20.6% 18.3% 18.4% 18.2% 18.3%

Return 16.7% 15.0% 15.8% 15.1% 15.5% -53.4% -51.9% -50.9% -50.8% -50.7% 36.7% 34.7% 34.9% 35.0% 34.9%

Volatility 9.3% 7.8% 7.7% 7.7% 7.6% 34.0% 34.0% 33.9% 33.0% 33.3% 17.1% 15.5% 15.4% 15.4% 15.4%

Return 27.4% 24.5% 25.3% 24.6% 25.0% -13.5% -15.8% -14.9% -15.0% -14.8% 24.2% 23.3% 23.5% 23.6% 23.5%

Volatility 5.6% 5.3% 5.2% 5.4% 5.5% 17.1% 18.2% 18.2% 17.6% 17.8% 8.2% 8.3% 8.3% 8.0% 8.1%

Return 25.5% 22.8% 23.6% 22.9% 23.3% -20.0% -21.7% -20.7% -20.8% -20.6% 29.2% 27.7% 27.9% 28.0% 27.9%

Volatility 8.5% 7.1% 7.1% 7.4% 7.3% 19.7% 20.4% 20.4% 19.6% 19.9% 10.2% 10.4% 10.3% 10.2% 10.2%

Return 16.0% 13.8% 15.1% 14.9% 15.3% -30.7% -31.3% -30.4% -30.4% -30.2% 31.2% 29.6% 29.8% 29.9% 29.8%

Volatility 9.4% 7.8% 8.0% 8.1% 8.1% 21.3% 21.6% 21.6% 20.8% 21.1% 12.9% 12.6% 12.6% 12.3% 12.4%

Return 13.1% 11.2% 12.5% 12.3% 12.7% -20.2% -21.8% -20.8% -20.9% -20.7% 16.9% 16.7% 16.9% 17.0% 16.9%

Volatility 6.8% 6.1% 6.1% 6.3% 6.3% 13.6% 14.2% 14.2% 13.5% 13.7% 7.8% 8.2% 8.1% 7.9% 8.0%

Return 19.4% 16.8% 18.1% 17.9% 18.4% -38.2% -38.3% -37.3% -37.2% -37.1% 30.7% 29.1% 29.3% 29.4% 29.3%

Volatility 8.5% 8.1% 8.1% 8.0% 8.1% 29.8% 29.9% 29.9% 29.0% 29.3% 14.2% 14.2% 14.1% 13.8% 13.9%

Return 52.3% 47.0% 47.8% 47.2% 47.5% -20.2% -22.0% -21.0% -21.0% -20.8% 37.7% 35.4% 35.6% 35.7% 35.6%

Volatility 15.7% 14.7% 14.8% 14.3% 14.7% 36.3% 35.6% 35.6% 34.8% 35.1% 15.4% 15.0% 14.9% 14.8% 14.8%

Return 29.3% 26.3% 27.1% 26.4% 26.8% -24.8% -26.0% -25.0% -25.0% -24.9% 36.4% 34.2% 34.4% 34.5% 34.4%

Volatility 7.7% 6.0% 5.9% 6.4% 6.0% 21.1% 21.3% 21.3% 20.4% 20.7% 12.4% 12.3% 12.3% 12.1% 12.2%

Return 25.8% 23.2% 24.0% 23.3% 23.6% -39.4% -39.2% -38.2% -38.2% -38.0% 28.7% 27.4% 27.5% 27.6% 27.6%

Volatility 9.8% 8.9% 8.6% 8.8% 8.8% 21.3% 21.9% 21.8% 20.9% 21.2% 12.9% 12.2% 12.2% 12.1% 12.2%

Return 20.7% 18.1% 19.4% 19.2% 19.6% -33.9% -34.2% -33.2% -33.2% -33.0% 18.1% 17.8% 18.0% 18.1% 18.1%

Volatility 7.9% 6.6% 6.7% 6.7% 6.7% 21.7% 21.8% 21.8% 20.9% 21.2% 13.6% 12.9% 12.8% 12.9% 12.8%

Return 2.0% 3.9% 4.7% 2.9% 4.1% -79.2% -74.8% -73.7% -73.7% -73.5% 39.9% 37.5% 37.7% 37.8% 37.8%

Volatility 13.5% 12.7% 12.6% 12.1% 12.3% 40.9% 39.3% 39.0% 38.3% 38.6% 21.1% 20.0% 20.0% 19.7% 19.8%

Return -1.6% -1.6% -0.8% -1.5% -1.1% -37. 8% - 38 .0% -36.9% -36.8% -36.7% 34.3% 32.4% 32.6% 32.7% 32.6%

Volatility 9.9% 9.2% 9.3% 8.6% 8.9% 33.6% 33.7% 33.7% 32.6% 33.0% 17.7% 16.9% 16.8% 16.7% 16.7%

Return 23.0% 20.1% 21.4% 21.2% 21.7% -33.4% -33.9% -32.9% -32.9% -32.8% 32.2% 30.4% 30.6% 30.7% 30.6%

Volatility 7.2% 6.4% 6.5% 6.5% 6.5% 26.2% 26.7% 26.7% 25.7% 26.1% 15.0% 14.9% 14.8% 14.6% 14.6%

Return -5.3% -5.0% -4.2% -4.9% -4.5% -16.0% -18.1% -17.1% -17.2% -17.0% 8.3% 9.3% 9.5% 9.6% 9.5%Volatility 4.4% 4.7% 4.7% 4.3% 4.5% 30.2% 29.9% 29.9% 29.1% 29.4% 23.7% 21.2% 21.1% 20.9% 21.0%

Return 16.8% 15.0% 15.8% 15.1% 15.5% -11.1% -13.7% -12.7% -12.8% -12.6% 23.0% 22.1% 22.3% 22.4% 22.4%

Volatility 8.0% 6.8% 7.1% 7.2% 7.2% 17.3% 18.2% 18.2% 17.6% 17.8% 9.2% 9.8% 9.6% 9.4% 9.5%

Return 13.0% 11.0% 12.3% 12.1% 12.6% -19.2% -21.1% -20.1% -20.1% -19.9% 29.7% 28.2% 28.4% 28.5% 28.5%

Volatility 8.6% 8.3% 8.4% 8.1% 8.3% 23.0% 23.6% 23.6% 22.6% 22.9% 12.9% 12.4% 12.4% 12.3% 12.3%

Return 16.7% 14.4% 15.7% 15.5% 15.9% -34.2% -34.5% -33.4% -33.5% -33.3% 17.7% 17.4% 17.6% 17.7% 17.7%

Volatility 7.6% 6.1% 6.3% 6.7% 6.5% 21.7% 22.1% 21.9% 21.2% 21.4% 17.3% 16.5% 16.5% 16.4% 16.4%

Return 16.0% 13.8% 15.1% 14.9% 15.3% -30.7% -31.3% -30.4% -30.4% -30.2% 31.2% 29.6% 29.8% 29.9% 29.8%

Volatility 9.4% 7.8% 8.0% 8.1% 8.1% 21.3% 21.6% 21.6% 20.8% 21.1% 12.9% 12.6% 12.6% 12.3% 12.4%

Return 11.8% 10.0% 11.3% 11.1% 11.6% -26.4% -27.4% -26.4% -26.5% -26.3% 21.3% 20.6% 20.8% 20.9% 20.8%

Volatility 6.9% 5.9% 6.0% 6.1% 6.2% 14.5% 15.7% 15.6% 14.7% 15.0% 9.9% 9.9% 9.8% 9.7% 9.7%

Return 16.3% 14.1% 15.3% 15.1% 15.6% -25.3% -26.5% -25.5% -25.5% -25.3% 29.9% 28.4% 28.6% 28.7% 28.6%

Volatility 8.1% 7.6% 7.6% 7.5% 7.5% 23.2% 23.5% 23.4% 22.7% 22.9% 15.7% 15.4% 15.3% 15.2% 15.2%

Return 9.8% 8.6% 9.4% 8.8% 9.1% -23.7% -25.2% -24.2% -24.2% -24.0% 38.3% 36.0% 36.2% 36.3% 36.2%

Volatility 11.3% 11.2% 11.1% 10.8% 11.1% 30.9% 31.0% 31.0% 30.1% 30.4% 12.7% 12.4% 12.2% 12.1% 12.2%

Return 25.4% 22.7% 23.5% 22.8% 23.2% -27.1% -28.0% -27.0% -27.0% -26.8% 40.5% 38.0% 38.2% 38.3% 38.2%

Volatility 5.0% 4.1% 4.1% 4.6% 4.5% 30.2% 29.4% 29.3% 28.6% 28.8% 16.9% 16.0% 16.0% 15.8% 15.9%

Return 10.7% 9.0% 10.3% 10.1% 10.5% -21.8% -23.3% -22.3% -22.4% -22.2% 25.3% 24.2% 24.4% 24.5% 24.5%

Volatility 5.0% 4.6% 4.8% 4.6% 4.7% 17.5% 18.4% 18.4% 17.6% 17.9% 11.7% 11.6% 11.5% 11.4% 11.4%

Return 10.3% 8.6% 9.9% 9.7% 10.1% -23.3% -24.7% -23.7% -23.8% -23.6% 22.7% 21.9% 22.1% 22.2% 22.1%

Volatility 4.4% 4.4% 4.5% 4.5% 4.6% 15.2% 16.7% 16.5% 15.6% 16.0% 11.6% 11.8% 11.7% 11.5% 11.6%United States

Taiwan

Thailand

Turkey

United Kingdom

South Korea

Spain

Sweden

Switzerland

Russia

Singapore

Slovakia

South Africa

Philippines

Poland

Portugal

Romania

Netherlands

New Zealand

Norway

Peru

Latvia

Lithuania

Malaysia

Mexico

Indonesia

Ireland

Italy

Japan

Hong Kong

Hungary

Iceland

India

Finland

France

Germany

Greece

Czech Republic

Denmark

Egypt

Estonia

Bulgaria

Canada

Chile

Colombia

Argentina

Australia

Austria

Belgium

Post-Crisis ( Mar-09 through Dec-10)

Portfolio with 10% allocation to:Portfolio with 10% allocation to:

Pre-Crisi s ( Jan-06 t hrou gh Ju n-07) Crisis (Jul-0 7 t hrou gh Fe b-09)

Portfolio with 10% allocation to:


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11





January 23, 2011

Commodity correlations with equities have

finally started breaking downThanks to the extraordinary stimulus provided by monetary

and fiscal authorities over the past three years, correlations of

changes in asset prices are at an exceptionally high level

(Exhibits 18-25). We calculate that the rolling quarterly

correlations between the S&P GSCI spot price index and the

MSCI AC World price index and the S&P 500, respectively, are

0.65 and 0.68. These figures are down from recent highs above

0.70levels never before seen since the S&P GSCI was created

twenty years ago.

However, this unsustainable sympathy finally appears to be

deteriorating, which ultimately is good for investors. A falling

correlation is good because it implies that the recovery is findingfirmer footing and greater differentiation is appearing between

the time horizons priced by asset classes.

The shift in correlations is already more advanced in Europe,

where the correlation of S&P GSCI and the MSCI European

Union price index is now at 0.44, down from above 0.75 in recent

months. In Eastern Europe, the correlation is at 0.55 and falling.

Emerging markets, specifically Latin America and Asia, appear

to be following. The MSCI Emerging Markets Index is at 0.59

against the S&P GSCI and the MSCI Latin America Index is at

0.60.

An interesting result that turned up in this analysis is both

China (a major commodity consumer) and Australia (a major

commodity producer) show comparably weaker, but still very

positive, levels of correlation with the S&P GSCI, at 0.46 and

0.43 respectively. Two main factors are driving this result: (1)

China managed the recession better than most, and pulled

Australia along with it, (2) Australia's shipments to China include

a heavy emphasis on iron ore and coking coal, two commodities

lacking liquid futures markets and thus not found in traded

commodity indices. But even in these countries' charts, the

general pattern of a sharp increase in correlation during the

recession (to previously unobserved levels since 1995), followed

by a nascent rollover, is still evident. This suggests that evenwith its enormous endowment of natural resources, Australian

portfolios would do well to add commodities into their asset

mix. Similarly, the data show that the negative correlation of

MSCI China price changes with the S&P GSCI can be 0.25 or

lower for long periods of time, making commodities a useful

portfolio hedge when shares are not performing as well as they

recently have been.

The cumulative benefits of commodity allocations fromJanuary 2006 through December 2010 have gone into negative

territory in a number of countries after the strong rally in

equities. However, even now the cumulative five-year cost

of this insurance might be -200bp to -400bp in a given

countrywhich is a cost of only 40bp to 50bp per year.

Moreover, now is a good time to enter commodity positions,

as the cumulative curves are flattening.

Commodities provided significant benefits in 2008as muchas 100bp or more in returns per month in several countries

when portfolios needed the most protection. Among the

country portfolios that benefited during this time frame wereAustralia (page 38), Canada (page 43), China (page 42), the

Netherlands (page 66), Switzerland (page 81), and the US

(page 86).

Cumulatively over the past five years, the country portfolioswith an allocation to the JPMCCI total return index fared the

best relative to the other commodity indices. However, we

note that in the contango-dominated markets of recent years,

the S&P GSCI total return index has struggledbut

backwardation is likely to become once again a dominant

factor in commodity returns later this cycle.

Allocations to commodities over the past five years alsolowered portfolio volatility. On average, approximately 73%

of local country portfolios had lower volatility while 88% of

country portfolios in US dollars exhibited lower vol. For

these country portfolios, the average benefit was 60bp in the

local currency portfolios and 90bp in the US dollar

denominated portfolios.

Table 2: Impact of commodity allocations on country portfolios, local

Quantifying monthly portfolio benefits from Jan-06 through Oct-08, percent

Source: S&P, Dow Jones, J.P. Morgan Commodities Research. Note: Results are monthly median

benefits not cumulative over the interval.

S&P GSCI total

return index

S&P GSCI

enhanced

return index

DJ-UBS total

return index

JPMCCI total

return index

% COUNTRIES HELPED 57% 96% 63% 90%

QUANTIFYING THE BENEFIT, IF ONE EXISTED

MEDIAN 2.1% 2.4% 1.5% 2.3%MAX 13.8% 14.7% 13.4% 14.6%MIN 0.1% 0.3% 0.0% 0.2%

QUANTIFYING THE COST, IF ONE EXISTED

MEDIAN -1.0% -0.4% -0.5% -0.2%MAX -0.2% -0.2% -0.2% -0.1%MIN -2.4% -0.5% -2.1% -1.1%


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January 23, 2011

-1.00

-0.50

0.00

0.50

1.00

95 96 97 98 99 00 01 02 03 04 05 0 6 07 08 09 10 11 12 13 14

?

21-Jan-11

0.44

Heart of the financial

crisis in gray

-1.00

-0.50

0.00

0.50

1.00

95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14

?

21-Jan-11

0.55


crisis in gray

-1.00

-0.50

0.00

0.50

1.00

95 96 97 98 99 00 01 02 03 04 05 0 6 07 08 09 10 11 12 13 14

?

21-Jan-11

0.68


crisis in gray

-1.00

-0.50

0.00

0.50

1.00

95 96 97 98 99 00 01 02 03 04 05 0 6 07 08 09 10 11 12 13 14

?


crisis in gray

21-Jan-11

0.65

Exhibit 18: Rolling quarterly correlation between S&P GSCI and MSCI

AC World price returns

Source: S&P, MSCI, J.P. Morgan Commodities Research

Extreme correlations likely to revert toward the mean


European Union price returns


Exhibit 19: Rolling quarterly correlation between S&P GSCI and S&P

500 price returns

Source: S&P, J.P. Morgan Commodities Research


Emerging Europe price returns


-1.00

-0.50

0.00

0.50

1.00

95 96 97 98 99 00 01 02 03 04 05 0 6 07 08 09 10 11 12 13 14

?

21-Jan-11

0.46


crisis in gray

-1.00

-0.50

0.00

0.50

1.00

95 96 97 98 99 00 01 02 03 04 05 0 6 07 08 09 10 11 12 13 14

?

21-Jan-11

0.43


crisis in gray

-1.00

-0.50

0.00

0.50

1.00

95 96 97 98 99 00 01 02 03 04 05 0 6 07 08 09 10 11 12 13 14

?

21-Jan-11

0.60

Heart of the financialcrisis in gray

-1.00

-0.50

0.00

0.50

1.00

95 96 97 98 99 00 01 02 03 04 05 0 6 07 08 09 10 11 12 13 14

?

21-Jan-110.59

Heart of the financialcrisis in gray


Emerging Markets price returns



China price returns



Latin America price returns



Australia price returns



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January 23, 2011

Imputed volatility distributions since 15-

Dec-09 likely understate actual riskPrompt implied volatilities in most commodities are low relativeto their long-run averages. Our sense has been that the very

depressed level of asset price volatility generally has not only

reduced expectations about the value of the mean for implied

volatility in most commodities, but more critically has also

demolished expectations about the dispersion of potential

outcomes around the new mean. In plain English, we surmise:

(a) because zero interest rate policy and quantitative easing

have helped significantly depress realized and implied vols for

two years, consensus has come to expect a lower mean vol in

most commodities, (b) observed implied vols are lower than

even those reduced expected means, and (c) tail risk is once

again vastly underestimated because of (a) and (b). Thevolatility is the message.

To test our hypothesis, we compiled histograms of historical

prompt implied volatility for 24 commodity markets, spanning

energy, agriculture, and metals. Most of these data sets go

back nearly 18 years, incorporating a range of idiosyncratic

events and market regimes. We then calculated the imputed

normal distribution of prompt implied vol for the same 24 markets

using only the period from 15-Dec-09 through today and

superimposed those narrow-interval distributions over the more-

meaty histograms (Exhibits 26-49). We selected this starting

point because it marks the one-year anniversary of ZIRP and

roughly the nine-month mark of the equity rally. In the charts,

the red vertical lines indicate current values.

What immediately becomes apparent in running this exercise is

that if a commodity risk analysis is giving substantial weight to

the unusual market environment of the ZIRP world, it is likely

grossly underestimating the range of potential outcomes

(especially the right tail of risk) that will increasingly become

more apparent when monetary supports are removed, a US rate

hike cycle begins, and the macro environment inevitably moves

back to some form of normalcy. Given the current low levels of

industrial commodity vol relative to even the recent distribution,

let alone the more realistic portrait of risk revealed in thehistograms, the transformation in consensus opinion over the

next 12-to-24 months could be bruising.

The potential underestimation of volatility risk looks to be most

severe in petroleum markets. Implied vols in truly risky oil

markets routinely surpass 50% on an annualized basis: the

imputed distribution we calculate based on observed prices in

the recent market suggests a nearly zero percent probability for

this outcome. There is no way this is an accurate statement of

the condition of risk in either a high-inventory or low-inventory

regime. Charitably, it could be said that current petroleum stocks

are normal relative to demand and this helps account for the

observed volatility level. But even if this is true, the globalpetroleum balance has moved decidedly into deficit and those

stocks are drawing. Moreover, the mere existence of crude

stocks somewhere on the planet does not guarantee that they

are suitable for a particular refinery configuration or that they

are available for sale anywhere near current prices. The truly

available inventory for refinery acquisition is not the same thing

as a count of the world's crude stores, an important economic

fact in a rising interest rate environment. We amplify on this

concept of friction in Commodity Markets Outlook and

Strategy: Freedom and Friction (Nov 8, 2010).

The implied vol curves in softs have shifted sharply to the

right, with both imputed means and current values coming inwell above the long-run normal on severe, weather-driven supply

shocks. Volatility in precious metals has been guided higher by

fears about escalating sovereign debt, doubts about the value

of fiat currencies, and rising inflation risks. Both softs and

precious metals have broadly entered corrections, as we

forecasted two weeks ago, and implied vols are coming down

as flat prices become more directional to the downside. Notably,

implied vols in energy are still very light, and we see this as the

next big factor to change in the commodity vol theme. Spot

Brent and WTI crude oil prices are trading close to our 1Q2011

forecasts on both an average price and settlement price basis.

We expect the strong upward momentum in oil prices of the

past two months now to moderate as the heart of the NorthernHemisphere winter passes (reducing demand for middle distillate

heating fuels) and refinery maintenance begins (reducing the

call on crude throughput). The Brent-WTI spread, in particular,

is presently wide and looks vulnerable to retracement. However,

we expect the pause in energy momentum to be brief, with any

soft patch in pricing (perhaps on a surprise OPEC production

announcement) quickly followed by a resurgent market tone

this summer, as global economic activity continues to gather

pace. This path will likely be a catalyst for rising implied volatility

priced into options.

Timing is difficult. Attempts to capture a risk reversal through

owning straddles, for example, face being early and chopped

up on time decay. Tactical traders should be cautious about the

potential for the vol story to be gradual in its evolution. However,

whether vol increases quickly or slowly, index investors and

other stewards of patient capital are being offered excellent

opportunities to initiate or extend exposures at attractive levels

in price terms and very low levels in vol terms. This suggests

the risk in implementing a strategic program in the next couple

of months is unusually favorable to the buyer. We view the

window as the best entry opportunity since August 2010.


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January 23, 2011

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0 10 20 30 40 50 60 70 80 90 100 110 120130 140 150

0.00

0.02

0.04

0.06

0.08

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0.12

0.14

27.13

21-Jan-11

0.00

0.02

0.04

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0.08

0.10

0 10 20 30 40 50 60 70 80 90 100 110 120130 140 150

0.00

0.02

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0.10

19.64

21-Jan-11

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 10 20 30 40 50 60 70 80 90 100 110 120130 140 150

0.00

0.02

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0.06

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0.12

22.75

21-Jan-11

0.00

0.02

0.04

0.06

0.08

0.10

0 10 20 30 40 50 60 70 80 90 100 110 120130 140 150

0.00

0.02

0.04

0.06

0.08

0.10

24.15

21-Jan-11

0.00

0.02

0.04

0.06

0.08

0.10

0 10 20 30 40 50 60 70 80 90 100 110 120130 140 150

0.00

0.02

0.04

0.06

0.08

0.10

38.76

21-Jan-11

0.00

0.02

0.04

0.06

0.08

0.10

0 10 20 30 40 50 60 70 80 90 100 110 120130 140 150

0.00

0.02

0.04

0.06

0.08

0.10

21.2521-Jan-11

0.00

0.02

0.04

0.06

0.08

0.10

0 10 20 30 40 50 60 70 80 90 100 110 120130 140 150

0.00

0.02

0.04

0.06

0.08

0.10

21.8221-Jan-11

0.00

0.02

0.04

0.06

0.08

0.10

0 10 20 30 40 50 60 70 80 90 100 110 120130 140 150

0.00

0.02

0.04

0.06

0.08

0.10

24.06

21-Jan-11

Exhibit 26: Prompt NYM WTI crude oil

Freq. by implied vol (%). Histogram since 7/15/93. Line is recent distribution.

Source: Exchanges, J.P. Morgan Commodities Research

Imputed volatility distributions since 15-Dec-09 likely underestimate risk

Exhibit 28: Prompt NYM heating oil



Exhibit 30: Prompt ICE gas oil



Exhibit 32: Prompt CMX copper



Exhibit 27: Prompt ICE Brent crude oil



Exhibit 29: Prompt NYM RBOB gasoline



Exhibit 31: Prompt NYM natural gas



Exhibit 33: LME 3 Month copper




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January 23, 2011

0.00

0.02

0.04

0.06

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0.14

0 10 20 30 40 50 60 70 80 90 100 110 120

0.00

0.02

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0.14

23.94

21-Jan-11

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 10 20 30 40 50 60 70 80 90 100 110 120

0.00

0.02

0.04

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0.12

26.1

21-Jan-11

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 10 20 30 40 50 60 70 80 90 100 110 120

0.00

0.02

0.04

0.06

0.08

0.10

0.12

26.64

21-Jan-11

0.00

0.02

0.04

0.06

0.08

0.10

0 10 20 30 40 50 60 70 80 90 100 110 120

0.00

0.02

0.04

0.06

0.08

0.10

33.84

21-Jan-11

0.00

0.02

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0.06

0.08

0.10

0 10 20 30 40 50 60 70 80 90 100 110 120

0.00

0.02

0.04

0.06

0.08

0.10

33.7721-Jan-11

0.00

0.05

0.10

0.15

0.20

0 10 20 30 40 50 60 70 80 90 100 110 120

0.00

0.05

0.10

0.15

0.20

14.3621-Jan-11

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 10 20 30 40 50 60 70 80 90 100 110 120

0.00

0.02

0.04

0.06

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0.12

35.78

21-Jan-11

0.00

0.02

0.04

0.06

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0.10

0.12

0 10 20 30 40 50 60 70 80 90 100 110 120

0.00

0.02

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0.06

0.08

0.10

0.12

20.78

21-Jan-11

Exhibit 34: Prompt LME aluminum



Imputed volatility distributions since 15-Dec-09 likely underestimate risk

Exhibit 36: Prompt CMX gold



Exhibit 38: Prompt CBT corn



Exhibit 40: Prompt CBT soybean meal



Exhibit 35: Prompt LME lead



Exhibit 37: Prompt CMX silver



Exhibit 39: Prompt CBT soybean



Exhibit 41: Prompt CBT soybean oil




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January 23, 2011

potential disruption, last summer genomics scientists

reported they had successfully created the first self-replicating, synthetic bacteria cell. The press focused on

the supposed Frankenstein dimension of creating life. Lost

in this portrait was the question of why the scientists did

it: their objective is to engineer garbage-eating, methane-

eating bacteria that could be placed in confined waste

receptacles and produce baseload power while also reducing

landfill deposits. The coal, gas, or oil producer who ignores

disruptive experiments like these is likely underestimating

risk on the margin.

7. Infrastructure failure: In September, a natural gas pipeline

explosion in San Bruno, CA destroyed 30 homes and killed

8 people, refocusing attention on the agedness-of-infrastructure theme. The previous year, Venezuela's Guri

power complex, the third largest hydroelectric dam in the

world, nearly reached catastrophically low levels of water

flow through its turbines, triggering a country-wide power

crisis, on a combination of a severe drought,

underinvestment in alternative capacity, and poor strategic

planning. Not so long before that, the Minneapolis bridge

failure had spotlighted previously under-recognized risks

in US transportation systems. We are concerned that the

potential for both commonplace problems (pipeline breaks)

and more rare events (geomagnetic storms disrupting power

grids, as happened in Quebec in 1989) are being ignored.

8. Leverage: Consensus is aware of the serious risks in the

debt and deleveraging still to be resolved in European

sovereign balance sheets, US state and municipal budgets,

and Chinese property markets. Any of these factors could

derail our views. However, it is the long-run US entitlement

math that most continues to worry us, especially the

probability that the Social Security trust fund debate

reemerges ahead of the 2012 US presidential campaign.

This debate holds the potential to cast serious doubt about

the long-run fiscal health of the entitlement programs and

even the federal budget itself, as acknowledged in a Financial

Times op ed three days ago by Peter Orszag, a health

economics expert who resigned last year as PresidentObama's handpicked director of the Office of Management

and Budget (OMB). Last summer's annual trustee reports

for the Old Age and Survivors Disability Insurance (OASDI)

funds, which include the official projections for the long-

run financing of the Social Security program, were submitted

later in the year than any of their predecessors in the 70-year

history of the program (Exhibit 51).

9. Rising debt lifts cost curves: Spain has recently rescinded

intended subsidies to its solar power sector, while US solar

companies have recently announced layoffs and plant

closures. These factors hold the potential to raise the

marginal costs embedded in the long-dated oil curve abovewhere we presently mark them, which would further open up

potential oil price volatility on the longer-dated horizons.

10. Riots over availability of food: Concerns about food price

riots are rising. While isolated incidents of riots have

occurred in Algeria and Mozambique, larger disturbances

have generally not manifested. It seems to us that one

unexpected reason why is that many of the focal countries

for food price inflation in 2008 have subsequently been so

beset by natural disasters (Haiti, Pakistan, Bangladesh)

that factors other than prices have so far been more pressing.

This could change if wheat prices move substantially

higher, as we expect them to do this summer.

Exhibit 50: US food CPI-Urban

Percentage change Y/Y, SA

Source: BLS, J.P. Morgan Commodities Research

0

5

10

15

20

25

1970 1970 1971 1971 1972 1972 1973 1973 1974 1974 1975 1975

15-Aug-71

President Nixon announces food

price and wage controls

US food CPI surges to

20%y/y within two years

Exhibit 51: Timeline of release dates for the annual Old Age and

Survivors Disability Insurance trustee report

Source: OASDI, J.P. Morgan Commodities Research


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January 23, 2011

Exhibit 52: Current geometry of the world rate cycle

Name of Rate Date Level Date Target Rate 2010E (US$bn) 2011F YoY

TURKEY 1-WEEK REPO RATE (0.25) 20-Jan-11 6.25 CURRENT 6.25 733 4.5%

POLAND 7-DAY INTERVENTION RATE 0.25 20-Jan-11 3.50 25-Jun-09 3.75 570 4.0%

BRAZIL SELEC OVERNIGHT RATE 0.50 19-Jan-11 8.75 22-Jul-09 11.25 2,155 4.5%

SOUTH KOREA BASE RATE 0.25 13-Jan-11 2.00 12-Feb-09 2.75 1,030 4.2%

THAILAND 1-DAY REPO RATE 0.25 12-Jan-11 1.15 24-Dec-09 2.25 313 5.0%

PERU REFERENCE RATE 0.25 6-Jan-11 1.25 31-Aug-09 3.25 152 6.5%

TAIWAN OFFICIAL DISCOUNT RATE 0.13 30-Dec-10 1.25 18-Feb-09 1.63 443 5.0%

CHINA 1-YEAR WORKING CAPITAL 0.25 26-Dec-10 5.31 22-Dec-08 5.81 5,739 9.1%

RUSSIA 1-WEEK DEPOSIT RATE 0.25 24-Dec-10 2.75 31-May-10 3.00 1,507 4.4%

SWEDEN REPO RATE 0.25 22-Dec-10 0.25 8-Jul-09 1.25 438 4.3%

HUNGARY 2-WEEK DEPOSIT RATE 0.25 20-Dec-10 5.25 26-Apr-10 5.75 166 2.8%

CHILE DISCOUNT RATE 0.25 16-Dec-10 0.50

the volatility is the message_jp morgan_jan2011

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