global econ weekly

7/29/2019 global econ weekly

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October 17, 2012

Issue No: 12/34

Global Economics Weekly

Economics Research

Assessing the interplay of macro surprises and spread products

Macro surprises matter for spread products but in different ways

We investigate the impact of macro surprises on the corporate bond and

Agency MBS markets. We find that for both investment grade corporate

bonds and Agency MBS, it is total returns (or equivalently yields) that

respond to macro surprises. For high yield bonds, it is the spreads that

respond to macro surprises. This difference reflects a trade-off between the

rates effect, where positive surprises cause a back-up in rates, and the

spread effect, where positive surprises lower the default premium. For

investment grade bonds and Agency MBS, the rates effect largely dominates

the spread effect, while for high yield bonds the two effects cancel out.

The impact is broader and larger since the global financial crisis

Focusing on the post-global financial crisis (GFC) sample period, we

document that the impact of macro surprises on both the corporate bond

and Agency MBS markets has become larger and broader.

Recent spread rally driven by declining premia, not better data

Looking at the recent spread rally, we find that both high yield bonds and

Agency MBS have outperformed the macro data, confirming our view that

the rally has been driven mostly by risk premia compression as opposed to

a better macro picture.

The impact of macro surprises on spreads has become bigger and broaderpost-crisisThe plot shows the response of credit spreads (in bp) to one standard deviationof surprises in the non-farm payroll report.

Source: Goldman Sachs Credit Strategy, Haver Analytics, Bloomberg

Lotfi Karoui(917) 343-1548 [email protected]

Goldman, Sachs & Co.

Kamakshya Trivedi

+44(20)7051-4005 [email protected]

Goldman Sachs International

Hui Shan(212) 902-4447 [email protected]


Jose Ursua

(212) 357-2234 [email protected]


George Cole

+44(20)7552-3779 [email protected]

Goldman Sachs International

Julian Richers



Dominic Wilson



Investors should consider this report as only a single factor in making their investment decision. For Reg AC certificationand other important disclosures, see the Disclosure Appendix, or go to www.gs.com/research/hedge.html.

The Goldman Sachs Group, Inc. Goldman Sachs

-15 -10 -5 0

A finan cials

BBB finan cials

B

BB

CCC

Daily change in spreads (bps)

Full Sample

Post-crisis

Pre-crisis


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October 17, 2012 Global Economics Weekly

Goldman Sachs Global Economics, Commodities and Strategy Research 2

Macro surprises matter (more so than in the past)

Few drivers are more important to macro investors than economic data surprises, i.e., the

difference between the realised value of macro indicators and the consensus expectation.

Perhaps surprisingly, the literature linking macroeconomic news to financial markets has

been quite extensive for the equity and Treasury markets but very limited for spreadproducts.1 In this Global Economics Weekly, we address this void with an in-depth analysis

of the impact of macro surprises on the Agency MBS and corporate bond markets.2

Exhibits 1 and 2 provide some intuition on the sensitivity of the corporate bond and

Agency mortgage-backed securities (MBS) markets to macro surprises. They plot the

standardised surprises in the US non-farm payroll report vs. the daily change in CCC- and

B-rated corporate bond spreads (Exhibit 1), as well as the daily change in the yield of the

Fannie Mae fixed rate 30-year constant maturity mortgage (CMM) (Exhibit 2).

The relationship with macro surprises is reasonably robust for both corporate bond

spreads and MBS yields: CCC and B spreads are negatively related to the surprises, while

the inverse pattern prevails for CMM yields. The intuition conveyed by Exhibits 1 and 2 is

simple: positive surprises lift growth expectations and thus cause the default risk premium

to compress and Treasury yields to back up. The result is tighter corporate bond spreads

and wider MBS yields.

Exhibit 1: CCC and B spreads are strongly related tosurprises in the non-farm payroll reportThe scatter plot shows the daily change in spreads (in bp) vs.the standardised surprise in the non-farm payroll report onthe announcement date.

Exhibit 2:Mortgage yields are also strongly related tosurprises in the non-farm payroll reportThe scatter plot shows the daily change in the yield of FannieMae 30-year fixed rate constant maturity mortgage (CMM) (inpercentage points) vs. the standardised surprise in the non-farm payroll report on the announcement date.

Source: Goldman Sachs Credit Strategy, Haver Analytics, Bloomberg Source: Goldman Sachs Credit Strategy, Bloomberg

1 For a study on the impact of macro surprises in the Treasury market, see Fleming, M and E., Remolona, 1999,

Price formation and liquidity in the U.S. Treasury market: the response to public information, Journal of Finance, 54,

1901- 1914. For our own work on the FX and equity markets, see for example Market Surprise Indices for Canada,

Global Viewpoint, March 7, 2012 and Economic News and the Equity Market, US Economics Analyst, July 27,

2012.2 To our best knowledge, Huang and Kong (2008) is the only study that examines the effect of macroeconomic news

announcements on corporate credit using a sample spanning the period from January 1997 through June 2003

(Huang, J and W., Kong, 2008, Macroeconomic news announcements and corporate credit spreads, Working

paper, Penn State University.)

-40

-20

0

20

40

-4 -2 0 2 4

Bps

Standardized surprise in non-farm payrolls

B CCC

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

-4 -2 0 2 4

%



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Exhibits 1 and 2 led us to consider a more detailed and systematic investigation of the

response of corporate credit and Agency MBS to macroeconomic news. The goal of our

investigation is threefold. First, we identify the macro indicators that matter the most to

corporate bonds and Agency MBS. Second, we examine whether there is a structural shift

in the way the credit and mortgage markets respond to macro surprises after the global

financial crisis (GFC). Lastly, we discuss some market implications and construct macro

surprise indices tailored to explain the performance of high yield bonds and Agency MBS.These indices provide some indication of the extent to which high yield bonds and the 30-

year CMM yield have over- or underperformed the recent macro data.

Our findings suggest the following:

On average, spreads on investment grade corporate bonds and MBS are relativelyinsensitive to macro surprises, while high yield bond spreads exhibit a strong

sensitivity. The converse is true when one looks at total returns or yields instead of

spreads: high yield is unresponsive, whereas investment grade corporate bond

total returns and Agency MBS yields exhibit a strong sensitivity.

Focusing on the post-GFC period, we document a larger and broader impact ofmacro surprises on both the corporate credit and Agency MBS markets. Pre-GFC,

spreads on financials were insensitive to macro surprises but have turned

responsive since. The same is true for CCC total returns, which have turned

responsive post-GFC. For Agency MBS yields, the size of the response has also

increased substantially post-GFC.

Lastly, we show that when benchmarked to the recent trajectory of the macro data,both high yield spreads and Agency MBS yields have compressed more than

macro surprises would imply, a finding that reinforces our view that much of the

recent rally has been driven by a risk premium compression as opposed to better

than expected macro data.

Spread products and macro surprises: What really matters

To quantify the impact of macro surprises on corporate bonds and Agency MBS, we look ata large number of US macroeconomic indicators and a range of corporate bond indices

over the past 15 years. Specifically, our sample comprises 28 macro indicators for which

consensus forecasts are provided by Bloomberg. Our credit market variables include the

following rating and sector buckets: A financials, BBB financials, A non-financials, BBB non-

financials, BB, B and CCC. For each of these buckets, we look at both spreads and total

returns. The response of spreads to macro surprises allows us to isolate the impact on the

default premium, while the response of total returns captures the simultaneous impact on

spreads, rates and duration.

Finally, as a proxy for the response of mortgage markets, we use the Fannie Mae fixed rate

30-year constant maturity mortgage yield (CMM). For comparison, we also report the

response of the 2-, 10- and 30-year Treasury yields (see the box on the next page for a

detailed explanation of our data construction and regression specification).

Our baseline estimation reveals three key findings.

Not all macro indicators are createdequal (unsurprisingly). For both the credit andmortgage markets, labour market indicators (non-farm payrolls, initial claims, the

ADP employment report and the unemployment rate) tend to have the strongest

impact. Survey data (such as the ISM both manufacturing and non-

manufacturing and Philly Fed) and hard data (such as retail sales and durable

goods) also appear to have a significant impact on daily moves in credit spreads

and total returns, as well as on CMM yields.


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,

,

Our methodology in a nutshell

The data

Our market variables consist of the Merrill Lynch (ML) bond spread and total return indices, and the yield on Fannie

Mae 30-year current coupon mortgages. In the corporate investment grade space, we distinguish between financials

and non-financials. In high yield, we look at BB, B and CCC rating buckets. All together, we examine seven credit

variables: A non-financials, BBB non-financials, A financials, BBB financials, BB, B and CCC. For each index, we look at

both spreads and total returns.

For macro surprises, we use the following 28 macro indicators:

Labour market: Non-Farm Payrolls, the Unemployment Rate, Initial Claims, Continuing Claims and the ADPEmployment Report.

Survey data: ISM, ISM Non-Manufacturing, Philly Fed, Chicago PMI, Empire State Manufacturing Survey,Richmond Federal Reserve Manufacturing Survey, Conference Board Consumer Confidence and University of

Michigan Survey of Consumer Confidence Sentiment.

Hard data: Industrial Production, US Durable Goods New Orders Total ex Transportation, US Trade Balanceof Payments, US Personal Consumption Expenditure, Adjusted Retail Sales Less Autos and GDP.

Inflation: Core CPI, Core PPI and the US Import Price Index. Housing: New One Family Houses Sold, Existing Homes Sales, Pending Home Sales Index, New Privately

Owned Housing Units Started, Census Bureau Construction Spending and the National Association of Home

Builders Market Index.

The basic regression model

For each of the macro variables cited above, we define the surprise as the difference between the actual release and

the median forecast of the Bloomberg News survey, normalised by the sample standard deviation:

where is our surprise measure,denotes the actual release, denotes the median forecast in the BloombergNews survey, and denotes the standard deviation of the surprises in the sample for that specific macro indicator.For example, the total non-farm payroll for March 2004 was released on April 2. Among the 71 forecasts in the

Bloomberg News survey, the median was 120K and the actual release was 308K. Because the standard deviation of all

non-farm payroll surprises in our sample is 91K, the 188K surprise on April 2, 2004 translates into a positive surprise

of 2.1 standard deviations.

Our basic regression model can be written as follows:

where is the daily change in the market variable (spread, yield or total return) and is our surprise measure.Our sample spans the period from January 1997 through the first week of October 2012.

The cumulative surprise indices

After estimating the regression model, we select the macro variables with estimated coefficients that are both

statistically and economically significant. Because macro indicators are typically released monthly and because daily

changes in spreads and yields can be very noisy, we sum up the cumulative impact of all macro surprises since

January 2010 and compare it with the cumulative change in high yield spreads and CMM yields over the same period.

We construct the surprise index separately for high yield spreads (cash) and mortgage yields.


5/14



In spread terms, only high yield is sensitive to macro surprises (Exhibit 3).Moreover, the response of high yield spreads to macro surprises is monotonic in

ratings: the lower the rating, the stronger the response. By contrast, investment

grade credit spreads are virtually unresponsive to macro surprises for both

financials and non-financials. For total returns, the opposite pattern prevails. High

yield is insensitive to macro surprises, whereas investment grade total returns

respond strongly (Exhibit 4). The unresponsiveness of high yield total returns tomacro surprises reflects the fact that the spread tightening that follows a positive

surprise is offset by a roughly equal back-up in rates. For example, for CCC-rated

bonds and non-farm payrolls, a one standard deviation of surprise translates into

close to 5bp of spread tightening, which is roughly offset by 4bp of back-up in

rates. By contrast, investment grade bonds (both financials and non-financials)

respond negatively to positive macro surprises. This reflects the stronger rates

effect relative to the spread effect.

Lastly, CMM yields respond to macro surprises in a way that is almost identical to10-year Treasury yields (Exhibits 5 and 6). This is consistent with the notion that

agency MBS and 10-year Treasury securities are close substitutes of each other.

Exhibit 3:In spread terms, only high yield bonds respondto macro announcement surprisesThe plot shows the response of high yield spreads (in bp) toone standard deviation of surprises.

Exhibit 4:For total returns, the inverse pattern prevails:investment grade is responsive while high yield is notThe plot shows the response of investment grade totalreturns (in percentage points) to one standard deviation ofsurprises.

Source: Goldman Sachs Credit Strategy, Haver Analytics, Bloomberg Source: Goldman Sachs Credit Strategy, Haver Analytics, Bloomberg

-6 -4 -2 0 2 4

Unemployment rate

Richmond

Retail sales

Philly fed

Payrolls

ISM non-mfg

ISM

Empire

Durable goods

Core CPI

ADP


CCC

B

BB

-0.3 -0.2 -0.1 0

Retail sales

Payrolls

ISM non-mfg

ISM

ADP

Daily change in total returns (%)

A financials

BBB financials

A nonfinancia ls

BBB nonfinancials


6/14



Exhibit 5:Mortgage yields respond to a large number ofmacro surprisesThe plot shows the response of the 30-year CMM yield (inpercentage points) to one standard deviation of surprises.

Exhibit 6:following a similar pattern to Treasury yieldsThe plot shows the response of the Treasury curve (inpercentage points) to one standard deviation of surprises.

Source: Goldman Sachs Credit Strategy, Bloomberg Source: Goldman Sachs Credit Strategy, Bloomberg

Credit and Agency MBS have turned more macro post-GFC

The above results show the average response of the credit and mortgage markets to macro

surprises from 1997 to 2012. In quantifying this response, we implicitly constrained the

sensitivity of each market variable to each macro indicator to be the same throughout the

sample period. One potential drawback with this approach is that it ignores the potential

structural shift in the way the credit and mortgage markets respond to macroeconomic

news.

Exhibits 7 and 8 provide some preliminary intuition on the post-GFC effect. They plot thestandardised surprises in the non-farm payroll report vs. the daily change in CCC spreads

and A financials spreads, pre- and post-GFC. The pre-crisis sample covers the period from

January 1997 to August 2008, while the post-GFC sample spans the period from March

2009 to today. We deliberately excluded 4Q2008 and 1Q2009 given the degree of disruption

in financials markets at that time. These exhibits show that the slope of the regression line

clearly increased post-GFC.

To further investigate potential post-GFC changes, we estimate our model using the pre-

GFC sample and the post-GFC sample separately. For ease of exhibition, we only show the

beta to surprises in the non-farm payroll report below since it is the most important macro

indicator, but our model still includes all 28 macro indictors described earlier.

Our results can be summarised into three findings.

For the same credit bucket, the impact of macro surprises has become larger post-GFC. For example, Exhibit 9 shows that high yield spreads tighten on average in

response to positive payroll surprises. However, the magnitude of this effect is

three to four times larger post-GFC compared with the pre-GFC period.

Macro surprises affect more credit indices post-GFC. Pre-GFC, investment gradespreads were insensitive to macro surprises. By contrast, in the post-GFC sample,

spreads of A and BBB financials have become responsive (Exhibit 9). The same is

true for the CCC total return index, which has turned responsive post-GFC (Exhibit

10). This suggests that the spread effect for CCCs has become larger than the

-0.02 -0.01 0 0.01 0.02 0.03 0.04

Initial claims

Core PPI

Empire

Durable goods

Trade

Chicago PMI

Philly fed

ISM non-mfg

ADP

Retail sales

ISM

Payrolls

Daily change in yields (%)

30-yr CMM

-0.02 -0.01 0 0. 01 0.02 0.03 0.04

Unemployment rate

Initial claims

Consumer confidence

Trade

Core PPI

Existing home sales

Chicago PMI

Philly f ed

ISM non-mfg

Durable goods

ADP

Retail sales

ISM

Payrolls

Daily change in yields (percentage points)

2-yr Tsy

10-yr Tsy

30-yr Tsy


7/14



rates effect, thereby leading to an increase in the total return following positive

payroll surprises.

CMM yields still behave very much like Treasury yields post-GFC. The analysisperformed on the full sample showed that CMM and Treasury yields respond to

macro surprises in a quasi-identical way. When splitting the sample into pre- and

post-GFC, the magnitude of the beta to non-farm payroll surprises increases

substantially after the GFC for both CMM and Treasury yields. However, the

increases are commensurate to each other, suggesting that Agency MBS and

Treasury bonds remain close substitutes despite the impact of the GFC on the

landscape of both markets (Exhibit 11).

Exhibit 7:CCC spreads have become more sensitive tomacro surprises post-crisisThe scatter plot shows the daily change in CCC spreads vs.the standardised surprise in the non-farm payroll report onthe announcement date.

Exhibit 8:A financials spreads were insensitive to macrosurprises pre-crisis but have turned more responsivesince March 2009The scatter plot shows the daily change in A financialsspreads vs. the standardised surprise in the non-farm payrollreport on the announcement date.


Exhibit 9:The impact of macro surprises on spreads hasbecome bigger and broader post-crisisThe plot shows the response of credit spreads (in bp) to onestandard deviation of surprises in the non-farm payrollreport.

Exhibit 10:Same is true for investment grade totalreturnsThe plot shows the response of investment grade totalreturns (in percentage points) to one standard deviation ofsurprises in the non-farm payroll report.


Pre-GFC: y = -4.9x - 0.7

Post-GFC: y = -11.7x - 3.0-80

-60

-40

-20

0

20

40

60

80

100

-4 -2 0 2 4

Bps


Pre-GFC

Post-GFC

Post-GFC: y = -2.6x - 1.2-15

-10

-5

0

5

10

15

-4 -2 0 2 4

Bps


Pre-GFC

Post-GFC

Pre-GFC: No significantrelationhsip

-15 -10 -5 0

A financials

BBB financials

B

BB

CCC


Full Sample

Post-crisis

Pre-crisis

-0.6 -0.4 -0.2 0 0.2 0.4

A financials

BBB financials

A nonfinanc ials

BBB nonfinancials

CCC

Daily change in total returns (%)

Full sample

Post-crisis

Pre-crisis


8/14



Exhibit 11:CMM and Treasury yields behave similarlyThe plot shows the response of mortgage and Treasury yields (in percentage points) to onestandard deviation of surprises in the non-farm payroll report.

Source: Goldman Sachs Credit Strategy, Bloomberg

Deconstructing the spread rally: Aggressive policy has eclipsedlacklustre macro data

Armed with the estimated coefficients on the macro surprises, we construct surprise

indices that are tailored for high yield bond spreads and CMM yields. These surprise

indices can be thought of as the portion of the cumulative change in spreads or yields that

is due to macro surprises. Any disconnect between the surprise index and the actual

change in spreads or yields therefore indicates potential under- or outperformance relativeto macro surprises.

Exhibits 12 and 13 provide time series for our surprise indices alongside the cumulative

change in high yield spreads and CMM yields since January 2010. For high yield spreads,

positive surprises push the index down and tighten spreads, while the converse is true for

the 30-year CMM yields, which behave in a similar way to Treasury yields.

Starting with high yield spreads, it is clear that up until a few weeks ago, high yield spreads

significantly outperformed macro surprises. This outperformance started after the

European summit in June, further intensified following ECB President Mario Draghis

speech, and culminated with the aggressive policy moves by the ECB and the Fed. The gap

between the surprise index and the change in high yield spreads has recently started to

close. This pattern is by and large reflective of waning negative surprises and a stallingrally in the high yield bond market.

The above findings confirm our existing view that the recent compression in corporate

credit spreads has been driven mostly by premium compression as opposed to better than

expected macro data. While growth expectations and risk premium are closely coupled

concepts each feeds heavily into the other we suspect the current round of policy action

will be more effective in reducing systemic concerns than meaningfully improving the

growth outlook.

0.00 0.02 0.04 0.06 0.08 0.10

30-yr CMM

30-yr Tsy

10-yr Tsy

2-yr Tsy

Daily change in yields (percentage points)

Full sample

Post-crisis

Pre-crisis


9/14



Exhibit 12:Current high yield bond spreads haveoutperformed macro surprises since JuneThe plot shows our high yield surprise index alongside thecumulative change in high yield spreads since January 2010.

Exhibit 13:Mortgage yields have also diverged frommacro surprises recentlyThe plot shows our CMM yield surprise index alongside thecumulative change in yields since January 2010.

Source: Goldman Sachs Credit Strategy, Haver Analytics, Bloomberg Source: Goldman Sachs Credit Strategy, Bloomberg

We have long favoured high-quality high yield as the best place to express this view in

corporate credit. Although high yield spreads have tightened more than 200bp from the

wides of roughly 700bp in May 2012, we think spreads driven more by a search for yield

than by growth optimism could continue to grind tighter from here. As we have pointed

out in previous research (see, for example, Affirming the case for HY bonds, The Credit

Line, August 30, 2012), we think high yield credit risk remains compelling, especially the BB

and B buckets, for at least three reasons. First, our own Credit Risk Premium estimates still

indicate that, with the exception of the CCC bucket, the premia for BB and B bonds is above

average. Second, credit metrics remain close to their best levels in 25 years. This is true

despite a slight deterioration since the fourth quarter of 2011. Third, the current macroenvironment remains typical of a post-bust recovery, with low rates and inflation. Coupled

with the exceptionally accommodative stance of monetary policy, this should support

demand for spread assets by enhancing search for yield motives.

Turning to CMM yields, they have continued to decline despite the modest improvement in

the macro data (see Exhibit 13). Of course, the Feds decision to purchase an additional

$40bn Agency MBS per month on an open-ended basis can explain much of this

divergence. But with the QE3 announcement now behind us, we think there is probably

some pressure for mortgage yields to catch up with the data, all else equal. Thus, unless

macro surprises turn negative, we think the rally in Agency MBS is probably done.

Lotfi Karoui and Hui Shan

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40

60

80

-300

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0

100

200

300

400

Jan-10 J ul-10 Feb-11 Aug-11 Mar-12 Oct -12

BpsBps

Cumulative s pread c hange

Cumulative surprise (RHS)-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

J an-10 J ul-10 Feb-11 Aug-11 Mar-12 Oct -12

%%

Cumulative yield change (LHS)

Cumulative surprise (RHS)


10/14



Global Economic Forecasts

Real GDP, %ch yoy Consumer Prices, %ch yoy

Source: Goldman Sachs Global ECS Research.

For India we use WPI not CPI. For a list of the members within groups, please refer to ERWIN.

For our latest Bond, Currency and GSDEER forecasts, please refer to the Goldman Sachs 360 website: (https://360.gs.com/gs/portal/research/econ/econmarkets/).

2010 2011 2012 2013

USA 2.4 1.8 2.2 1.9

Euro area 2.0 1.5 -0.5 0.1

Japan 4.5 -0.8 2.4 1.1

Australia 2.5 2.1 3.5 2.6

Canada 3.2 2.4 2.1 2.4

France 1.6 1.7 0.2 0.6

Germany 4.0 3.1 1.0 1.2

Italy 1.8 0.5 -2.2 -0.7

New Zealand 1.7 1.3 2.5 2.7

Norway 0.6 1.5 4.1 2.0

Spain -0.3 0.4 -1.3 -1.7

Sweden 6.3 3.9 1.3 1.9

Switzerland 2.7 2.1 1.2 1.1UK 1.8 0.9 0.1 1.9

China 10.4 9.3 7.6 8.0

Hong Kong 7.0 5.1 3.5 5.2

India 8.4 6.5 5.7 7.0

Indonesia 6.1 6.5 6.0 6.4

Malaysia 7.2 5.1 4.6 5.3

Philippines 7.3 3.9 4.9 5.3

Singapore 14.8 4.9 2.0 4.0

South Korea 6.3 3.6 2.6 3.5

Taiwan 10.7 4.0 3.0 4.5

Thailand 7.8 0.1 5.7 4.8

Czech Republic 2.6 1.7 -0.2 1.7

Hungary 1.2 1.7 -1.0 1.2

Poland 3.9 4.3 2.8 2.4

Russia 4.0 4.3 3.8 4.3

South Africa 2.9 3.1 2.6 3.4

Turkey 9.2 8.5 3.2 5.5

Argentina 9.2 8.9 3.0 4.1

Brazil 7.5 2.7 1.6 4.0

Chile 6.1 6.0 4.5 5.2

Mexico 5.5 3.9 3.7 4.0

Venezuela -1.5 4.2 4.5 2.9

BRICS 8.9 7.4 6.2 6.9

G7 2.8 1.4 1.5 1.5

EU27 2.1 1.6 -0.2 0.6G20 4.9 3.8 3.1 3.6

Asia ex Japan 9.3 7.4 6.3 7.0

Central and Eastern Europe 3.2 3.3 1.5 2.1

Latin America 6.4 4.6 3.2 4.2

Emerging Markets 8.1 6.8 5.6 6.3

Advanced Economies 3.0 1.6 1.4 1.5

World 5.0 3.8 3.1 3.6

Latin America

Regional Aggregates

G3

Advanced Economies

Asia

CEEMEA

2010 2011 2012 2013

USA 1.6 3.1 2.1 2.1

Euro area 1.6 2.7 2.5 2.2

Japan -0.7 -0.3 0.1 0.2

Australia 2.8 3.4 2.0 3.2

Canada 1.8 2.9 2.0 2.0

France 1.7 2.3 2.2 1.8

Germany 1.2 2.5 2.1 2.0

Italy 1.6 2.9 3.5 2.7

New Zealand 2.3 4.0 1.4 2.4

Norway 2.4 1.3 1.0 1.3

Spain 2.0 3.1 2.3 2.3

Sweden 1.3 2.6 1.3 1.3

Switzerland 0.7 0.2 0.4 0.7UK 3.3 4.5 2.7 2.1

China 3.3 5.4 2.8 3.0

Hong Kong 2.4 5.3 4.2 4.4

India 9.6 8.9 7.2 5.7

Indonesia 5.1 5.4 4.5 5.7

Malaysia 1.7 3.2 1.8 2.8

Philippines 4.1 4.7 3.4 4.2

Singapore 2.8 5.2 4.3 3.4

South Korea 2.9 4.0 2.4 2.8

Taiwan 1.0 1.4 1.2 1.7

Thailand 3.3 3.8 3.1 4.4

Czech Republic 1.5 1.9 3.3 1.7

Hungary 4.9 3.9 5.7 4.0

Poland 2.6 4.3 3.8 2.8

Russia 6.8 9.8 5.1 6.4

South Africa 4.3 5.0 5.7 5.0

Turkey 8.6 6.5 9.2 9.0

Argentina 10.5 9.8 10.0 9.6

Brazil 5.0 6.6 5.3 4.9

Chile 1.4 3.3 3.0 3.2

Mexico 4.2 3.4 4.2 3.7

Venezuela 29.1 27.1 21.2 27.0

BRICS 5.3 6.8 4.2 4.2

G7 1.4 2.6 1.9 1.8

EU27 1.9 3.0 2.6 2.2G20 3.1 4.3 3.1 3.0

Asia ex Japan 4.6 5.7 3.7 3.7

Central and Eastern Europe 2.7 3.7 4.0 2.7

Latin America 6.1 6.7 6.1 5.7

Emerging Markets 5.5 6.5 4.7 4.6

Advanced Economies 1.6 2.7 2.1 2.0

World 3.1 4.3 3.2 3.1

G3

Advanced Economies

Asia

CEEMEA

Latin America

Regional Aggregates


11/14



Key Charts: The GLI, GS FSI, ERP and the Credit Premium

GLI Momentum vs. Global Industrial Production* GS Financial Stress Index

* Includes OECD countries plus BRICs, Indonesia and South Africa

SeeGlobal Economics Paper199 for methodology

Source: OECD, Goldman Sachs Global ECS Research.

See the November 2008Fixed Income Monthly for methodology


US Equity Risk Premium US Equity Credit Premium

SeeGlobal Economics Weekly02/35 for methodology


SeeGlobal Economics Weekly 02/35 for methodology


-4

-3

-2

-1

0

1

2

00 01 02 03 04 05 06 07 08 09 10 11 12 13

%mom

GLI MomentumGlobal I ndustrial Production*, 3mma

-2

-1

0

1

2

3

4

5

6

96 98 00 02 04 06 08 10 12

Index

1 standarddeviation band

1.7

2.1

2.5

2.9

3.3

3.7

4.1

4.5

4.9

5.3

5.76.1

6.5

04 05 06 07 08 09 10 11 12 13

%

US ERP, calculated da ily

US ERP, 200 Day Moving Average

-3

-2

-1

0

1

2

3

4

5

82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12

%

1985-1998average

2 standard deviationsband

Creditrelatively

expensive


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The World in a Nutshell

THE GLOBAL ECONOMY

OUTLOOK KEY ISSUES

UNITED STATES We expect growth of 2.2% in 2012 and 1.9% in 2013. On asequential basis, growth should settle at 2.0% in 2012Q4before falling to 1.5% in 2013Q1. We then expect a pick-upto 2.5% sequential growth in the second half of 2013.Despite weak growth, we expect the unemployment rateto fall to 7.7% by end-2013 as long-term unemploymentcontinues to depress labour force participation.

Our forecast of around 2.0% growth over the comingquarters is supported by factors that should preventgrowth from falling further from here. These includestrong real disposable income, a gradual housingrecovery, easier financial conditions and an end to theinventory drag on manufacturing. However, we worry thatthe fiscal cliff at year-end could potentially weigh moreheavily on financial markets and the real economy.

JAPAN We expect real GDP growth of 2.4% in 2012 and 1.1% in2013. The relative robustness of growth this year reflects(1) the statistical boost from January-March strength and(2) the current role of domestic demand as the key sourceof growth. With public-sector reconstruction demandgradually fading, we expect real GDP growth to declineslowly through the rest of the year.

Exports are crucial to Japans production activity. Aspublic reconstruction measures have faded, externaldemand is again becoming the key determinant. Exportsto China and Europe have recently fallen significantly, andas Chinas recovery remains slow, we see little indicationof a reversal of this trend.

EUROPE The Euro area-wide macroeconomic picture has

deteriorated in recent months. As a result, we downgradedour forecasts and now foresee a contraction of 0.5% in2012, followed by sub-trend growth of 0.1% in 2013. Cross-country divergence remains a key theme in this baselinescenario, with economic weakness expected to be moremarked in peripheral economies. Our baseline is still thatthe Euro area will muddle through but remain intact. Apotential delay in Spains request for EFSF support posesdownside risks to markets in the immediate term.

The open question remains whether the follow-through

on fundamental political decisions (periphery reformprogrammes, the building of a new regime ofmacroeconomic discipline and the intra-Euro area risk-sharing inherent in an enlarged EFSF/ESM) turns out to beweak or lacking. We perceive the ECB as willing to lendsubstantial support in the process but only see this as anincremental step in a long cumulative process to deliver agovernance structure sufficient to create a betterfunctioning Euro area.

NON-JAPAN ASIA For Asia ex-Japan, we expect growth of 6.3% and 7.0% in2012 and 2013, respectively. In 2012, we expect below-trend growth throughout the region, while in 2013 thesmaller AEJ economies are likely to recover to aroundtrend as the external environment improves. We do notcurrently expect precautionary policy easing in most of theregion.

In China, we expect below-trend GDP growth of 7.6% in2012 and 8.0% in 2013. Looking forward, we expectfurther easing in macro policy (via rate cuts, an easing inbank lending restrictions, less currency appreciation andnew investment projects) and a pick-up in sequentialgrowth, although the economy should remain softer inthe next few quarters.

LATIN AMERICA We forecast that real GDP growth in Latin America willslow to 3.2% in 2012, and then rebound to 4.2% in 2013.We expect monetary policy stances to remain mixedacross the region in the near term, and subsequent broadtightening in 2013.

In Brazil, we expect real GDP growth of 1.6% and 4.0% in2012 and 2013, respectively. Brazil has been in an easingcycle, including interest rate cuts and macro-prudentialmeasures to ease credit conditions. We expect policy toremain at current levels until mid-2013 despite the recentuptick in inflation.

CENTRAL &EASTERN EUROPE,MIDDLE EAST ANDAFRICA

We expect CEEMEA to continue on its sluggish path torecovery, with growth well below potential. Renewedstresses in the Euro area have led the region tounderperform LatAm and NJA but recent improvementsthere allow for green shoots of growth. In 2013, weexpect growth to reaccelerate, in line with a more benignglobal growth environment.

Within the region, we see balance sheet strength anddomestic demand robustness as the key macrodifferentiation theme coming out of the 2012 soft patch.Growth improvements are concentrated in countries withbetter domestic demand dynamics, i.e., Russia, Turkeyand Poland. Hungary and the Czech Republic also showimprovement, although from very low bases.

CENTRAL BANK INTEREST RATE POLICIES

CURRENT SITUATION NEXT MEETINGS EXPECTATION

UNITED STATES:

FOMC

The Fed cut the funds rate to a rangeof 0%-0.25% on December 16, 2008.

October 24

December 12

We expect the Fed to keep the funds rate near 0%through the end of 2013.

JAPAN: BoJ MonetaryPolicy Board

The BoJ cut the overnight call rate to arange of 0%-0.1% on October 5, 2010.

October 30

November 20

We expect the BoJ to keep the policy rate near 0%through the end of 2013.

EURO AREA: ECBGoverning Council

The ECB cut refi/deposit rates by 25bpto 0.75%/0.00% on July 5, 2012.

November 8

December 6

We expect the ECB to keep policy rates on holdthrough the end of 2013.

UK: BoE MonetaryPolicy Committee

The BoE cut rates by 50bp to 0.5% onMarch 5, 2009.

November 8

December 6

We expect the BoE to keep the policy rate unchangedbut to deliver further quantitative easing at itsNovember meeting.


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Disclosure Appendix

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We, Lotfi Karoui and Hui Shan, hereby certify that all of the views expressed in this report accurately reflect our personal views about the subjectcompany or companies and its or their securities. We also certify that no part of our compensation was, is or will be, directly or indirectly, related tothe specific recommendations or views expressed in this report.

We, Kamakshya Trivedi, Jose Ursua, George Cole, Julian Richers and Dominic Wilson, hereby certify that all of the views expressed in this reportaccurately reflect our personal views, which have not been influenced by considerations of the firm's business or client relationships.

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