2014 BURRIDGE CONFERENCE Friday October 24, 2014

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October 2014 Colin P. Fenton Managing Director Head of Global Commodities Research and Strategy

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"Other Disclosures" last revised June 21, 2014.

Copyright 2014 JPMorgan Chase & Co. All rights reserved. This report or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan. 2 In the 5-year period ending June 30, 2008, the rolling prompt NYM natural gas price twice surged past $12/MMBtu, spurring huge plans for imports to fill gaps.

NYM Natural Gas price (NG1), $/MMBtu Planned LNG import terminals, United States (June 2006)

But unforeseen technological breakthroughs lurked on the horizon. Commercially viable shale gas production emerged in 2007 & now accounts for more than half of gross withdrawals in the United States — the world’s largest producing country.

U.S. natural gas production (billion cubic feet per day)

Source: EIA, J.P. Morgan Commodities Research US natural gas imports and exports (billion cubic feet per day) July 2014: Net imports = 2.46 bcfd. The average in 2009 was 7.34 bcfd.

Source: EIA, J.P. Morgan Commodities Research

As of October 14, 2014, central expectations are for the build-out of up to 17 LNG export terminals in North America. US natural gas market is balanced on Canadian inflows. Domestic baseload demand is rising, but production will be higher by 2020. Transport use is miniscule.

US natural gas production and consumption balance US natural gas demand annual decomposition Bcf/day Bcf/day

90 90

80 80

70 70

60 60

50 50

40 40

30 30

20 20

10 10

0 0

-10 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 Lease and Plant Pipe and Distribution Residential 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Commercial Industrial Electric Generation Balance Dry Gas Production Total Consumption Transport

Source: EIA, J.P. Morgan Commodities Research Source: EIA, J.P. Morgan Commodities Research

Diesel demand on the roads is >10X larger than diesel demand from railroads. Gas is barely used in U.S. freight transport. Biomass use is 2X larger.

Fuel consumption by U.S. transportation sector (trillion Btu)

Source: Company reports, DoT, EIA, J.P. Morgan Commodities Research Existing alternative fuel infrastructure in the US is embryonic.

59 LNG stations (From 49) 289 biodiesel stations (From 323) 8,511 electric stations (From 7,597)

748 CNG stations (From 670) 2,376 ethanol (E85) stations 2,711 LPG stations (From 2,736)

Source: DOE. Excludes private stations.

The economic purpose of the enormous oil-gas price gap is to encourage large- scale displacement of diesel fuels by gas in the transportation segment.

Spot history of ICE gasoil and NYM natural gas prices with current forward curves (dotted lines, as of 12-Sept-14), $/MMBtu

Source: ICE, NYM, J.P. Morgan Commodities Research.

Weather: the very thin line between order and chaos.

Heating degree days, US West North Central region

Coal consumption (2011=100, original data in mtoe) For the past six months, sea surface temperature and other climate data have pointed to at least a two-thirds probability of an El Nino event in 2014.

Source: NOAA

Deviations from normal weather are strong, but not uniform.

Weather heat map: color scale goes from warmest (deepest red) to coldest (deepest blue)

Source: NOAA, Custom Weather, J.P. Morgan Commodities Research Arctic clippers have sent Consuming East cash markets spiking much higher than consensus believed possible, but higher vol is an underlying trend.

5 day rolling average fixed price: Algonquin Transco 6 non-NY (mid-point) next day City Gate Boston vs Transco Zone 6 NY natural gas price $/MMBtu $/MMBtu

$140 $140

$120 $120

$100 $100

$80 $80

$60 $60

$40 $40

$20 $20

$0 $0 Jul-07 Apr-02 Apr-09 Oct-05 Oct-12 Jun-03 Jan-04 Jun-10 Jan-11 Mar-05 Mar-12 Feb-08 Nov-02 Nov-09 Dec-13 Dec-06 Sep-01 Aug-04 Aug-11 Sep-08 May-06 May-13 Transco Z6 NY Algonquin City Gate 2-Jul-13 7-Feb-14 4-Mar-13 9-Dec-13 22-Jul-13 3-May-13 13-Apr-13 10-Oct-13 30-Oct-13 12-Jun-13 18-Jan-14 23-Jan-13 24-Mar-13 12-Feb-13 29-Dec-13 11-Aug-13 31-Aug-13 20-Sep-13 19-Nov-13 23-May-13 Source: ICE, J.P. Morgan Commodities Research Source: Platts

The UN says human civilization will grow from 7Bn today to 9Bn by 2050. The largest growth in mouths-to-feed will be in areas least equipped today to handle it. Trade, technology, and education must be part of the solution to manage this risk.

Global population growth and percent of growth by region (2010-2050) Global arable land and population

Source: United Nations (2011) Source: United Nations (2011) Oil and gas wells are getting deeper and costs are rising

Global offshore drilling count reveals that production is becoming Offshore gas field operating costs are rising more rapidly than more dependent on fewer & larger ultradeepwater fields (number) onshore gas operating costs

Index 1976=100 (deflated by US GDP price deflator) 3000 114 108 128 250 102 2500 416 370 148 200 422 404 2000 397 150 1500 2324 100 1000 2268 2176 2075 1731 50 500 0 0 2005 2006 2007 2008 2009 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 Shallow water Deepw ater Ultra deepw ater Onshore gas Offshore gas Source: Offshore Magazine, J.P. Morgan Commodities Research Source: EIA, J.P. Morgan Commodities Research Major US energy companies' E&P spending by region Unconventional drilling uses more steel than conventional methods surged in 2000 and took off in 2004 due to EM demand

US$ billions $200 Canada Europe Eurasia Africa Middle East Other Eastern Hemisphere Other Western Hemisphere US $100

$0 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007

Source: Devon Energy, Vallourec Source: EIA, J.P. Morgan Commodities Research

The need for additional supplies has lead companies to pursue much deeper wells in much more difficult to reach places: for example, below oceans.

Global offshore drilling count reveals Petrobras’ deepwater operations drive oil valuation that production is becoming more dependent on fewer & larger ultra- Enchov a Piraúna Marimbá MarlimMarlim Sul Marlim Sul Roncador Roncador Lula deepwater fields (Number of wells)

3000 114 108 128 102 2500 416 370 148 422 404 1,000m 2000 397 1500 2324 1000 2268 2176 2075 1731 2,000m 500 0 2005 2006 2007 2008 2009 2,629m 2,825m 3,000m Shallow water Deepw ater Ultra deepw ater 2,930m 3,167m 1977 1983 1992 3,230m Source: Offshore Magazine 3,318m 1997 1988 1994 3,759m 4,000m 1999 Offshore gas field operating costs are 4,343m rising more rapidly than onshore gas 2003 operating costs (Index 1976=100 5,000m (deflated by US GDP price deflator)

250 6,000m

200 7,000m 150 2007 7,000m 100 Source: Petrobras 50

0 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 Onshore gas Offshore gas

Source: EIA Deeper wells use more steel: the extra steel is not costless

Rising seamless pipe prices are one of the several key cost The IEA’s latest estimate of the global oil production cost curve, drivers of oil prices relative to resource

Seamless steel pipe cost, $/t (LHS), prompt NYM WTI crude oil price in US$/bbl (RHS), annual averages 3,000 120

2,500 100

2,000 80

1,500 60 Seamless Pipe WTI 1,000 40

500 20

- 0 2000 2003 2005 2008 Source: Company Reports, ICE, J.P. Morgan Commodities Research Chinese gasoline imports surged ahead of the Beijing Olympics in 2008 (Thousand tons per month)

1,200

1,000 Diesel Gasoline 800

600 Source: IEA Medium-Term Oil Market Report© OECD/International Energy Agency 2011, page 62.

400

200

0 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Sep-06 Sep-07 Sep-08 Sep-09 Sep-10 May-06 May-07 May-08 May-09 May-10 May-11

Source: China Customs General Administration, J.P. Morgan Commodities Research

Strong commodity demand has exhausted spare capacity, forcing a sustained call on hard-to-get and costly supplies. Producer hedging needs a partner.

Seamless steel price plotted against COGS for Exxon Average depth of crude oil exploratory and developmental Billions USD (LHS), $ per ton (RHS) wells drilled in the US (1950-2008), feet per well

$120 $3,500 6000 $100 $3,000 5500 $2,500 $80 5000 $2,000 $60 $1,500 4500 $40 $1,000 4000 $20 $500 3500 $- $0 3000 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 XOM COGS Seamless steel 1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006

Source: Company Reports, PipeLogix, JP Morgan Commodities Research Source: EIA, JP Morgan Commodities Research Data on oil recovery lease and operating costs prove: (1) costs rose sharply between 2003 and 2008, and (2) marginal costs grew faster than average costs

Oil lease equipment costs Oil recovery operating costs Natural gas lease equipment costs (x-axis) well depth (feet), % chg 2003-08 (y-axis) (x-axis) well depth (feet), % chg 2003-08 (y-axis) (x-axis) well depth (feet), % chg 2003-08 (y-axis)

100% 45% 68.0% 67%

87% 67.0% 90% 44% 44% 66.0% 43% 80% 75% 65.0% 64% 43% 43% 64% 68% 64% 70% 42% 64.0% 63% 42% 63.0% 60% 50% 62.0% 50% 41% 61.0% 40% 40% 60.0% 2000 4000 8000 12000 2000 4000 8000 12000 2,000 4,000 8,000 12,000 16,000

Source: EIA, JP Morgan Commodities Research Source: EIA, JP Morgan Commodities Research Source: EIA, JP Morgan Commodities Research Fundamentals, not excessive speculation, are driving commodity prices. In 2008, three major events in China each caused a large surge in oil demand.

Chinese gasoline and diesel imports surged in 2008 due Times word mentioned in Sept 2008 U.S. Senate Hearing to a number of key events in China before Subcommittee on Energy, “Speculative Investing Thousand barrels per day in Energy Markets” (88 pgs, 53,935 words)

300 350 Diesel Gasoline 318 Sichuan Earthquake 300 250 Beijing Olympics Severe snowstorms

250 200

200

150 150

100 100

50 50 23 0 0 1 0 0 Snow Earthquake Olympics China, Speculate*, Chinese speculator, speculation, Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Sep-06 Sep-07 Sep-08 Sep-09 Sep-10 Sep-11

May-06 May-07 May-08 May-09 May-10 May-11 speculative

Source: China Customs General Administration Source: U.S. Senate, J.P. Morgan Commodities Research Note: According to local news reports from February 23, 2008, 129 people were killed Note: The hearing took place before the Subcommittee on Energy of the Committee on and 1.66 million people were evacuated in the January 2008 snowstorms. The storms Energy and Natural Resource, United States Senate, September 16, 2008. It was were described as the heaviest accumulation in 50 years in some areas. The Sichuan entitled “Speculative Investment in Energy Markets.” earthquake, the strongest in China since 1950, killed 69,000 people (38X the death toll *includes “speculating” of Hurricane Katrina ) and led Premier Wen to mobilize the army for search and rescue operations. These efforts, involving large numbers of trucks and military equipment in mountainous terrain, were very diesel intensive. 3

Chinese exchanges now account for 23% of global open interest in base metals: up from 7% in 2006 and equal to about half of China’s share of physical metals demand. Energy, at 0.1%, is low compared to China’s 11% share of oil demand.

Energy open interest by country, % of world Industrial metals open interest by country, % of world

80% 100% 67.5% 2006 2011 87.0% 2006 2011 70% 90% 80% 60% 56.5% 68.4% 70% 50% 42.8% 60% 40% 32.1% 50% 30% 40% 30% 22.9% 20% 20% 8.8% 10% 7.0% 6.0% 0.3% 0.3% 0.0% 0.1% 0.5% 0.0% 10% 0% 0% US UK Tokyo China Dubai UK China US

Source: Exchanges, J.P. Morgan Commodities Research Source: Exchanges, J.P. Morgan Commodities Research

Grains & oilseeds open interest by country, % of world Softs & livestock open interest by country, % of world

100% 90% 84.2% 2006 2011 2006 2011 90% 80% 74.6%

80% 86.8% 80.0% 70% 70% 60% 60% 50% 50% 40% 40% 30% 30% 20% 15.1%

8.7% 15.9% 20% 11.2% 2.4% 1.8% 1.6% 1.5%

0.9% 9.4% 0.4% 0.3% 0.2% 0.2%

10% 0.1% 0% 10% 3.3% 0.2% 0.2% 1.1% 0.0% US China UK India Tokyo South Australia 0% Africa US China UK Brazil Japan

Source: Exchanges, J.P. Morgan Commodities Research Source: Exchanges, J.P. Morgan Commodities Research As Asian physical demand rises as a share of global demand, Asian exchanges, listing local basis contracts, will inevitably rise to manage risks. This process has already begun and is completely driven by risk in physical fundamentals.

Open interest in global agriculture futures markets, year-end 2011 (US$ millions)

CBT DCE TGE LIF MGE ZCE NDX KBT SAF ASX

Corn

Wheat

Soybeans

Soybean Meal

Soybean Oil

$0 $5,000 $10,000 $15,000 $20,000 $25,000 $30,000 $35,000 $40,000 $45,000

„ China’s share of agriculture futures open interest in USD terms increased from 5% in 2006 to 15% in 2011. In the associated physical markets, China now accounts for 19% of global production and 22% of global consumption.

Source: Exchanges, J.P. Morgan Commodities Research

Claim: Since 2000, commodity price volatility has been larger than in the past

Daily price change in prompt ICE Brent crude oil Daily price change in prompt ICE Brent crude oil US$/bbl Percentage change

$15.00 40% 2007/09 6-June-08 9-May-11 30% 1990/91 Recession Recession $10.00 +$6.77 +$10.15 6-Aug-90 20% 31-Dec-08 +14.0% $5.00 +13.5% 10% $- 0%

$(5.00) -10% -20% 5-May-11 $(10.00) 17-Jan-91 5-May-11 -30% 17-Jan-91 –8.6% –$10.50 –34.8% $(15.00) –$10.39 -40% 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Source: ICE, US Treasury, J.P. Morgan Commodities Research Source: ICE, J.P. Morgan Commodities Research Daily price change in prompt NYM heating oil Daily price change in prompt NYM heating oil US cents/gallon. Higher base price means larger absolute swings for any vol. % chg. Controlling for higher cost reveals vol is actually more inbounds than before.

40 40% 1-Mar-10 30% 30 9-Jul-07 +29.32 20 +20.14 cents 20% +9.5 10 10% 0 0% (10) -10% (20) -20% –9.5% 4-Feb-98 21-Dec-01 10-Jan-05 (30) 13-Dec-91 31-Dec-92 10-Jan-05 21-Jun-10 -30% 13-Dec-91 –13.6% 31-Dec-92 –18.9% –17.5% –24.48 –29.64 cents –21.99 cents –24.0% (40) –23.45 cents -40% –32.4% 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Source: NYM, J.P. Morgan Commodities Research Source: NYM, J.P. Morgan Commodities Research Wheat prices are rising because production costs are rising across farmers’ balance sheets. Average production cost for North Dakota wheat doubled between 2002 and 2012, and that state is at the heart of Bakken oil output.

2002 2007 2012

Source: North Dakota State University Extension Service Source: North Dakota State University Extension Service Source: North Dakota State University Extension Service

Cost curves go up and down through time. Critically, demand that calls on 90%+ of production is calling on the highest cost supply. Each 1% increase in demand on the right side of this chart is asking for increasingly marginal (costly) wheat.

Distribution of wheat operating plus ownership costs per bushel, 2004 and 2008 (US$ per bushel, y-axis; percent of wheat farms, x-axis)

2008 price = $6.80/bu

2008 costs

2004 price = $3.40/bu

2004 costs

Source: USDA, Economic Research Service using data from UDSA’s 2004 Agricultural Resource Management Survey for wheat (2009) Wheat prices have been rising in nominal terms, but they are still low in inflation- adjusted terms. Today’s price is about half the average real price from 1873 to 2007.

Inflation-adjusted annual price of Montana wheat Nominal annual price of Montana wheat US$ per bushel (2010$) US$ per bushel

$40 $40

$35 $35

$30 Pre WWI $30 average 1873–2007 $19.12 $25 average $25 $14.01 1982–2003 $20 average $20 $5.54

$15 $15

$10 $10

$5 $5

$- $- 1873 1879 1885 1891 1897 1903 1909 1915 1921 1927 1933 1939 1945 1951 1957 1963 1969 1975 1981 1987 1993 1999 2005 2011 1873 1879 1885 1891 1897 1903 1909 1915 1921 1927 1933 1939 1945 1951 1957 1963 1969 1975 1981 1987 1993 1999 2005 2011

Source: USDA, J.P. Morgan Commodities Research Source: USDA

In rich countries, most food waste is done by the consumer, implying that prices would need to rise much higher to change behavior. In poor countries, almost all waste is incurred as a result of technological inefficiency in harvesting.

Percent of cereal production lost or wasted at various stages of food supply chain (percent)

Production to Retail Consumer 40%

35%

30%

25%

20%

15%

10%

5%

0% North America & Europe Industrialized Asia North Africa, West & Latin America South & Southeast Sub-Saharn Africa Oceania Central Asia Asia

Source: FAO (2011), Swedish Institute for Food and Biotechnology, J.P. Morgan Commodities Research At the same time, there is enormous waste in the energy-intensive production and distribution of food. At least one-third of the global food produced is wasted.

33% of all food produced in the world is wasted or lost. Between 1985 and 2007, This is equivalent to about 1.3 billion tons per protein intake per year. capita in Asia grew by 15.4%, while sugar 20% of all US households with intake increased by children were classified as "food- 31.6%. In sub-Saharan Africa, insecure" last year. the population of "food-insecure" people is expected to In industrialized countries, food waste at the consumer rise by 17 million level is 222 million tons per year. In sub-Saharan people this year. Africa, total net food production is 230 million tons per year.

More than 35% of household final consumption is spent on food in 17 countries.

Source: USDA (2011), FAO (2011), J.P. Morgan Commodities Research

Recent observed volatility in wheat prices is not atypical of historical trends. In 255 of the past 750 years (34% of the sample), swings in annual average wheat prices exceeded 20%. The year 2011 was not one of them.

Y/Y change in the average price of wheat since the Middle Ages Percent

150% 2011 +19.7% 1973 +112.3%

1933 100% +86.7%

1916 +60.5% 2007 +57.3%

50%

0%

-50% 1920 –41.0% 1938 –51.1%

-100% 1260 1272 1284 1296 1308 1320 1332 1344 1356 1368 1380 1392 1404 1416 1428 1440 1452 1464 1476 1488 1500 1512 1524 1536 1548 1560 1572 1584 1596 1608 1620 1632 1644 1656 1668 1680 1692 1704 1716 1728 1740 1752 1764 1776 1788 1800 1812 1824 1836 1848 1860 1872 1884 1896 1908 1920 1932 1944 1956 1968 1980 1992 2004

Source: G. Clark time series data for Europe, 1261-1872, then USDA Montana wheat, J.P. Morgan Commodities Research Asia’s needs for investment in energy infrastructure are gargantuan: at least $480 Billion per year, every year, through 2030.

Source: IEA, JP Morgan Commodities Research

In oil, gas, and coal, expected US capex requirements to sustain growth and prepare for the future are as large as Asia’s investment needs in these markets.

Source: IEA, JP Morgan Commodities Research Even the largest corporations are subject to extinction through “creative destruction”. Current commodity prices are high enough (and technology costs are deflating fast enough) to spur use of genetics to create new seeds and fuels.

Nanodynomite – commodities on the nanoscale Sequencing cost per genome in natural log terms

„ Power generation: Researchers at MIT and $100,000,000

RMIT have discovered that a chemical Sep-01, reaction in a fuel-coated nanotube creates a $95,263,072 strong electric current, which may be able to $10,000,000 provide “bursts” of power to small systems. Oct-07, $7,147,571 „ Storage: Stanford researchers used $1,000,000 nanoparticles to make a new electrode that could be used in an efficient, durable, high- power, rechargeable battery to store electricity on the grid. $100,000

„ Information processing: A professor at UVA

published a breakthrough showing the $10,000 Jul-11, creation of a “massive number of entangled $10,497 qubits,” which could be used for information processing in the hypothetical quantum $1,000 computer, with capabilities far beyond today’s most sophisticated supercomputers. 2001 2002 2002 2003 2004 2004 2005 2005 2006 2006 2007 2008 2008 2009 2009 2010 2011

Source: RMIT, MIT, Stanford, UVA, J.P. Morgan Commodities Research Source: National Human Genome Research Institute, J.P. Morgan Commodities Research

When a producer externalizes its risk by selling a futures contract to an investor or a consumer, he must offer a discount to the price expected to prevail in the future.

The discount provided on WTI crude oil to futures investors at expiry minus two years and expiry minus one year, calculated for the June contract, using the average May price

Contract Average price during the month of May (US$ per barrel) Discount on price (US$ per barrel) Discount on price (%) at expiry minus 2 at expiry minus 1 at expiry minus 2 at expiry minus 1 at expiry minus 2 at expiry minus 1 years year at expiry years year years year Jun-00 $ 17.7 $ 16.7 $ 28.4 $ 10.7 $ 11.7 60% 70% Jun-01 $ 16.9 $ 23.9 $ 28.7 $ 11.8 $ 4.7 70% 20% Jun-02 $ 21.1 $ 26.0 $ 27.6 $ 6.5 $ 1.6 31% 6% Jun-03 $ 23.7 $ 24.4 $ 27.6 $ 3.9 $ 3.2 16% 13% Jun-04 $ 23.0 $ 25.0 $ 40.2 $ 17.2 $ 15.2 75% 61% Jun-05 $ 24.1 $ 34.7 $ 49.5 $ 25.4 $ 14.8 105% 42% Jun-06 $ 32.1 $ 52.0 $ 70.8 $ 38.8 $ 18.9 121% 36% Jun-07 $ 50.5 $ 74.6 $ 63.2 $ 12.7 $ (11.3) 25% -15% Jun-08 $ 72.6 $ 70.1 $ 123.2 $ 50.5 $ 53.1 70% 76% Jun-09 $ 70.4 $ 122.7 $ 57.1 $ (13.3) $ (65.7) -19% -53% Jun-10 $ 121.1 $ 66.9 $ 75.2 $ (45.9) $ 8.4 -38% 13% Jun-11 $ 70.9 $ 82.9 $ 101.8 $ 30.85 $ 18.89 44% 23% Average discount provided to futures investors $ 57.8 $ 26.94 $ 14.03 46.6% 24.3%

Source: NYMEX, J.P. Morgan Commodities Research

„ Since 2000, the futures market has provided an average 24.3% discount to investors in WTI crude oil futures at one year prior to expiry, or $14.03 per barrel against the 12-year average. This has allowed companies to finance investment that has increased oil production and constrained oil price appreciation from what it would otherwise be.

A simple calculation of “net” positions in WTI crude oil misstates true risk levels

NYM WTI crude oil Net positions (long – short) 2011 average positions, thousand contracts

Not Reportable, Other 6.5% Reportables: The managed money long in NYM WTI crude oil is 6.9% of total risk Net, 4.8% PMPU: Net, Open interest by group 34.5% 2011 average positions, thousand contracts

NOT REPORTABLE NOT REPORTABLE LONG SHORT PMPU LONG OTHER 2.9% 1.9% REPORTABLE 9.5% Managed SPREAD Money: Net, 16.3% 38.7% OTHER PMPU SHORT Swap Dealers: REPORTABLE 14.8% Net, 15.5% SHORT 1.9% OTHER REPORTABLE LONG 2.6% SWAP DEALER LONG MANAGED 5.9% MONEY SPREAD This calculation is one of the sources of the 8.4% incorrect assertion that “managed money MANAGED MONEY SWAP DEALER SHORT SHORT accounts for 40% of the oil futures market”. 8.3% 1.0% MANAGED MONEY LONG SWAP DEALER 6.9% SPREAD 19.7%

The actual numbers: managed money (MM) length accounts for 7%, MM shorts account for 1%, and MM spreads account for 8%, for a total of 16%.

Source: CFTC, J.P. Morgan Commodities Research

Analyses of CFTC data on traders’ positions often ignore spreading or compare shares on a net position basis. Including all CFTC data reveals that the Managed Money “Longs” across 26 markets amount to only a 9% share (only 7% in WTI).

Open interest by group using CFTC’s transparent Disaggregated Commitment of Traders Report 2011 average positions, thousand contracts

„ Managed money long positions only account for 9% of total open interest in 26 key NR Short NR Long 5% PMPU Long commodity markets* and only about 7% in 5% 12% NYM WTI crude oil OR Spread 10%

OR Short 2% „ Groups included in CFTC reporting: OR Long 2% PMPU Short 24% „ PMPU – Producer/Merchant/ MM Spread 6% Processor/User

MM Short 3% „ SD – Swap Dealers

„ MM – Managed Money MM Long SD Long 9% SD Spread SD Short 10% „ OR – Other Reportables 8% 4% „ NR – Non-Reportable

Source: CFTC, J.P. Morgan Commodities Research *Includes NYM WTI crude oil, ICE WTI crude oil, ICE Brent crude oil, ICE gas oil, NYM heating oil, NYM RBOB gasoline, NYM natural gas, CMX copper, CMX gold, CMX silver, NYM palladium, NYM platinum, CBT corn, CBT soybeans, CBT soybean meal, CBT soybean oil, CBT wheat, KBT wheat, MGE wheat, ICE sugar no. 11, ICE cotton no. 2, ICE coffee, ICE cocoa, CME lean hogs, CME feeder cattle, CME live cattle Even in gold (indisputably an investor-intensive market), the managed money long amounts to only a 15% share. The swap dealer short likely reflects producer hedging done OTC, which is then hedged back into the listed market. NYM WTI Crude Oil NYM RBOB Gasoline

OR Spread NR Long NR Short NR Short PMPU Long OR Short 3% 4% NR Long 2% 3% 3% 9% 2% PMPU Long OR Spread OR Long 20% 16% MM Spread 3% PMPU Short 3% 14% OR Short MM Short 2% 1% OR Long 3% MM Long SD Long 12% 6% MM Spread 9% SD Short MM Short 8% SD Spread 4% 1% SD Short PMPU Short MM Long 2% SD Long 37% SD Spread 6% 7% 20%

Source: CFTC, J.P. Morgan Commodities Research Source: CFTC, J.P. Morgan Commodities Research CBT Wheat CMX Gold

NR Short NR Short PMPU Long NR Long PMPU Long NR Long 7% 3% 8% 5% 7% 6% OR Spread OR Spread 10% 12% PMPU Short OR Short 22% OR Short PMPU Short 3% 2% 22% OR Long OR Long 2% MM Spread 4% MM Spread 3% 5% MM Short 1% SD Long MM Short 5% 7% SD Long 19% SD Short MM Long MM Long 11% 7% SD Spread SD Short 15% SD Spread 4% 2% 8% Source: CFTC, J.P. Morgan Commodities Research Source: CFTC, J.P. Morgan Commodities Research

Most open interest in a futures market is within the next 12 months, but speculator positions do extend out 5+ years. Each forward year is attached to a new year of physical market risk, a critical point in market share calculations.

Forward curve of ICE Brent crude oil: (contract month, x- Forward curve of NYM WTI crude oil: (contract month, x- axis; price in US$/barrel, y-axis; open interest in million axis; price in US$/barrel, y-axis; open interest in million US$, bubble size) US$, bubble size)

$130 = $1,500 $130 = $1,500

$125 $125

$120 $120

$115 $115

$110 $110

$105 $105

$100 $100

$95 $95

$90 $90 -13 -14 -16 -17 -11 -11 -13 -13 -14 -14 -16 -16 -17 -17 -11 -12 -13 -13 -13 -14 -14 -14 -15 -16 -16 -16 -17 -17 -17 -12 -12 -13 -14 -15 -15 -16 -17 Jul-12 Jul-15 Apr-12 Oct-12 Apr-15 Oct-15 Jun-17 Jun-14 Jan-16 Jan-13 Mar Mar Dec Nov Dec Nov Dec Sep Aug Sep Aug Sep May May Jul Jul Oct Oct Apr Apr Jan Jun Jan Jun Mar Mar Dec Aug Nov Sep Dec Aug Nov Sep Dec May May Source: ICE, J.P. Morgan Commodities Research, Updated as of 23-Mar-12 Source: NYM, J.P. Morgan Commodities Research, Updated as of 23-Mar-12 The sum total of all open interest across the top three oil futures markets (including all corporate positions, not just speculators) is a fraction of just one year of global production value risk in the physical market ($3.2Tn and more).

Percent of annual global oil production value risk (estimated by one year of production times current price of Brent crude) covered by all futures in the ICE Brent, ICE WTI, and NYM WTI crude oil markets (total open interest)

7.0%

6.1% 6.0%

5.0%

4.0%

3.0%

2.0%

1.3%

1.0% 0.4% 0.1% 0.1% 0.0% Y1 Y2 Y3 Y4 Y5

Source: ICE, NYM, J.P. Morgan Commodities Research, Updated as of 16-Mar-12

All of the oil trading in the world, including non-speculators, is providing insurance on just 6% of global physical production risk. Without speculators, the coverage would be even lower, for both producers and consumers.

Open interest (OI) in 1000-barrel contracts, prices in $/bbl, value in billions USD. Market data as of settlement on Friday, July 29, 2011.

Source: NYM, ICE, J.P. Morgan Commodities Research Because index investors are managing toward an exposure ratio, when prices go up, they reduce their position. When prices go down, they buy. This smoothing force is clear in CFTC data. Investors help dampen volatility and reduce prices.

Open interest in contracts (LHS), CBT wheat in US $/bu (RHS)

250000 1400 Net position of index investors Prompt CBT wheat price

1200 200000

1000

150000 800

600 100000

400

50000 200

0 0 2006 2007 2008 2009 2010 2011 2012

Source: CFTC, CBT

48

If it were true that there is $20 per barrel of non-fundamental froth in oil futures (implying a +240% annual oil return), it should be visible in passive index returns. It is not. S&P GSCI investors lost money in 2011, despite surging oil prices.

Annual return on S&P GSCI (percent)

50% 50% 41% 40% 38% 34% 32% 33% 28% 29% 30% 26% 24% 20% 21% 20% 16% 17% 13% 12% 10% 9% 10% 4% 5% 1% 2% 0% -1% -10% -6% -12% -14% -15% -20%

-30% -32% -40% -36%

-50% -46% 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

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

In the year ending June 30, 2014, the total returns performance of the S&P GSCI subindices closely tracked our expectations, except for livestock.

Also as expected, correlations within the S&P GSCI and across asset classes are deteriorating. This textbook result is nonetheless contrary to a BIS forecast. Claim: "Inefficient financial activity in the futures market pushed oil prices about 15 percent above the level justified by (current and expected) oil fundamentals over the period 2000-mid 2008, when the volume of crude oil derivatives traded on NYMEX quintupled.“*

Managed Money’s trading does not show persistent correlation Efficient frontier analysis shows that a 7% allocation to commodities vs. the path of associated futures prices would optimize risk/reward in US 60/40 portfolios June 20, 2006 through July 26, 2011 (N=268 weeks). For reference, S&P Ann. volatility (x-axis), Ann. total return (y-axis). Jan-1989 through Jul-2011 (60% GSCI spot index portfolio weights as of July 29, 2011 are included. S&P 500, 40% US government bonds. S&P GSCI)

Correlation of w/w simple change in net position of Managed Money and logdifference change in weekly average price at these time horizons: GSCI weight Same Week Week Ahead Month Ahead Quarter Ahead Heating Oil 5.21 0.36 (0.08) (0.10) (0.01) RBOB Gasoline 5.07 0.34 0.06 0.00 0.07 WTI Crude Oil 31.92 0.44 (0.05) (0.05) (0.00) Natural Gas 3.01 0.06 0.01 (0.03) (0.04) Chicago Wheat 2.98 0.48 0.06 0.03 0.01 Corn 4.35 0.55 0.01 0.06 0.10 Soybeans 2.39 0.52 (0.04) 0.07 0.07 Raw Sugar 2.35 0.38 0.05 0.06 0.03 Cotton 1.20 0.30 0.10 (0.02) 0.03 Live Cattle 2.45 0.24 (0.05) (0.03) 0.07 Lean Hogs 1.48 0.27 0.04 (0.01) (0.05) Copper 3.73 0.44 0.06 0.08 0.12 Gold 2.91 0.48 (0.08) (0.04) 0.04 Average 0.37 0.01 0.00 0.03

Source: CBT,CME, ICE, NYM, CFTC, J.P. Morgan Commodities Research. Note: GSCI portfolio weights above sum to 69%. Brent plus gasoil Source: J.P. Morgan Commodities Research would increase coverage to 93%

*Lombardi, Marco J. and Ine Van Robays. Do Financial Investors Destabilize the Oil Price? European Central Bank Working Paper No. 1346 (June 2011), 4.

Gold and commodities in general outperform financial assets when US CPI is rising, raising the question of ‘when’ does inflation return?

Annualized total returns and volatility for various asset classes by stage of US business cycle

US CPI rate is above average and rising US CPI rate is above average and falling

30% 30% 25% 23% 25% 21% 23% 25% 22% 20% 17% 20% 15% 15% 15% 15% 12% 11% 9% 11% 10% 5% 10% 3% 5% 5% 0% 0% -5% -5% -10% -5% -10% -8% CMX Gold S&P GSCI Large Cap US US Corporate CMX Gold S&P GSCI Large Cap US US Corporate Equities Bonds Equities Bonds Ann. total return Ann. volatility Ann. total return Ann. volatility

US CPI rate is below average and rising US CPI rate is below average and falling

30% 30% 25% 21% 25% 18% 20% 16% 20% 15% 15% 14% 14% 14% 15% 15% 13% 15% 9% 10% 7% 6% 10% 8% 4% 4% 5% 5% 0% 0% -5% -5% -10% -10% CMX Gold S&P GSCI Large Cap US US Corporate CMX Gold S&P GSCI Large Cap US US Corporate Equities Bonds Equities Bonds Ann. total return Ann. volatility Current Ann. total return Ann. volatility

Source: Federal Reserve, S&P, Ibbotson, FINRA, Dow Jones, CMX, J.P. Morgan Commodities Research. Note: “rising” and “falling” refer to the path of US CPI. Above” and “below” refer to the average level of US CPI (2.3% since July 2001). Data from Jan 1971 through Jul 2014.

G O L D 47 US PPI price changes by commodity (% oya, as of August 2014).

Source: BLS

US PPI price changes by commodity (% oya, as of August 2014).

Polyethylene Polyethylene Corrugated food refuse bags grocery bags containers +5.5% +1.6% +1.2%

Plastic bottles Hydraulic Polystyrene -1.4% fracturing sand foam food +7.8% containers -3.1%

Source: BLS

Total inflows into commodity markets in 2014 ytd amount to +$90 Bn, by our reckoning. Total OI has slipped below $1Tn. Base metals have led the inflows.

APPLYING THE STRATEGIC CASE FOR COMMODITIES TO A LIVE ENVIRONMENT FOR TACTICAL RISK

FUNDAMENTALS IN OIL AS OF OCTOBER 2014

Global markets are focused on the price declines in various grades of crude oil.

Price of ICE Brent crude oil ($/b, monthly average) Price of NYM WTI crude oil ($/b, monthly average)

Source: ICE, NYM, J.P. Morgan Commodities Research

September 16, 2014: Probabilities for WTI crude oil price at NYM contract expiry, according to the options markets.

Note: 3-year trailing prices as of 10/13 are: $96.44 (average), $113.93 (high), and $75.67 (low). All US$/bbl.

Source: NYM. Geopolitical conflict in Central Europe

Source: Nations Online Project

Russia relies heavily on energy sales to Europe for export earnings. Crude and products are larger levers than natural gas.

120%

Nickel Copper Natural Gas 100% Oil Products Gold

80% Electricity Crude Oil 60% Diamonds Coal Palladium Aluminum 40%

Wheat 20%

0% Europe’s share of Russia’s export of this commodity of export Russia’s of share Europe’s 0 5 10 15 20 25 30 35 40 45 Number of European countries importing this commodity from Russia

Source: Russia and European customs data, J.P. Morgan Commodities Research, Note: bubble indicates relative size Eurasia’s economies are more gas dependent than are Europe’s. In most of Europe, oil is still the larger share of primary energy consumption.

Fuel intensity for European and Eurasian countries X-axis: oil fuel intensity (percent of total energy consumption), Y-axis: natural gas fuel intensity (percent of total energy consumption), Size of bubble: primary energy consumption, Color: region 100%

90% Uzbekistan Europe Turkmeni stan 80%

70% Belarus Eurasia

60% Azerbaijan Russian Federation 50% Lithuania Hungary Netherlands 40% Ukraine Italy Turkey Romania 30% Slovakia United Kingdom Republic of Ireland Natural gas fuel intensityfuel Natural gas Austria Denmark Belgium 20% Czech Republic Poland Germany Kazakhstan Spain Portugal Greece 10% Bulgaria France Switzerland Norway Sweden Finland 0% 0% 10% 20% 30% 40% 50% 60% Oil fuel intensity

Source: BPSR, J.P. Morgan Commodities Research

Islamic State’s annualized revenue run rate now conservatively appears to exceed $600 Mn. CRUDE OIL LOOTED ANTIQUITIES HUMAN TRAFFICKING ‘DABIQ’ MAGAZINE

55,000 barrels per day * $30 per bbl = $1.65M per day = $602M per year (annualized) Ethnic conflict in Xinjiang is intensifying and broadening

May 2014: trial of 55 “extremists” in a soccer stadium in Xinjiang

The Straits Times www.straitstimes.com Published on Sep 16, 2014 Indonesia ISIS probe throws focus on Xinjiang link Scores from China's province nabbed in South-east Asia in recent months By Zakir Hussain & Kor Kian Beng EVEN as Indonesian police continue to investigate four ethnic Uighurs arrested in Sulawesi over the weekend on suspicion of terror links, attention is being focused on the rising number of this group from China's restive Xinjiang province making their way to South-east Asia in recent months. Scores of illegal immigrants from Xinjiang have been arrested in Thailand, Malaysia and Vietnam, and analysts say the trend is linked to rising violence in their home province over the past year. Many of them arrive in the hope of seeking asylum in Turkey, a country that has been sympathetic to their plight given their ethnic and linguistic kinship, even though few have made it there. "The vast majority do not support separatism or terrorism," Dr Rohan Gunaratna of Singapore's International Centre for Political Violence and Terrorism Research told The Straits Times.

Source: University of Texas Like the Caucasus, Xinjiang is gas rich and inhabited by a population from a different ethno-linguistic tradition than the core. In many ways, especially religion, Xinjiang and Chechnya have more in common with each other than either has with the rest of China or the rest of Russia.

Source: University of Texas

The US possesses both military and economic policy options in addressing geopolitical conflict.

Economic cost of US involvement in recent military Iraqi crude oil production conflicts translated into oil barrel equivalent terms mbd, line represents average production 2003-2011 mbd „ Height:Heightg ::1 12.07cm207cm (4((4.75”).7575 ) „ Width: 11.01cm (4.33”) 3 6 – Horizontal: 3.38cm (1.33”) 8 years, – Vertical: 5.08cm (2.00”) 272 days 2.5 5

12 years, 2 4 202 days 1.5 3

1 2 79 days 0.5 1 0 0 Jul-03 Jul-11 Jul-04 Jul-05 Jul-06 Jul-07 Jul-08 Jul-09 Jul-10 Jan-03 Jan-04 Jan-11 Jan-05 Jan-08 Kosovo Afghanistan Iraq Jan-06 Jan-07 Jan-09 Jan-10 Source: Brown University, PBS, NATO, NYM, J.P. Morgan Commodities Research Source: EIA, J.P. Morgan Commodities Research Note: Costs translated at 2014 ytd average prompt WTI crude oil price. Annual path of upstream oil revenues, inflation adjusted: 1971-1987 and 1998-2013: UNITED STATES

$150

$125

2008 2011 2012 2013 $100

1981 $400 2010 2007 $75 1982 2006 1983 1980 $300 2005 2009 1984 1985 $50 2004 1979 $200 1975 2003 2000 1978 1976 1974 2002 2001 1977 1987 1986 $25 1999 $100 Green lines are 1973 1972 1998 1971 revenue isoquants in 2013 $Bn. Every point on a line yields Annual Annual real price for average domestic output (2013$/b) the same revenue. $0 6 7 8 9 10 11 12

Source: EIA, BPSR, J.P. Morgan Commodities Research Oil liquids output (mbd)

What if the US deployed the 1986 oil tactic? A 25% increase in output would completely offset a 25% decrease in price for the UNITED STATES.

$150

$125

2008 2011 2012 2013 $100 Post tactic? 1981 Price: -25% yoy 2007 2010 Output: +25% yoy $75 1982 2006 1983 1980 2005 2009 1984 $400 1985 $50 Post tactic? 2004 1979 Price: -50% yoy $300 2000 1975 Output: +50% yoy 2003 1976 1978 2001 1974 $200 2002 19771987 1986 $25 1999 Green lines are 1973 1972 1998 1971 revenue isoquants in 2013 $Bn. Every $100 point on a line yields Annual Annual real price for average domestic output (2013$/b) the same revenue. $0 6 7 8 9 10 11 12 13 14 15 16 17

Source: EIA, BPSR, J.P. Morgan Commodities Research Oil liquids output (mbd) Annual path of upstream oil revenues, inflation adjusted: 1975-1987 and 1998-2013: RUSSIA

$150

$125

2011 2012 $100 2008 2013 1980 $400 1981 2010 $75 2007 1982 $300 2006 1979 2009 2005 1984 1983 1975 1977 1985 $50 1976 1978 $200 2004 1987 2000 2003 2001 $25 1986 Green lines are 1999 2002 $100 revenue isoquants 1998 in 2013 $Bn. Every point on a line yields Annual Annual real price for average domestic output (2013$/b) the same revenue. $0 5 6 7 8 9 10 11 12

Source: EIA, BPSR, Russian Central Bank, J.P. Morgan Oil liquids output (mbd) Commodities Research

Russia’s upstream oil revenues post a 2014 deployment of the 1986 oil tactic?

$150

$125

2011 2012 $100 2008 2013 Post tactic? 1980 $400 Price: -25% yoy 1981 Output: -25% yoy 2010 $75 2007 Post tactic? 1982 $300 2006 1979 Price: -50% yoy 2009 1984 1983 Output: 1998 level 2005 1975 1977 1985 $50 1976 1978 $200 2004 1987 2000 2003 2001 $25 1986 Green lines are 1999 2002 $100 revenue isoquants 1998 in 2013 $Bn. Every point on a line yields Annual Annual real price for average domestic output (2013$/b) the same revenue. $0 5 6 7 8 9 10 11 12

Source: EIA, BPSR, Russian Central Bank, J.P. Morgan Oil liquids output (mbd) Commodities Research Annual path of upstream oil revenues, inflation adjusted: 1971-1987 and 1998-2013: SAUDI ARABIA

$150

$125

2011 2012 2008 2013 $100 1981 $400 1980 2010 1982 2007 $75 1983 2006 1984 2009 $300 1979 2005 1974 1985 1976 1977 $50 1975 1978 2004 $200 1987 2000 2003 2002 1986 2001 $25 1999 $100 Green lines are 1998 revenue isoquants 1973 in 2013 $Bn. Every 1972 point on a line yields Annual Annual real price for average domestic output (2013$/b) 1971 $0 the same revenue. 3 4 5 6 7 8 9 10 11 12

Source: EIA, BPSR, J.P. Morgan Commodities Research Oil liquids output (mbd)

Saudi Arabia’s oil revenues post a 2014 deployment of the 1986 oil tactic?

$150

$125

2011 2012 2008 2013 $100 1981 1980 2010 $400 1982 2007 Post tactic? $75 Price: -25% yoy 1983 2006 1984 2009 Output: Flat yoy 1979 2005 $300 1974 1985 1976 1977 $50 1975 1978 2004 Post tactic? Price: -50% yoy$200 1987 2000 2003 2002 Output: 12.5 mbd 1986 2001 $25 1999 Green lines are $100 1998 revenue isoquants 1973 in 2013 $Bn. Every 1972 point on a line yields Annual Annual real price for average domestic output (2013$/b) 1971 $0 the same revenue. 3 4 5 6 7 8 9 10 11 12 13

Source: EIA, BPSR, J.P. Morgan Commodities Research Oil liquids output (mbd) Developments in US oil fundamentals since our April 17 oil note.

Note: field crude production is a subset of total liquids production.

Source: EIA, .P. Morgan Commodities Research

October 13, 2014: Probabilities for WTI crude oil price at NYM contract expiry, according to the options markets.

Note: 3-year trailing prices are: $96.44 (average), $113.93 (high), and $75.67 (low). All US$/bbl.

Source: NYM. Delta of monthly average prices in 2014 to the average prices in 1H2013 for CL1 ($94.25/b) and CO1 ($108/b). Down moves hurt more than up moves feel good.

Source: Bloomberg, J.P. Morgan Commodities Research

Price volatility in rolling prompt ICE Brent crude oil has been at historic lows for the past two years. Markets seem ill-prepared for the eventual return of “normal vol in oil”.

Annualized 30-day realized and implied volatility in rolling prompt ICE Brent crude oil (%)

Source: Bloomberg, ICE, J.P. Morgan Commodities Research Perception about the recent oil price move is amplified by the fact that realized vol in June 2014 was the lowest ever. Even in October, vol. is 15%-points below average.

Despite the recent pickup in realized price volatility in CO1, all months in 2014 have been “low vol”.

average

Source: Bloomberg, ICE, J.P. Morgan Commodities Research Note: average is for the interval July 1988 through Sept. 2013 (n=303). This is the full ICE history before the bars in the chart above.

Daily settlement prices in October 2014 for select crude grades. A $75/b cash price has now been observed in Midland. How do US producers respond?

Source: Bloomberg, Platts, Reuters, J.P. Morgan Commodities Research Delta of monthly average prices in 2014 to the average prices in 1H2013 for NG1 ($3.75/MMBtu) and LME 3-month Copper ($7575/t).

Source: Bloomberg, J.P. Morgan Commodities Research

The heat map of global industrial activity remains close to three-year highs, despite the recent concerns that Germany is now joining France in an economic slide.

PMI (manufacturing) heat map: economic scale moves from below normal (deepest red) to above normal (deepest green)

Source: Government and industry sources, J.P. Morgan Commodities and Economics Research What if there had been no North American energy resurgence?

Source: EIA, IEA, Company Reports, BPSR, J.P. Morgan Commodities Research

Conventional wisdom has embraced the claim that stronger USD has caused lower commodity prices, especially oil. But this ignores that CNY has strengthened v. USD.

Since mid-April, USD has strengthened by 8% v. an OECD-biased basket. But USD is –2% v. CNY.

Source: Bloomberg, J.P. Morgan Commodities Research It is odd to ignore CNY strength given that China is structurally short crude and is the world’s 2nd largest crude importer. Price AND currency are moving in China’s favor.

In Euro terms, spot Brent was unchanged through end Sept. It was in CNY terms that mattered.

Source: Bloomberg, J.P. Morgan Commodities Research

The exclusive focus on Europe and Japan makes little sense. It is China’s self-spurred incentives and its opportunistic behavior that matters most on the oil demand side.

China's crude oil imports relative to trend China's trade balance in refined products million barrels per day (RHS: monthly avg CO1 price, $/b) million barrels per day (RHS: monthly avg CO1 price, $/b)

Source: Customs General, J.P. Morgan Commodities Research Source: Customs General, J.P. Morgan Commodities Research Note: green color signifies positive for oil price, all else equal. Note: green color signifies positive for oil price, all else equal.

China reported September trade data on OCTOBER 13. The data show that September brought the price-responsive flips we had expected in crude and products. At 6.7 mbd, inbound crude flows were more than 500kbd above trend. China has imported more than 6 mbd of crude in 7 of the past 12 months. The trend import level is now 6.2 mbd, up from 3.8 mbd in July 2008.

China’s crude oil imports (mbd) China’s crude oil imports deviation from trend (%)

30%

25% +2 σ 20%

15% +1 σ 10%

5%

0%

-5% -1 σ -10%

-15% -2 σ -20%

-25%

-30% 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Source: China Customs, J.P. Morgan Commodities Research Source: China Customs, J.P. Morgan Commodities Research

A coming headline that we expect: China imports more crude than USA, becomes world’s largest crude importer

Crude oil imports (mbd)

Source: China Customs, EIA, J.P. Morgan Commodities Research The gap in crude imports by the world’s two largest importing countries was just 545 kbd in Sept. 2014.

Difference between US crude oil imports and China crude oil imports, (US-China in mbd)

Source: China Customs, EIA, J.P. Morgan Commodities Research

China currently sources no crude from the US and < 20 kbd from Canada plus Mexico. China’s crude imports (kbd) from Iran and Russia are growing.

Source: China Customs, J.P. Morgan Commodities Research OSPs reveal the producer-on-producer export competition for Asia’s business. This factor has now been well covered in the world media.

Source: OPEC, MEES, Bloomberg, J.P. Morgan Commodities Research

Crude oil production from Libya and Iraq (kbd).The surge from Libya in August and September (to over 900 kbd) was an importer driver of the slide in oil prices.

Source: OPEC OPEC’s capacity is constrained and producers are competing to defend market share. This is neither the source of the marginal barrel nor the first place to look for price-driven production cuts.

Source: OPEC, EIA, J.P. Morgan Commodities Research

Estimated unplanned liquids outages have been large and not confined to OPEC

OPEC unplanned disruptions to liquids supply (million barrels per day)

Non-OPEC unplanned disruptions to liquids supply (million barrels per day)

Source: OPEC, EIA, J.P. Morgan Commodities Research Barriers on U.S. crude exports have crumbled. U.S. crude exports are already booming. U.S. self-sufficiency in crude is simultaneously increasing anyway.

Latest EIA estimates: week ending 26-Sept-2014

Crude Production: 8.837 mbd Crude Exports: 0.420 mbd

In absolute terms, these exports are higher than 99.9% of observations since 1920. As a share of production, they are higher than 95.6% of observations.

Domestic crude production has covered more than half of U.S. refinery inputs of crude since September 2013 (a year ago).

The last time this was true was in April 1993.

Source: EIA, J.P. Morgan Commodities Research

US crude exports, as of this first week in October 2014, tally to about 4.7% of domestic crude production.

‰ Last month, US field production of crude oil averaged 8.783 mbd. This qty is +433 kbd higher than the ytd average as of end of August. Output gained 184 kbd mom and 1.04 mbd yoy.

‰ In September US crude exports averaged 394 kbd. This qty is +91 kbd higher than the previous ytd average. Crude exports last month were +289 kbd higher than a year ago. Current exports are 6X higher than in November 2012.

Source: EIA, J.P. Morgan Commodities Research The US energy renaissance is substantial, broad, and ongoing.

U.S. liquids production by type (thousand barrels per day)

Source: EIA, J.P. Morgan Commodities Research

What are the implications for Asia if the US energy resurgence sustains?

US light crude imports (>35 degree API) US crude imports from Africa kbd mbd

Source: EIA, J.P. Morgan Commodities Research

West African crude exports to Asia kbd

2500 2000 1500 1000 500 0 Jul-12 Jul-13 Apr-12 Oct-12 Apr-13 Oct-13 Jan-12 Jan-13 Jan-14

Source: EIA, Bloomberg, J.P. Morgan Commodities Research US crude production growth above 1 mbd appears to be large enough to depress crude price volatility toward its long-run lows, even in the face of large risks. (30-day, annualized) (30-day, WTI crude oil price volatility WTI crude oil price volatility

Year on Year Growth in Crude Production from 7 US tight oil basins (kbd)

Source: EIA, JP Morgan Commodities Research

Without the contribution from tight oil formations, US crude production might be half of its current level. These are the basins to watch for slower output growth.

U.S. crude production by basin (million barrels per day)

Source: EIA, J.P. Morgan Commodities Research But the boom in U.S. energy production, exports, and potential economic and geopolitical leverage comes with a cost — a high decline rate.

Production growth from 7 tight oil basins EIA data show the month-on-month decline YoY change in barrels per day rate from legacy wells is now –287 kbd.

To sustain current NET production growth Productivity gains have been impressive, but requires +400 kbd PER MONTH of new output the oil rig count is rising again.

Source: EIA, J.P. Morgan Commodities Research

Most projections envision a coming peak in US crude oil production (including condensates) by 2020, at about 25% above last year’s level.

EIA’s long-range projection for US production of crude oil and lease condensate quadrillion BTU

Source: EIA, J.P. Morgan Commodities Research Long-dated oil prices have become largely stationary once again in both crude and products; however, liquidity is a consideration in HO beyond a few years out.

NYM WTI forward curve as of end of August by year ($/b)

Source: Bloomberg, J.P. Morgan Commodities Research

A world consuming 95+ mbd confronts the evergreen reality of a steep cost curve. Biofuel economics continue to set the marginal price of petroleum.

2012 petroleum supply curve (US$ per barrel, y-axis; mbd, x-axis)

$200 $180 EU -Biodiesel $160 Middle East -GTL $140 North America -Ethanol $120 South America -Ultra-deep water $100 SaudiArabia - North America -Shale oil $80 Conventional China - North America -Oil sands Iraq -Conventional US -Conventional $60 Conventional $40 $20 $0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Source: Rystad, Corporate reports, DOE, IEA, OECD, RFA, USDA, J.P. Morgan Commodities Research. Note: Ultra-deep water is defined as 1500+ meters.

2017 petroleum supply curve (US$ per barrel, y-axis; mbd, x-axis)

$200 EU -Biodiesel $180 $160 South America -Shale oil $140 North America -Ethanol $120 Russia -Conventional $100 US -Conventional North America -Oil sands Iraq -Conventional North America -Shale oil $80 SaudiArabia - China - Conventional $60 Conventional $40 $20 $0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Source: Rystad, Corporate reports, DOE, IEA, OECD, RFA, USDA, J.P. Morgan Commodities Research How important is the North American energy resurgence for global energy markets and for the global economy more broadly?

Source: CAPP, Solidarity-US.org, JP Morgan Commodities Research

What is the JPM View for global oil supply, demand, and price?

2014 2015 2016 2017MR 2017SR in kbd Global oil capacity 97,553 99,941 100,893 102,386 102,386 Global oil production 92,053 92,962 94,326 94,248 94,248 Global oil demand 92,478 93,426 94,840 94,043 93,493

Balance (425) (464) (514) 205 755

Brent ($/bbl) 111 115 120 70 60 WTI ($/bbl) 103 108 113 63 53 Realized Volatility (%) 15 22 30 60 65

Source: J.P. Morgan Commodities Research A classic mistake is to confuse “capacity in the spreadsheet” with “effective capacity in the field” and then miss what this means for oil price volatility. Effective spare capacity for global liquids Realized volatility versus global petroleum upstream balance. Annual data, 1985-2013. kbd 2014 observation is ytd through July 8.

7,000 80

6,000 70

5,000 60

4,000 50

3,000 40

2,000 30

1,000 20

- 10 2011 2012 2013 2014 2015 2016 0 effective spare capacity restricted spare capacity -2% -1% 0% 1% 2% 3% Source: J.P. Morgan Commodities Research Green denotes 2014 observation. Red observations largely coincide with recessions (1990, 1991, 2008, 2009). Source: BP, ICE, J.P. Morgan Commodities Research

The stories of Leviathan and Prometheus are instructive.

Leviathan Prometheus

LEVIATHAN PROMETHEUS "THE MARKET" "THE REAL ECONOMY" ASSET CLASS 2014 SIZE (Tn) US = #1 LIQUIDS PRODUCER AND CONSUMER EQUITIES $54 US = #1 GAS PRODUCER AND CONSUMER BONDS $22 US = #1 PRODUCTS EXPORTER AND CONSUMER COMMODITIES $1 US = AWAKE AND UNBOUND

Source: BIS, British Library, Classical Wisdom

SECURITIES DIVISION

Natural Gas Winter 2013/2014 University of Colorado Burridge Conference - 2014

Prepared by Goldman Sachs Futures and Derivatives Clearing Services. In evaluating this material, you should know that it could have been previously provided to other clients and/or internal Goldman Sachs personnel, who could have already acted on it. The views or ideas expressed here are those of the desk and/or author only and are not an official view of Goldman Sachs; others at Goldman Sachs may have opinions or may express views that are contrary to those herein. This material is not independent advice and is not a product of Global Investment Research.

Polar Vortex – Temperatures

How cold did it get? • Avg temp. in Chicago from 12/1/2013 to 3/31/2014 was 22 °F (−6 °C), 10 °F (−12 °C) below average • Green Bay and Minneapolis-St.Paul average temperature of 8 °F (-13 °C) in February (3rd and 7th coldest respectively) • When accounting for wind chill on January 6th-7th (source NBC): o -16 °F in New York City o -31 °F in Chicago o -45 °F in northernmost Minnesota o -6 °F in Atlanta • Cost ~ 5bn lost revenue, 0.2% of GDP ( source Barron’s Feb 2/22/2014)

Boulder did ok

Jan 2014: Avg 35, Max 63 °F

Feb 2014: Avg 32, Max 65 °F

Mar 2014: Avg 43, Max 72 °F

Data Source: Bloomberg, Graph NOOA 2 Polar Vortex - Inventory

• Natural Gas inventory Winter 2013-2014 : lowest level of last 5 years ( < 1Trillion Cubic Feet) • Drawdown rate was also one of the fastest (steepest of last 5 years) • 2 Trillion cubic feet drawn in first 2 months 2014 ~ 58% of inventory (36% 2013; 29% 2012; 44% 2011)

3700

3200

2700

2200

1700 Billion Cubic FeetBillion Cubic

1200

700

Data Source: Bloomberg 3

Polar Vortex – Production (Lower 48)

• Cold temperatures led to weelhead freeze-offs, general operational problems • Production dipped at end of December 2013 and did not recover until end of February 2014

75.5

75

74.5

74

73.5

73 BcF/d

72.5

72

71.5

71

Data Source: Bloomberg 4 Henry Hub 1st Month Rolling Prices: 1/13 – 4/14

• After years of flat low prices ($2-$4 MMBtu), Winter 2013/14 was one of the most volatile periods ever • Prices stable through 2013 ($3.25-$4.5 MMBtu)

6.5 6.25 6 5.75 5.5 5.25 5

$/MMBtu $/MMBtu 4.75 4.5 4.25 4 3.75 3.5 3.25 3

Data Source: Bloomberg 5

Henry Hub First Month– Winter 2013/2014

• On Feb 10th 2014 Nat Gas was about $4.5 MMBtu • On Feb 21st 2014 it reached $6.15 MMBtu ~ 35% increase • By February 28th 2014 prices snapped back down to $4.5 MMBtu • Related products like Propane showed similar spikes • BusinessWeek article dated February 27, 2014 refers to it as “Carnage on the Natural Gas Market” 6.25

6

5.75

5.5

5.25

5

4.75 $/MMBtu 4.5

4.25

4

3.75

Data Source: Bloomberg 6 Henry Hub First Month Rolling Prices: Winter 2003

• Similar move in winter 2003, but that played out over a longer time frame

10.0

9.5

9.0

8.5

8.0

7.5

7.0

$/MMBtu 6.5

6.0

5.5

5.0

4.5

Data Source: Bloomberg 7

CME Henry Hub NG First Month Rolling: IM$ vs VM$ - 5 Years

• Prices were moving and margin requirements at clearinghouses almost tripled • IM levels were breached and had to be changed several times

Data Source: Bloomberg & CME 8 A further twist: spot prices at different hubs

• Spot prices in Transco Zone 6 ~ $120/MMBtu; Boston/City Gate reached $80 /MMBtu • Marcellus Shale/Utica capacity into New England (2016) might alleviate this problem • Northeast more reliant on gas fired electricity generation (33%) vs 8% in Midwest; 29% in the West 120 Cheyenne Hub Natural Gas Spot

Henry Hub Natural Gas Spot Pri 100 Natural Gas Waha Hub Spot Pric

Transco Zone 6 Non New York Na 80 Mid-Continent Natural Gas Spot

Boston/new England City Gate v 60 $/MMBtu 40

20

0

9 Data Source: Bloomberg, EIA

Volatility: Front Month ATM 12/13 - 2/14

• Volatility in front month contract ATM options moves from 34% to 80% (12/16/2013 to 2/4/2104) • By end of February volatility had moved back to ~ 34%, erasing two months of changes • Mean implied volatility of front month ATM options over previous 4 yrs ~40%

Volatility Surface December 16, 2014 Volatility Surface February 4, 2014 100 50 80 40 60 30 40 20 20 Vol % 10 Vol % 0 0 90% 90% 97.5% 97.5% 102.5% 1 MO 102.5% 2 MO 1 MO 3 MO 2 MO 6 MO 110% 1 YR 3 MO 6 MO 110% 1 YR Strike 1.5 YR 1.5 2 YR Strike 1.5 YR 1.5 2 YR

Volatility Surface February 27, 2014 50 40 30 20 10 Vol % 0 90% 97.5% 102.5% 1 MO 2 MO 3 MO 110% 6 MO 1 YR Strike 1.5 YR 1.5 2 YR Data Source: Bloomberg 10

Correlation: Gas Producers vs. Natural Gas

• Price of front month Natural Gas and an basket of gas producers showed little correlation ~ 0.21 • Equities market response was subdued indicating skepticism over Natural Gas rally (~10% increase)

170 7

165 6

5 160

4 $/MMBtu 155 3 Gas Producers Index 150 Index value $ value Index Front Month Price 2

145 1

140 0

11 Data Source: Bloomberg

Natural Gas Forward Curve

• Forward Curve reacted in the front end as expected, but prices stayed within a close band • On the longer end of the curve levels remained relatively flat (past 15 months) • Curve reinforces that surge will not have staying power for a variety of structural reasons

5.1 Fwd Crv 12/2/2013 Fwd Crv 1/2/2014 4.9 Fwd Crv 1/31/2014

4.7 Fwd Crv 2/11/2014 Fwd Crv 2/28/2014 4.5 Fwd Crv 3/31/2014

4.3 $/MMBtu

4.1

3.9

3.7

3.5 1M 3M 5M 7M 9M 11M 13M 15M 17M 19M 21M 23M

12 Data Source: Bloomberg Market Participants Positions

• Between Feb 11th and Feb 25th Money Mangers unwound approximately 5% of the spread market • This is equivalent to about 120,000 lots or 300,000,000 MMBtu, mostly of March-April Spread Clearing Producer/User Swap Dealers Managed Money Others House Long Short Long Short Spread Long Short Spread Long Short Spread 2/11/20014 &0( 38,045 56,153 25,967 8,442 25,464 75,851 34,233 88,573 8,101 61,598 39,224 ,&( 2,336,852 1,167,881 94,998 2,272,607 551,278 860,290 12,332 1,069,034 202,690 64,825 567,352 7RWDO 2,374,897 1,224,034 120,965 2,281,049 576,742 936,141 46,565 1,157,607 210,791 126,423 606,576 2/25/2014 &0( 37,542 53,617 25,808 11,686 27,628 75,116 32,629 70,473 8,144 61,693 37715 ,&( 2,340,257 1,131,212 100,251 2,420,797 551,582 969,036 14,643 967,139 200,720 69,542 608,852 7RWDO 2,377,799 1,184,829 126,059 2,432,483 579,210 1,044,152 47,272 1,037,612 208,864 131,235 646,567 DIFFERENCE &0( (503) (2,536) (159) 3,244 2,164 (735) (1,604) (18,100) 43 95 (1,510) ,&( 3,405 (36,669) 5,253 148,190 304 108,746 2,311 (101,895) (1,970) 4,717 41,500 7RWDO'LII 2,902 (39,205) 5,094 151,434 2,468 108,011 707 (119,995) (1,927) 4,812 39,991 Data Source: CFTC 13

What Happened and what’s next?

Prices spike (February 11): • Record low temperatures • Raise in demand/storage numbers falling/deliverability concerns • Traders started expressing bullish views • Hedge Fund managers, speculators and managed money bought natural gas futures, expecting prices to rise

Prices collapse (February 21): • Front month contract intraday price reaches $6.4/MMbtu • Front month contract expiry-> February 26 • Spot/Cash market declining earlier than the futures due to roll • Speculators had to abide by position limits for set by exchanges on front month contracts • Market participants started taking profits, selloff begins

Winter 2014/2105 view: • Current storage forecast going into winter 3.5 Trillion Cubic Feet < average 5 years • Farmer’s Almanac predicting another cold winter (Polar Vortex 2.0) • Draw your own conclusions! Data Source: Bloomberg, 14

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Housing and the Great Recession

October 22, 2014

Housing and the Great Recession

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Overview

1 Introduction

2 Rational Expectations?

3 House Prices and Debt

4 Origins of Credit Supply Shock

Housing and the Great Recession Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

House Price Dynamics since 19901

1Source: FHFA Housing and the Great Recession

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

House Price Boom – Potential Explanations

• From Q2 1999 to Q2 2006, cumulative real house price growth of 44% in the U.S. • Hypothesis to explain this observed boom: • Optimistic future house price expectations? Social dynamics? • Increase in credit supply? Decrease in interest rates? • If increase in credit supply, what is the root cause?

Housing and the Great Recession Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Consumer Views vs. House Prices2

2Source for opinions: Michigan Survey of National Consumers Housing and the Great Recession

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Measure of Optimistic Investors vs. House Prices3

3Source for opinions: Michigan Survey of National Consumers Housing and the Great Recession Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Optimistic Expectations/Social Dynamics?

• Can a small increase in the number of optimistic investors lead to significantly higher home prices? • Yes, if we consider the following facts: • Housing is a difficult asset to short • Existence of a large search friction in housing market – only 6% of all owner-occupied homes traded every year, compared to 120% of total market cap for NYSE stocks • Recorded prices only reflect transactions that take place • Thus, even if optimistic investors are a small share of total investors, if they represent a large share of volumes, they can drive up prices.

Housing and the Great Recession

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Household Debt since 19904

4Source: NIPA and Federal Reserve Flow of Funds Housing and the Great Recession Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

House Prices vs. Interest Rates5

5Source: Federal Reserve and Freddie Mac Housing and the Great Recession

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Large Increase in Mortgage Credit between 2000 and 2006 • How can we explain such large increase in mortgage debt during that time period? • Candidate explanation 1: a credit demand shock: • Positive productivity shocks, leading to higher wages and higher permanent incomes for households; • Increase in income prospects for households leading to higher demand for credit, incuding residential mortgages. • Candidate explanation 2: a credit supply shock: • Financial innovation, leading to better risk management abilities for mortgage originators; • Higher expected future prices, leading to loans with higher LTV ratios; • Regulatory arbitrage.

Housing and the Great Recession Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Evidence Against Credit Demand Shock6

6Source: Mian & Sufi 2009a Housing and the Great Recession

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Evidence Against Credit Demand Shock7

7Source: Mian & Sufi 2009a Housing and the Great Recession Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Evidence Against Credit Demand Shock

• Zip-codes with high percentage of subprime borrowers experienced negative income growth between 2002 and 2005 • Negative income growth zip-codes have mortgage origination growth twice as large as zip-codes with positive income growth • =⇒ Expansion of subprime mortgage lending cannot be explained by an income-based hypothesis

Housing and the Great Recession

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

A Credit Supply Shock8

8Source: Mian & Sufi 2009a Housing and the Great Recession Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

A Credit Supply Shock

• House price growth significantly stronger in subprime zip codes relative to prime zipcodes until 2006, before collapsing; • Credit growth significantly stronger in subprime zip codes during 2002 to 2005 • =⇒ A credit-induced house price boom? • Causality question: • Was credit growth driving growth in house prices? • Was growth in house prices driving credit growth?

Housing and the Great Recession

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

From Credit Supply Shock to Higher House Prices9

9Source: Mian & Sufi 2009a Housing and the Great Recession Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

From Credit Supply Shock to Higher House Prices • Can the increase in mortgage lending be caused by higher expected future house prices? • Evidence from Mian & Sufi 2009a: • Exploit local variation in credit growth and housing supply elasticity; • Focus on cities where housing supply is elastic (i.e. where land is cheap, no zoning regulations) • In those cities, house prices should closely track inflation and should not experience the type of boom observed in cities with low supply elasticities • For those cities, strong credit growth in subprime neighborhoods relative to prime neighborhoods • Thus, causality is from credit supply expansion to higher house prices. Housing and the Great Recession

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Macroeconomic Impact of Credit Supply Shock • Housing credit supply shock lead to a housing boom in 2002-2006 • How do homeowners react to an increase in house prices? • Credit constrained households would borrow against the increase in their house price in order to “relax” their budget constraint • Is the strong correlation between house price growth and household growth evidence of this channel? • As usual, difficulty is whether omitted factors (such as permanent income growth) drive both house prices and borrowing. • Carefully chosen instrument based on housing supply elasticity gives a clear picture

Housing and the Great Recession Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Elasticity of Household Debt to House Prices10

10Source: Mian & Sufi 2011 Housing and the Great Recession

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Usage of Mortgage Equity Withdrawal Proceeds

• Evidence shows that every $1 increase in home values leads to a $0.25 increase in household debt • In elasticity terms, a 1% increase in home values leads to a 0.52% increase in household debt • How did households use the proceeds from home equity extraction? • Purchase of a new and larger home? No evidence • Purchase of an investment property? No evidence • Paying down credit card balance? No evidence • Anecdotal evidence for home improvement and consumption expenditure

Housing and the Great Recession Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Macroeconomic Impact of Housing Boom and Bust11

11Source: FHFA and BEA Housing and the Great Recession

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Macroeconomic Impact of Housing Boom and Bust12

12Source: FHFA and BEA Housing and the Great Recession Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Mortgage Backed Securities Origination13

13Source: SIFMA Housing and the Great Recession

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Mortgage Backed Securities Outstanding14

14Source: SIFMA Housing and the Great Recession Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Conforming vs. Non-conforming Mortgages

• Conforming mortgages: • Loan balance below conforming limit ($417,000 currently for single-family homes) • LTV ratio under 80% • DTI ratio under 28/43 • Minimum credit score • Non-agency RMBS issuances reached close to 50% of aggregate residential MBS issuances in 2005-2006 • Largest categories of non-agency RMBS: • Subprime RMBS, with circa $600bn issuances in 2005-2006 • Alt-A RMBS, with circa $600bn issuances in 2005-2006

Housing and the Great Recession

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Originate and Distribute Business Model

• Most non-prime mortgages originated between 2002 and 2007 were securitized into alt-A and subprime RMBS; • Non-prime mortgages, once originated, were sold to Wall Street firms in the form of loan packages; • Wall-street firms securitized those loan packages into RMBS transactions; • RMBS bonds issued were sold to: • Mortgage agencies, banks, ABCP conduits, financial guaranty insurance companies and securities lenders for the triple-A risk; • ABS CDOs for the double-A through triple-B risk; • hedge funds or retained by the sponsor for the equity risk.

Housing and the Great Recession Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Subprime RMBS Example

Housing and the Great Recession

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

ABS CDO Example

Housing and the Great Recession Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Origins of Credit Supply Shock • Banks and ABCP Conduits were the largest buyers of non-conforming RMBS and CDOs between 2002 and 2007 • Via their liquidity backstops, banks were effectively guaranteeing the credit risk of assets purchased by their ABCP conduits ABCP Issuer Type Outstanding Outstanding 2000 - 2007 (Q1 2000) (Q2 2007) % Increase Multi-seller ∼ $340bn $651bn +91% Single-seller ∼ $50bn $227bn +354% Credit arbitrage ∼ $50bn $296bn +388% SIVs & Hybrids ∼ $25bn $122bn +492%

Table : ABCP Issuer Types – Growth between 2000 and 200715

15Source: Covitz, Liang and Suarez Housing and the Great Recession

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Origins of Credit Supply Shock - ABCP16

● Asset−Backed Commercial Paper Outstanding ● Financial Commercial Paper Outstanding ● Corporate Commercial Paper Outstanding ● Other Commercial Paper Outstanding $ millions 0 200000 400000 600000 800000 1000000 1200000

2002 2004 2006 2008 2010 2012 2014

Date

16Source: Federal Reserve Housing and the Great Recession Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Origins of Credit Supply Shock - ABCP17

● Asset−Backed CP as % of Total CP ● Financial CP as % of Total CP ● Corporate CP as % of Total CP ● Other CP as % of Total CP ● Total CP Outstanding % of Total CP Outstanding % of Total Total Commercial Paper Outstanding ($ millions) Commercial Paper Total 0 500000 1000000 1500000 2000000 0.0 0.2 0.4 0.6 0.8 1.0

2002 2004 2006 2008 2010 2012 2014

Date

17Source: Federal Reserve Housing and the Great Recession

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Regulatory Capital Arbitrage? • Requirement for US banks to be “well capitalized”: • Total Risk-Based Capital ≥ Risk Weighted Assets 10%; • Tier 1 (core) Capital ≥ Risk Weighted Assets 6%; • Tier 1 (core) Capital ≥ Average Total Assets 5%. • Bottom line: asset risk weights are crucial, and for structured finance bonds, they are entirely driven by credit ratings • Other US accounting and regulatory consideration • Dec 2003: FIN 46-R implemented post Enron, leading to consolidation of many ABCP conduits; • July 2004: new regulatory directive: ABCP assets exluded from capital calculations; liquidity facilities included with appropriate CCF.

Housing and the Great Recession Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Regulatory Capital Arbitrage?

• A simple regulatory capital calculation for a Triple-A rated CDO bond yielding Libor + 0.40% and held on a European bank’s balance-sheet • 7% Risk Weighted Asset under Basel II • 8% Risk Based Capital Ratio • =⇒ 0.56% equity capital requirement and 71% ROE • Now assume the bond is held onto the bank’s ABCP conduit • 20% credit conversion factor for liquidity facilities • 7% Risk Weighted Asset under Basel II • 8% Risk Based Capital Ratio • =⇒ 0.112% equity capital requirement and 355% ROE!

Housing and the Great Recession

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Rating Agencies – What went wrong? • Approx $80 of triple-A bonds issued out of $100 notional of subprime residential mortgages • Approx $90 of triple-A bonds issued out of $100 notional of alt-A residential mortgages • =⇒ Direct issuance of approx. $2tn of triple-A RMBS bonds for 2003-2007 • Rating methodology: • Expected losses for Moody’s, first dollar of loss for S&P • Optimistic house price appreciation assumptions • Limited historical data on subprime RMBS performance • Failure to appropriately capture correlation structure of RMBS bonds when rating CDOs

Housing and the Great Recession Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Mortgage Related Losses – 12/2007 - 08/200818

Firm Loss ($bn) Firm Loss ($bn) Citigroup 55.1 Barclays 9.1 Merrill Lynch 51.8 Lehman Brothers 8.2 UBS 44.2 Credit Agricole 8.0 HSBC 27.4 Fortis 7.4 Wachovia 22.5 HBOS 7.1 Bank of America 21.2 Societe Generale 6.8 IKB Deutsche 15.3 Bayerische Landesbank 6.4 Royal Bank of Scotland 14.9 Canadian Imperial (CIBC) 6.3 Washington Mutual 14.8 Mizuho Financial Group 5.9 Morgan Stanley 14.4 ING Groep 5.8 JPMorgan Chase 14.3 National City 5.4 Deutsche Bank 10.8 Credit Suisse 10.5 Top 25 Subtotal 403.6 Wells Fargo 10.0 Total 501.1

18Source: Bloomberg Housing and the Great Recession

Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Banks’ and Broker Dealers’ Internal Risk Management

• Accounting: fair-value vs. available-for-sale vs. hold-to-maturity • EITF 02-03, level 3 assets and mark-to-model • Risk management infrastructure • Counterparty risk management

Housing and the Great Recession Introduction Rational Expectations? House Prices and Debt Origins of Credit Supply Shock

Quotes from the FCIC Report

• As everybody in any business knows, if inventory is growing, that means you’re not pricing it correctly (Richard Bookstaber, Citigroup) • The risk of default on [AIGs] portfolio has been effectively removed and as a result from a risk management perspective, there are no substantive economic risks in the portfolio and as a result the fair value of the liability stream on these positions from a risk management perspective could reasonably be considered to be zero. (PwC, AIG’s auditor in 2007-2008)

Housing and the Great Recession

BUILDING TOMORROW™

The State of Credit Markets Macro Credit Research Tantrum 2.0 University of Colorado Boulder, October 2014 Alberto Gallo, CFA Head of European Macro Credit Research +44 (0) 20 7085 5736 [email protected]

Lee Tyrrell-Hendry Macro Credit Analyst +44 (0) 20 7085 9462 [email protected] Tao Pan Macro Credit Analyst +44 (0) 20 7678 3122 [email protected]

Mateja Popovic Macro Credit Analyst +44 (0) 20 7085 9698 [email protected]

Rajarshi Malaviya Gaurav Chhapia Chanchal Beriwal

Produced by The Royal Bank of Scotland plc. In the UK, the Royal Bank of Scotland plc is authorised by the Prudential Regulation Authority and regulated by the Financial Conduct rbs.com/cib Authority and the Prudential Regulation Authority.

The ECB plan, and what else it needs to work

QE

Source: Google. No animals were harmed during the making of this slide 2 The problems: Deleveraging and Deflation

$tn US GDP 70 US credit market debt European credit market debt

Financial crisis 60 Competition & credit control Big Bang (UK) G las s-Steagall A ct repealed Quantitative introduced / Bretton Woods easing bre ak s down

50

40 ?

30

You are here 20

10

0 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12 14

Source: RBS Credit Strategy, ECB, FRED, Bloomberg

3

The credit cycle: Periphery lagging even more behind Core, US

A cycle of four phases: Bubble Crunch

1. Crunch GDPĻĻ, SĻĻ, DĹĹ, M&AĻĻ 2. Stabilisation GDPĻ, SĻ, D ĻĻ, M&A Ļ 3. Re-leveraging GDPĹ, SĹ, D ~, M&A Ĺ 4. Bubble GDPĹĹ, SĹĹ, D ~, M&A ĹĹ

GDP = real growth S = inflation D = default rates M&A = M&A activity Re-leveraging Stabilisation Source: RBS Credit Strategy, Bloomberg

4 Deleveraging has stabilised Eurozone bank balance sheets, €tn

0. 0 +0.3 -0.5

-1.0 -0 .6 -0.3 -0.2 -1.5 -0.6 +0. +0.2 0. 0 +0.1 -2.0 -0 .6 -0 .1 -2.5 Ugly -0.4 deleveraging -3.0 -0.5 -0.3 Stabilisation -3.5 -0.2 +0.2 -0.1-0.03 +0.4 +0.13 +0.2 -0.02 +0.2 -4.0 -3.75 -0.1 -4.5 -0 .2 -0.1 -0.9 -5.0 Jun-12 Dec-12 Jun-13 Dec-13 Jun-14

Source: ECB, RBS Credit Strategy 5

Deflation risk is getting serious Euro area vs Japan CPI

5%

4% Japan CPI (peak = Nov 1990) Euro area CPI (peak = Nov 2011)

3%

2%

1%

0%

-1% Peak 1y 2y 3y 4y 5y

Source: Bloomberg, RBS Credit Strategy, The Economist 6 The economics of a TLTRO loan Example: a local core bank

35 Loan yield = 3% TLTRO cost = 0.15% SME loan RWA is 75% 30 (8% capital charge) 2% default rate, 25 40% recovery

20 Normal funding Savings cost = 1% = 0.85% 15 0.5% cost 0. 7% RoA 10 Based on a €1,000 loan loan a €1,000 on Based

5

0 Gross interest Normal TLTRO Cost of capital Cost of Fixed costs Net economic income funding costs savings on loans provisions profit

Source: ECB, RBS Credit Strategy estimates. Assuming 8% cost of capital

7

The economics of a TLTRO loan Example: a local periphery bank

50 Loan yield = 4. 5% TLTRO cost = 0.15% 45 4.5% default rate, 40 40% recovery 35 SME loan RW A is 75% 30 (8 % ca pit al c ha rge ) 25 Normal funding Savings 20 cost = 2% = 1.85%

15 0.5% cost 10 0. 4% RoA

B ased on a €1,000 loan5

0 Gross interest Normal TLTRO Cost of capital Cost of Fixed costs Net economic income funding cos ts savings on loans provisions profit

Source: ECB, RBS Credit Strategy estimates. Assuming 14% cost of capital

8 The first arrow: QE and ABS 2.0 ABS can help banks improve asset encumbrance, capital ratios and leverage ratios

Indicative balance sheet of a bank using covered bonds (left) vs ABS (right) to fund an increase in lending

On balance sheet Off balance sheet

100% Retained loans Cov ered bonds €50bn of new loans 90%

80% New loans Covered bonds 70% Loans Deposits 60% 50% 40% 16% - c api ta l ra ti o - 30.5% Loans Deposits 4% - leverage ratio - 7. 6% Repo 30% 50% - asset en cum brance - 5% 20% Senior Repo Securities 10% Securit ies Senior Sub Sub Equity (4%) Equity (7.6%) 0% Asset Liabilities Assets Liabilities

Covered bond funding ABS funding

Source: RBS Credit Strategy.

9

The ABS market: Still small and inhomogeneous

The securitisation market is well developed in the core The investor base is smaller and less diversified now Securities placed by banks, €bn ABS investor base, mid-2007 vs 2013

250 100% CDOs & HFs Others RMBS CBs & supras CMBS 90% Banks Insurers 200 CLO CDO 80% ABS 70% Real money Banks

150 60% Conduits 50%

100 40% MM funds 30% Real money & HFs 50 20%

10% SIVs

0 0% UK NL ES DE IT IE PT FR GR Mid-2007 2013

Source: RBS Credit Strategy, RBS ABS Strategy Source: RBS Credit Strategy, RBS ABS Strategy

10 The second arrow: Fiscal stimulus The EIB is boosting its standard lending programmes and exploring new ones

The EIB lends around €60bn every year EIB lending is weighted towards the periphery and CEE EIB lending inside the EU by policy areas, €bn Outstanding loans by regions, % total

2013-15 Policy objectives 2012 2013 2014 2015 average

Support for SMEs 10.7 13.6 13.3 13.3 13.4 Non-EU, 11%

Knowledge economy 8.9 11.1 10.6 10.6 10.8 Core EMU, 28% Transport 9.3 12.5 11.8 11.8 12.0

Energy 7.2 12.5 12.5 12.5 12.5 Other Europe, 26% €522bn of loans

Urban and health 2.3 2.9 2.7 2.7 2.8

Environment 6.1 9.5 9.1 9.1 9.2

Total 44.5 62.1 60.0 60.0 60.7 GIIPS, 37% Source: RBS Credit Strategy, EIB

Source: RBS Credit Strategy, EIB The EIB can help, but more EU investment is needed also

11

The third arrow: Reforms – Italy

Letta government:

ƒ €3bn infrastructure projects, €5bn increase in SME financing (MEF) ƒ €2bn SME lending programme through Cassa Depositi e Prestiti ƒ €40bn repayment of government arrears (€22bn executed) ƒ Moratorium of SME debt Category Rank (out of 31) ƒ IVASS allows insurance companies to invest in mini bonds and Protecting investors 16 securitisations Resolving insolvency 19 Starting a business 21 Renzi government:

Registering property 25 ƒ €68bn repayment of government arrears Ease of imports/ exports 26 ƒ Bank of Italy extended Eurosystem-eligible collateral to SME loans Construction permits 27 ƒ Renewal of SME debt moratorium Getting credit 28 ƒ Senate reform Getting electricity 29 ƒ “Unblock Italy” measures to accelerate infrastructure investing, SME financing. Opening of bank activities to insurers, credit mutual funds Paying taxes 31 ƒ Planning: overhaul of legal system, labour reforms, Bank of Italy and Enforcing contracts 31 Assopopolari to reform the “one person, one vote” model among Overall 29 popolari banks Source: RBS Credit Strategy, World Bank

12 The third arrow: Reforms – France

Ayrault cabinet:

ƒ Cut public spending by €19bn in 2014 ƒ Increased work duration requirement to 43 years, from 41.5 ƒ Expanded subsidised work programmes, with a target of 340k new hires in 2014, alongside apprenticeships and work-study Category Rank (out of 31) programmes Enforcing contracts 7 ƒ Opened up some professions and products to greater competition, Starting a business 15 like car insurance, prescription glasses and veterinary services Getting electricity 15 Valls cabinet: Paying taxes 17 Getting credit 20 ƒ Plan to cut public spending €50bn over 2015-17 and cut business Trading across borders 20 payroll taxes by €40bn Protecting investors 22 ƒ Plan to ease regulatory thresholds to lower red-tape for companies with more than 10, 20 and 50 employees Construction permits 22 ƒ Plan to restructure the number of regions to 1/2 Resolving insolvency 26 Registering property 28 Overall 24 Source: RBS Credit Strategy, World Bank

13

All hands on deck to close the gap

0

-100 +265 -109 -200

-300 +50

-400 +60 +6 0 -500 +50

-600 -594 -700 Decline in NFC A nnual growth New CLOs and Fresh EIB Non-bank TLTRO Net financing loans from in HY bond securitisations lending lenders gap peak market

Source: ECB, RBS Credit Strategy estimates

14 Towards a new financial world Restructuring the banking system

Source: Google. No banks were harmed during the making of this slide 15

What makes a banking system safe?

60% Higher vulnerability to financial stress

40% ress t s

l 20% a i

nanc 0% fi

ith -20% on w ti a l -40%

orre Lower vulnerability to financial stress C -60% Capitalisation Interest Liquid assets Wholesale Concentration New Intercon- Size margins funding products nectedness

Source: IMF, RBS Credit Strategy 16 Banking systems: Euro area vs US Euro area US 6,790 - Number of banks - 6,812

3.1x - Bank assets/GDP - 0.8x

37 - Number of branches - 35 per 100,000 adults

22% - Bonds/total debt - 52%

16% - Mkt share of top 5 banks - 33%

€1,041bn -NPLs-$200bn

10.9% -NPLs/GDP -1.2% Source: RBS Credit Strategy, Bank of Italy, ECB, IMF, World Bank, FRED 17

Current leverage ratio requirements are too low 3% of capital will be insufficient to cover asset losses in a crisis

Bank losses in previous crises European banks are generally between 3-4% capital Losses as % of loans and assets Leverage ratio, Q4 2013

20% Losses as % initial loans Losses as % initial assets

6% 15% 5% Leverage ratio

4% 10%

3%

5% 2%

1%

0% 0% ML DB CS BoI AIB B&B SNS UBS KBC BNP ABN UBS NBG Dexia Anglo Wach Nrock Amag BBVA Intesa Monte Lloyds CMZB HBOS WaMu Bank ia Nordea Danske Barclays Unicredit Soc Gen Soc ING Bank Rabobank Santander Credit AgCredit Group

Source: RBS Credit Strategy, Bloomberg, Company filings Source: RBS Credit Strategy, Company filings. Using our estimates for Intesa and UniCredit We estimate that banks need to reach a leverage ratio of 5.8% to insulate sovereigns from the cost of a crisis

18 Bank capital is near a 200-year low Book value of equity / total assets for US banks

70%

60%

50%

40%

30%

20%

10%

0% 1834 1844 1854 1864 1874 1884 1894 1904 1914 1924 1934 1944 1954 1964 1974 1984 1994 2004

Source: RBS Credit Strategy, FDIC, Historical Statistics of America 19

Capitalism without capital Large banks have optimised risk weights, but absolute capital levels remain low

RWA % total 100% 90% 80% 70%

60% BBVA 50% UniCredit Intesa Santander HSBC 40% Rabo ING Lloyds BPCE Barclays BNP 30% CS LBBW ABN Danske Credit Ag 20% UBS Soc Gen Deka Nwide SEB DZ Nordea DB SH Natixis 10% Pohjola 0% 0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000 Balance sheet size, EUR bn

Source: RBS Credit Strategy, Bloomberg 20 Too big to fail has not gone away Bank asset size / GDP

200%

180% 160% 140% 120%

100% 80% 60% 40% 20%

0% CS Citi GS DB MS UBS BES RZB JPM BAC BNP SEB KBC HSBC D anske SocGen Barclays Unicredit Credit Ag ING Bank ING Rabobank Santander

Source: RBS Credit Strategy, Bloomberg, company filings 21

Costs are still high Number of branches per 1,000 people, Italy

80

70

60

50

40

30

20

10

0 Museums High schools Secondary Primary Pharmacies K indergart ens H otels B ank schools schools branches

Source: RBS Credit Strategy, OECD, Istat 22 Interconnectedness: Cross-holdings in Europe’s banking system

Italy 77.7% Fondo 4.5% 2.57% Strategico 9.42% 100% Qatar Iberdrola Toro Generali

1.3% 0.07% 9.88% 1.4% 12.2% Abu Dhabi Libya Veolia Intesa 6.43% 2.6% 5% 6.44% Monte BlackRock 4.9% 8.7% Groupama Mediobanca UniCredit

0.10% Germany 2.45% France 2.51% 3.4% 2.27% SocGen Credit Agricole Mediolanum 17% Allianz 0.52% 2.28% Portugal AXA 0.52% 0.01% Telecom 0.34% Natixis AM POP 0.1% Deutsche Bank Commerzbank 0.16% BBVA 0.6% 1.21% Caixa Geral 3.65% 0.19% 4.92% 2.8% 5.75% 1.7% Credit 4% 3.15% 0.22% UBS CRH BNP Santander Suisse Caixa Telefonica Holdings 3.26% 6.4% 5.6% 2.81% 1.18% 0.15% 0.14% 34.6% 11.6% Norway Singapore Gas Natural Caixabank Repsol Bankinter State Street

Source: RBS Credit Strategy, Bloomberg, company filings. Red = bank, Grey = sovereign, Blue = other 23

Stress test: Mid-sized banks vulnerable to rising NPLs and bond spreads Bank CET1% after estimated NPL shocks and widening of sovereign yields over 3 years under EBA adverse scenario

24% +++ + = Initial capital CT1 loss on non-periphery sovereign widening (after earnings and dividends) CT1 loss on periphery sovereign widening (after earnings and dividends) 22% + = Capital after the exerc ise CT1 loss on NPL rise, 50% coverage ratio (after earnings and dividends) 20% Earnings (70% of 3y PPI net of cash dividends) CT1 capital after stress, before earnings 18% at risk 16% 14% 12%

10% 8% 6% 4% 2% 0% DB AIB BPI SI D RBI UBI NLB RZB IKB* RLB SEB SHB OVB KBC HSH BCP BNP BMN BKIR Erste WGZ RNW Jyske NBG* ABLV Intesa BPCE BCEE BBVA BPER Iccrea LBWF CMZB CXGD Lloyds HSBC Aareal Carige LBBW NKBM Alpha* Bankia Bawag Belfius CEISS CreVal Helaba Veneto Pohjola Unicaja Nordea Popular Danske Ibercaja Argenta SocGen CredEm Nykredit Piraeus* Barclays NordLB* DZ BankDZ Sabadell Sydbank DNB Nor DNB Popolare UniCredit ApoB ank Bankinter ING Bank ING Liberbank BayernLB Catalunya BP Milano Santander Rabobank BoValletta Bank SNS Crédit Mut Crédit Swedbank DekaBank Caixabank Kutxabank Eurobank* Perm. TSB Perm. France BPI BP Sondrio BP Berlin Land BP Vicenza BP NCG Banco C'mar/ BCC ABN AMRO ABN Me d iob an ca Cred Ag Ag Cred Grp Bk of of Cyprus Bk Munich Hy po Monte Paschi* Hellenic Bank* Hellenic Stronger Weaker

Source: RBS Credit Strategy, company filings, Bloomberg, EBA. Sovereign holdings data is from the EBA, and is as of June 2013. Notes: We use 2014 Q2 results for the banks or the most recent filings available. We take into account measures to improve capital ratios post 31 December 2013. To translate the EBA’s adverse scenario into an NPL shock, we regress the change in NPLs over change in real GDP, unemployment and house prices during the crisis and adapt it to the EBA assumptions. If sovereign holdings data from the EBA is unavailable we use the national average. If EBA data is not available for any bank from a country, we use the periphery or core average, depending on the bank’s location. We stress the AfS and HfT portfolios of banks only, proxied as 70% of gross direct long positions. 3y PPI is 70% of average pre-provision income (after taking out sovereign carry) in 2011-13 less 70% of average cash dividends paid over 2011-13, as % of RWAs. Transitional CET1 ratios are used for all except SID, Catalunya Banc, Landeskreditbank Baden-Wurttemberg-Forderbank, Iccrea Holding S.p.A. and Caja Espana de Inversiones where the latest reports are based on Basel 2/2.5 standards. IKB, Belfius and WGZ Bank Group have negative earnings so we reduce remaining CET1 capital to adjust for this. The EBA will use 8% and 5.5% capital threshold for its baseline and adverse scenario of the stress tests. The above sample has banks which are included in the EBA’s sample list: the EBA list has roughly 26 more banks than our current sample. The weak German banks in our test have reasonable initial capital levels, but lose out due to high holdings of bunds. *These banks have EC-approved restructuring plans in place. The ECB stress test results will be adjusted to reflect24 this, though we have not done so above given limited information. Bail-in: Some capital structures still vulnerable to 8% threshold Capital structure of banks, black line = 8% of total liabilities*

60%

50%

40%

30%

20%

10%

0% DB IKB SID UBI LBB NLB SHB BCP BNP BFA BES SEB KBC NBG HSH NWB Tat ra BKIR Erste WGZ Alpha Iccrea Aareal BCEE BBVA Monte Intesa Bankia NKBM CMZB Natixis Lloyd s CXGD LBWF Cari ge LBBW HSBC Bawag Veneto Belfius Helaba BPEIM Pohj ola Abanka Norde a Sondrio Ibercaja Danske Argen ta Pi raeus Popular Nord LB SocGen Sabad ell Barclay s H ypo RE Un icredi t Cred Em Cred DZ Bank DZ Popolare ApoBan k Bankinter DN B N or ING Bank ING Eurob ank Libe rbank Baye rnLB Rabo bank Raiffeisen Slovenská Catalu nya BP Milano BP C Espan . a Cariparma Allied Irish Allied San tan der C aixa bank DekaBank Bank SNS Kutxaban k Sw edba nk Bd MarcheBd Na tio nwi de Banco BPI Perm. TSBPerm. BPI France Valtellinese Grp. BPCE Cred Ag SA M. Nostrum M. BP Vicenza Me diob anca R entenb ank NC Ban G co ABN AM RO BkCyprus of Munich HypoMunich Crédi Mutuel t H ell enic Ban k

More equity and sub Banq ue In Lux t Less equity and sub Eq uity (min. of market ca p an d book val ue) Subordi nated Senior uns ecured Dep osits Secured

Source: RBS Credit Strategy, Bloomberg, company filings. Note banks can bail-in 8% of total liabilities or under special circumstances 20% of RWAs (where the ex-ante contributions were 3%) before accessing national resolution funds. For banks for which no market cap is available, we have approximated taking the avg price-to-book across comparable banks. For Greek banks we have used market cap, as the book value was unavailable. *calculated as a proportion of the sum of equity (market cap), sub, senior, deposits and covered bonds. The calculation excludes derivatives as the final treatment of the valuation of derivatives for the purpose of bail-in has not been determined yet. 25

Capital: Quality of capital will count more in the future National regulators could impose equity thresholds or limit coco capital

Spanish banks have relatively smaller buffer of sub A large part of Spanish bank capital is made up of DTAs Capital ratios, % RWAs DTAs, % of Core Tier 1 equity (Spanish banks in red)

80%

20% 70% CT1 (%) Gov t Support (% ) DTA (%) T1 (%) T2 (%) 60%

15% 50%

40% 10% 30%

20% 5% 10%

0% 0% DB

BNP BCP -10% Caixa Intes a Monte BBVA Lloyds CXGD Bankia Group Popular SocG en Barclays Unicredit Sabadell Credit Ag Credit BPI Sant ander Banco BPI Banco KBC BCP BNP RBS SEB SHB DNB Erst e Rabo Caixa Dexia Intesa BBVA Monte CXGD Lloy ds CMZB HSBC Bankia Nordea Popular Danske Commerzbank SocG en Barclay s Unicredit S abadell Bankinter C. Ag Grp Santander

Source: RBS Credit Strategy, Bloomberg, company filings Source: RBS Credit Strategy, Bloomberg, company filings DTAs make up a high proportion of Spanish bank capital

26 Assets: Bad loans continue to rise in the periphery, stable in core Europe NPL ratios are increasing in Greece, Ireland, Italy, Portugal and Spain

Periphery NPL ratios are still rising NPLs remain low in core Europe NPL ratio, % of total loans* NPL ratio, % of total loans

8 35 UK Greece Italy 7 France 30 Spain Portugal Germany 6 Belgium Ireland Netherlands 25 5

20 4

15 3

10 2

5 1

0 0 07 08 09 10 11 12 13 14 07 08 09 10 11 12 13 14

Source: RBS Credit Strategy, Bloomberg, Banco de Espana Source: RBS Credit Strategy, Bloomberg Increasing bad loans will continue to hurt peripheral banks and erode capital

*The recent decline in Spanish NPLs is partly due to SAREB, which has taken over €50bn of assets off bank balance sheets. For Ireland, we use mortgage arrears/ total loans, which will be lower than total NPL levels 27

Assets: The sovereign-bank nexus is getting stronger Sovereign exposure is a multiple of equity

Mid-tier banks have the most periphery sovereign debt Italian, Spanish and Portuguese increase their holdings Bank sovereign holdings at Q2 2013, % total assets Aggregate MFI holdings of sovereigns, % of total assets

18% 12% IE ES IT PT GR Spain Portugal 15% Italy 10% Ireland 12% Greece 8% 9%

6% 6%

3% 4%

0% 2% UBI BPI BCP BES Dexia BBVA Intesa

Monte Year end Bankia

Popular 0% Unicredit Popolare Santander CaixaBank 2008 2009 2010 2011 2012 2013 Caixa Geral Caixa

Source: RBS Credit Strategy estimates, EBA Source: RBS Credit Strategy, ECB

Peripheral banks are increasingly exposed to sovereign risk

28 Reforms and consolidation pay off Banks in pro-reform countries trade at better P/B multiples and at lower spreads

Comprehensive Min ima l Price-to-book ratio CDS spreads (RHS) restructuring restruc- 1.4x with bad banks turing 300

1.2x 250

1.0x 200 0.8x 150 0.6x 100 0.4x

50 0.2x

0.0x 0 Ireland Spain US Portugal UK Eurozone France Germany Italy

Source: ECB, RBS Credit Strategy estimates

29

Facilitating NPL disposals: The foreclosure process

It takes nearly 5 years to foreclose in Italy A lengthy judicial process discourages investment Typical duration of a foreclosure procedure, years IRR decreases with longer foreclosure period

5 IRR 50%

4 40%

3 Spain 30% Germany France 2 Portugal Greece 20% 1 Italy 10% 0

Italy 0% Spain France Austria Greece Belgium

Portugal 12345 Germany

Netherlands Years to foreclose Luxembourg

Source: RBS Credit Strategy, ECB Source: RBS Credit Strategy; IRRs are calculated based on a hypothetical bad loan investment, assuming €100m face value with 50% hair-cut for purchase price and 70% recovery rate

The faster and more straightforward is the bankruptcy process, the easier it is for banks to dispose of NPLs

30 Long-term solutions: Disintermediation Regulators are studying new ways to get credit to the economy

ƒ Growth in ABS/securitisations. The ECB could launch Growth in HY bonds could offset falling bank lending an ABS support programme, together with EC effort to Size of European high yield market, €bn market value guarantee/support SME funding ƒ Growth in bond markets. The €HY market has almost 350 CCC tripled since 2009. Twenty issuers made their HY debut in B 2013. Finnish, Dutch and Italian mini-bond markets are 300 BB also expanding ƒ Growth in non-bank lending. Insurance companies, 250 peer-to-peer lending can plug some of the lending gap 200

150

100

50

0 2000 2002 2004 2006 2008 2010 2012 2014

Source: RBS Credit Strategy, ML Europe must fix its banks as well as promoting non-bank lending

31

Exit risk: taking out the punch bowl in US, UK “Now, back to Macro. What is your exit strategy? The players won't be in on the scam, so they'll all think it's their lucky night, but you'll never get them out of there with their winnings, they'll gamble it all back. That's Vegas and that's your problem.” Roman Nagel, Ocean’s Thirteen

32 Central banks still control the bond market The PBoC and the BoJ are keeping up stimulus even as the Fed pulls back

Central banks are still expanding their balance sheets They are big holders of sovereigns, pushing down yields Total central bank assets, $tn Central banks’ holdings of government securities, % total

16 PBoC Fed ECB BoJ BoE 30%

14 25% 12 20% 10

8 15%

6 10% 4

5% 2

0 0% 2007 2008 2009 2010 2011 2012 2013 2014 Q3 BOE BoJ PBOC FED Euro System

Source: RBS Credit Strategy, Bloomberg, Federal Reserve, ECB, Bank of Japan, Bank of England Source: RBS Credit Strategy, Bloomberg, Federal Reserve, ECB, Bank of Japan, Bank of England

QE can cause distortions in asset prices, investor behaviour and wealth distribution

33

US labour markets back to (a different) normal

The market is not pricing the FOMC’s own estimated pace of tightening

4%

Ma r ke t 3% underpricing

2%

1% Futures-implied Fed Funds Primary dealer median forecast FOMC median forecast 0% Jun 14 Dec 14 Jun 15 Dec 15 Jun 16 Dec 16 Jun 17 Dec 17

Source: RBS Credit Strategy, BLS Source: RBS Credit Strategy, Bloomberg, FOMC, Federal Reserve Bank of New York 34 Banks have turned the corner and housing is rebounding Banks are starting to lend to companies and households

Mortgage rates have fallen, spurring home sales House prices are rising again as inventories fall 30y mortgage rate, %; refinancing applications, thousands US house prices, Dec 1999 = 100; inventories, millions

9 30-year mortgage rate Refinancing applications 12,000 18 0 House Prices Housing Inventory 4. 0

8 17 0 3. 5 10,000 7 16 0 3. 0 15 0 6 8, 000 2. 5 14 0 5 6, 000 13 0 2. 0 4 12 0 1. 5 3 4, 000 11 0 2 1. 0 10 0 2, 000 1 0. 5 90

0 0 80 0. 0 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14

Source: RBS Credit Strategy, Bloomberg Source: RBS Credit Strategy, Bloomberg

35

Divided we stand? How much can inequality rise? QE has collateral effects in society, markets and behaviour

The top 1% now earns around 20%. Bubble risk is rising as M&A volumes rise. Irrational exuberance is back

60% 2,500 Difference 1774 1860 2010 US

50% Europe 2,000

40% 1,500

30%

1,000 20%

500 10%

0% 0 Top 1% Top 5% Top 10% 02 04 06 08 10 12 14

“I can only say: I'm sorry, America. As a former Federal Reserve official, I was responsible for executing the centerpiece program of the Fed's first plunge into the bond-buying experiment known as quantitative easing. The central bank continues to spin QE as a tool for helping Main Street. But I've come to recognize the program for what it really is: the greatest backdoor Wall Street bailout of all time.”

Andrew Huszar, formerly manager of the Federal Reserve’s mortgage-backed purchase program, Confession of a Quantitative Easer, WSJ November 11, 2013

36 The exit could be painful Total returns during the taper tantrum (22 May – 29 August 2013) € Fin T1 € Fin € 1-3yr € HY 500 S&P Spain € 3-5yr € IG Fins Health GoodsCapital Cons Non-cyc Materials € IG Italy CycCons Industrials Tech Utilities Energy ETF BKLN € 5-7yr LT2 € Fin Telcos Xover ETF JNKE $ HY CDX HY € 7-10yr UC00 $ IG HYG ETF ETF JNK EM CDX Portugal LQ D E TF IG$ EM HY $ EM SOV EM $ ETF EMB ETF EMHY ETF EMLC Greece 0%

Indices -4 % Sector s Per iphery ETFs -8 % “There is always a temptation for the central bank to speak in a whisper, because anything that gets said reverberates so loudly in markets. But the softer it talks, the -12% more the market leans in to hear better and, thus, the more the whisper gets amplified. So efforts to overly manage the market volatility associated with our -16% communications may ultimately be self-defeating.” Jeremy C. Stein, Federal Reserve Governor, May 6, 2014

Source: RBS Credit Strategy, Bloomberg 37

The UK case: From safe haven to leverage trap The longer rates stay low, the more households lever up, the harder it is to exit

Consumers suffer: Inflation is eroding real wages Change in total household expenditure, 2007-2011 Weekly earnings growth vs CPI inflation, % UK consumers spending less for discretionary items

10%

6% Inflation erodes income 5% 5% 0% 4% -5% 3% -10% 2% -15% 1% -20% 0% -25% -1 %

-2 % Food Health

G rowt h in real earnings Alcohol, footwear goods & Transport culture tobacco & tobacco Education -3 % hotels Clothing & power Household

CPI YoY Other items Recreation &

-4 % W eekly earnings ex. bonus 3m/YoY & Restaurants Housing, & fuel Communication 2 001 2 003 20 05 2007 20 09 201 1 201 3

Source: RBS Credit Strategy, Bloomberg Source: RBS Credit Strategy, Office of National Statistics. Housing exp excl mortgage payments "The extent to which house prices and the household debt burden appear sustainable, or vulnerable to correction, will depend on how likely it is that long-term interest rates remain near their current low levels“

Bank of England Financial Stability Report, November 2013

38 The Pruman Show: Macro-pru and microwave risk in the UK Microwave risk can turn bondholders into popcorn

BoE: loosest policy in 320 years A London bubble UK Bank of England Bank Rate, % Change in house prices since 2007

18% UK Bank Rate 16%

14%

12%

10%

8%

6%

4%

2%

0% 1700 1750 1800 1850 1900 1950 2000

Source: RBS Credit Strategy, Bank of England Source: RBS Credit Strategy, HM Land Registry Low rates and QE bring collateral effects like: rising asset prices and rising income inequality

39

Risk is rising in UK mortgage markets Mortgage tenors for first-time buyers have risen with more borrowers with high LTI

Mortgage tenors for first-time buyers have risen More borrowers with high mortgage debt / income ratios Mean mortgage tenor for first-time buyers (%) Distribution of mortgage debt to income ratios

Years LTI > 4.5 4 < LTI < 4.5 3 < LTI < 4 30% 32 2 < LTI < 3 1 < LTI < 2 25% 30

20% 28

15% 26

10% 24

22 5%

20 0% 05 06 07 08 09 10 11 12 13 14 <1 1-2 2-3 3-4 4-5 >5

Source: RBS Credit Strategy, Bank of England Source: RBS Credit Strategy, Bank of England

Households with high level of debt may be hurt when the BoE hikes rates

40 UK consumers have eaten too much cake Britain Higher base rates will squeeze spending

Disposable income (£) 24,000

22,000 We are here If Bank Rate goes to 2% disposable 20,000 income would fall by £2,000 (-9%)

18,000 80% LTV 16,000 But for borrowers with a 95% mortgage the decline is (-11%) 14,000 £2,400 95% LTV

12,000 0% 1% 2% 3% 4% 5% Bank Rate

Source: RBS Credit Strategy, ONS, Bank of England. Assuming house price to income of 4.6x for the 80% LTV mortgage and of 5.2x for the 95% LTV. 41

Towards a new financial world The new geography of bond markets

Source: The Economist 42 The US financial circuit

Public sector Government (19.9tn) Money Market Funds ($2.6tn)

Assets USTs USTs Future MM units tax Federal Reserve ($4.4tn) Banks ($14.7tn) Prime claims financial Benefits Reserves Currency assets payable USTs Deposits Loans

Reserves MBS Bonds Securities Equity

Households ($95.5tn) Corporates ($35tn) Mutual Funds ($12.8tn)

Property Loans & Financial Loans Bonds & durable mortgages assets goods Bonds Units Non- Financial Net Equities financial assets wealth Equity assets

Real economy Financial system

Equity Bonds and loans Short-term

Source: RBS Credit Strategy, Federal Reserve, ICI 43

Long credit has become a crowded retail trade Fund and household holdings of corporate bonds (% of corp. bond market)

35% Fund ownership share Household ownership share 30%

25%

20%

15 %

10 %

5%

0% 1945 1951 1957 1963 1969 1975 1981 1987 1993 1999 2005 2011

“There are three ways to make a living in this business: be first, be smarter, or cheat” John Tuld, Margin Call (2011)

Source: RBS Credit Strategy, Federal Reserve 44 Asset managers face pressure as fee % of yield increases Some respond with lower fees, others hunt for more yield

10Y UST vs Asset Manager fees Asset Manager fees as a % of 10Y UST yield

18% 10Y UST Asset Manager fees 40% 16% 35% 14% 30% 12% 25% 10% 20% 8% 15 % 6% 10 % 4% 5% 2% 0% 0% 62 65 68 71 74 77 80 83 86 89 92 95 98 01 04 07 10 13 62 65 68 71 74 77 80 83 86 89 92 95 98 01 04 07 10 13

Source: RBS Credit Strategy, FRED Source: RBS Credit Strategy, FRED

45

High yield investors can get nervous

400 80

350 Equity 70 Corp IG 300 60 Corp HY (RHS) 250 Loans (RHS) 50 Taper Tantrum 200 40

150 Yellen on HY 30

100 20

50 10 Euro debt crisis 0 0

-50 -10 Jun 2010 Dec 2010 Jun 2011 Dec 2011 Jun 2012 Dec 2012 Jun 2013 Dec 2013 Jun 2014

Source: RBS Credit Strategy, AMG US cumulative fund flows 46 Dealer inventories are near record lows US corporate bond dealer inventories, $bn.

250

IG + HY CP + RMBS + CMBS 200 IG HY CP RMBS CMBS

150

100

50

0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Source: RBS Credit Strategy, Federal Reserve Bank of New York, SIFMA, MarketAxess. Note: because corporate bond dealer inventories are not broken down prior to 2013, we have assumed IG and HY inventories are in the same proportion as the average from 2013-present.

47

RBS Liquid-o-Meter: Liquidity in credit has declined 70% since crisis Trading liquidity in US credit markets continues to decline, but is improving in Treasury markets.

100 = Dec 2006 US Treasuries US corporate bonds 160

140

120

100

80

60

40

20

0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Source: RBS Credit Strategy, Bloomberg, SIFMA, MarketAxess. 48 Liquidity survey: What are investors thinking? 84% of respondents think lack of liquidity is a potential systemic risk for credit markets

What could make liquidity get worse in the near term? How do you address lack of liquidity

70% 70% 60% 60% 50% 50% 40% 40%

30% 30%

20% 20%

10% 10%

0% 0% Fed gets Rising Idiosyncratic Eurozone Other

more hawkish geopolitical ris k deflation Other fees risk Introduce classes Hold to Hold redemption Trade more CDS indices liquid asset liquid funds with funds t arget dates Move to more Move to Increase cash maturit y/launch

Source: RBS Credit Strategy Source: RBS Credit Strategy

49

What to do after the melt-up Avoid bubbles, don’t be a hero

Source: Minh Uong/The New York Times 50 2014 and 2015 key risk events: Apocalypse not now

AQR results published

ECB becomes bank supervisor BoE's f irs t rate hik e Fed's first rate hike Spain elections

Catalan independenc e UK elections Portugal elections referendum

Fed ends QE Finland elections

Brazilian elections

US Senate elections

Sep 2014 Dec 2014 Mar 2015 Jun 2015 S ep 2015 Dec 2015

Source: RBS Credit Strategy, Bloomberg. Blue = low impact; Red = high impact

51

Valuations: There is little juice left in risk premia

3,000 LT average 120 Current Max 2,500 100 Min

2,000 80

1,500 60

1,000 40

500 20

0 US IG EUR IG EM IG US HY EUR HY EM HY V2X VIX P/E

Source: RBS Credit Strategy, FRED, Bloomberg. 52 The end of the beta trade Stylised efficient frontier

E xpect ed ret urn 14% Pre-crisis: leveraged high- 12% rated portfolios (e.g. synthetic 10% CDOs, CPDOs) Today: investors buy unlevered, high 8% risk assets (e.g. HY, coc os)

6% QE effect

4%

2%

0% 0% 5% 10% 15% 20% 25% 30%

Volatility

Source: RBS Credit Strategy 53

Our Junk Bubble Indicator says credit no longer cheap Our indicator based on valuations, fundamentals, investor/issuer behaviour shows credit isn’t cheap

400% US 350% Europe

300%

250%

200% Overvalued

150%

100%

50% Undervalued

0% 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Source: RBS Credit Strategy 54 Our model suggests high yield spreads should be higher

2,500

2,000 HY spreads Mo d el

1,500

1,000

500

0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Source: RBS Credit Strategy, Federal Reserve 55

The end of the beta trade High-beta credit continues to weaken while Euro IG stays resilient, total returns, 100 = 31 Dec 2013

110

106

102

98

Euro I G US IG 94 Euro B US CCC Cocos Corporat e Hybrids 90 US Distressed

86 Jan Feb Mar Apr May Jun Jul Aug Sep Oct

Source: RBS Credit Strategy 56 Tantrum 2.0 vs Tantrum 1.0 Total returns during the taper tantrum (22 May – 29 August 2013) vs 2014 sell off (since June 2014 YtD)

10%

5%

0%

-5 %

-10% Taper tant rum 2014 tantrum -15%

-20% € IG £ IG $ IG Tech $ HY € HY Health Telcos € 3-5yr € 5-7yr € 1-3yr Energy Utilities Italy s ov € 7-10y r € Fin T1 $ EM IG S&P 500 € IGFins $ EM HY JNK ETF JNK LQ D E TF € Fin LT2 Spain sov Spain Cons Cyc Cons HYG ETFHYG EM$ Sov EMB ETF Industrials JNKE ETF BKLN ETF EMLC ETF EMHY ETF sov Greece Portugal sov Portugal Cons Non-cyc Cons Capital Goods Capital

Source: RBS Credit Strategy, Bloomberg. 57

Climbing the Pyramid of Yield

I’m a Credit Ninja CDO^2, CPDOs The Fed’s got my back, more risk on! CLOs, PIKs, cov-lites Cocos, corporate hybrids

There’s some real value there let’s join the trend. High yield, EM, financials, periphery, CDS

I’m making some money, let’s take little bit more risk. Investment grade corporates, periphery covered bonds

The world is in flames! Who cares about returns – I just want my money back Core sovereign bonds at negative yields, covered bonds, cash

Source: RBS Credit Strategy 58 Conclusions: Don’t be a hero

1. Europe is on a slow recovery trajectory, but no Japanification 2. Policymakers are restructuring the system. It will take time for banks to lend again, despite TLTRO. The ABS plan needs fiscal cooperation to work 3. The risks are: lack of 2nd (fiscal) and 3rd (reform) arrows, more hawkish Fed/BoE, EM and geopolitics 4. Markets have anticipated a lot of positive news. Now the ball is in the court of governments. Don’t be a hero Source: Google images. No animals or planets (only banks) were harmed for the making of this slide 59

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60 QE3 ends this month and stocks are struggling but questionable economic efficacy was evident in stock prices if you looked closely

Stocks with bond-like characteristics did well against bonds as the Fed drove real yields lower…but cyclical stocks actually cheapened to real yields with the average discount widening nearly 20 per cent

1 The US government sponsors + 75% of the residential mortgages in the United States today Investment Theme: Consolidation at the Top

All of the Top 25 Banks Today Were Formed Through M&A Activity; From 1990, Only 9 Banks Remain 1990 2013

- 1 - Bank Failures by Region

- 2 - 2014 Burridge Conference

1

Global Growth Was Soft in H1, But Appears to Be Rebounding…

6 Global GDP (q/q% saar)

5 CS Forecast

4 Long term average 3

2

1

0

-1

-2

-3

-4 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Source: Credit Suisse, Thomson Reuters DataStream

2 Part of The Challenge at Present Is That The Regions Are Unusually Uncorrelated

Q2 GDP vs Long-term average (pp of GDP) 2.5 2.0 2.0

1.5 1.0 1.0

0.5

0.0

-0.5

-1.0

-1.5 -1.5 -1.6 -2.0 -8.9 China EA US Japan UK

Source: Credit Suisse, Thomson Reuters DataStream

3

Monetary Policy Is Heading In Different Directions Across the Regions… Who Will Tighten First?

Source: Credit Suisse, Bank for International Settlements

4 The USD Bull Market Has Only Just Begun: The Dollar is Likely to Move More Than Expected in Coming Years USD REER

+46% over 6 150 years

140

+27% over 9 years 130

120 -23% over 10 years -34% over 7 110 years

100 -33% over 9 +9% over 3 years years

90 1964 1969 1974 1979 1984 1989 1994 1999 2004 2009 2014

Source: Credit Suisse

5

Impact of Regulation

2.3 10-year Treasury Yield (%, Intraday lows)

2.25

2.2

2.15

2.1

2.05

2

1.95

1.9

1.85 10/14/2014 10/15/2014 10/16/2014 10/17/2014 10/20/2014 10/21/2014

Source: Credit Suisse, Thomson Reuters DataStream

6