International Equity Market Correlations

International Equity Market Correlations

presented at Northfield Research Conference 4 December, 2000

Rosemary MacedoBailard, Biehl & Kaiser

950 Tower Lane #1900, Foster City, CA [email protected] 650-483-7953

2

Discussion Topics

• Is our ability to diversify international portfolios deteriorating?

• Does diversification disappear right when you need it most?

• Should the country approach be replaced with a sector approach?

3

Why the Answers Matter

Stress-testing--assessment of effects of high volatility on portfolio should include expected changes in the correlation.

Choice of long-term or recent history to estimate covariance.

Better assessment of risk and opportunity.

Optimal allocation of assets.

Hedging strategies.

4

Data

Datastream indices:

Daily market returns ( Jan 1973 through Oct 2000, US$)60 day cross-country correlations ( 231 pairs of countries)

Weekly individual country sector returns(9cty X 10sect, Jan 1990 - Oct 2000, E)eg: French telecom, German telecom . . .

French banks, German banks . . .

Weekly regional sector returns ( Jan 1973 through Oct 2000, E)Weekly country returns ( Jan 1973 through Oct 2000, E)

5

Deteriorating Diversification

Globalization--countries, companies and markets becoming more and more alike.

Euroland--move to single currency, single market.

Cross border mergers and acquisitions.

Multiple listings, expanded trading hours.

Institutional portfolio manager herding.

6

Average Correlations With USA1.0

.9

.8

.7

.6

.5

.4

.3

.2

.1

0.0

-.1

0007

1300

0127

9908

1299

0225

9809

0898

0324

9710

0397

0418

9610

3096

0515

9511

2795

0612

9412

2394

0708

9401

2193

0806

9302

1992

0902

9203

1891

0930

9104

1590

1025

9005

1089

1121

8906

0688

1219

8807

0488

0118

8707

3087

0212

8608

2686

0311

8509

2 085

040 5

8410

1 784

050 2

8311

1 583

053 1

8212

1 482

062 9

8201

1 281

072 8

8102

1 080

082 2

8003

0 779

091 9

7904

0 478

101 6

7805

0 177

111 0

7705

2 676

1209

7606

2476

0108

7507

2275

0204

7408

1674

0301

7309

1273

0328

across country pairs: ρ(USA,UK),ρ(USA,Jpn),ρ(USA,Ger) . . .

Source: BB&K

Trailing 60days (yymmdd)

7

0007

1300

0127

9908

1299

0225

9809

0898

0324

9710

0397

0418

9610

3096

0515

9511

2795

0612

9412

2394

0708

9401

2193

0806

9302

1992

0902

9203

1891

0930

9104

1590

1025

9005

1089

1121

8906

0688

1219

8807

0488

0118

8707

3087

0212

8608

2686

0311

8509

2 085

040 5

8410

1 784

050 2

8311

1 583

053 1

8212

1 482

062 9

8201

1 281

072 8

8102

1 080

082 2

8003

0 779

091 9

7904

0 478

101 6

7805

0 177

111 0

7705

2 676

1209

7606

2476

0108

7507

2275

0204

7408

1674

0301

7309

1273

0328

1.0

.9

.8

.7

.6

.5

.4

.3

.2

.1

0.0

across country pairs: ρ(Fra,Ger),ρ(Fra,Ita),ρ(Ger,Ita) . . .

Average Correlations Within Euroland

Source: BB&K


8

1.0

.9

.8

.7

.6

.5

.4

.3

.2

.1

0.0

0007

1300

0127

9908

1299

0225

9809

0898

0324

9710

0397

0418

9610

3096

0515

9511

2795

0612

9412

2394

0708

9401

2193

0806

9302

1992

0902

9203

1891

0930

9104

1590

1025

9005

1089

1121

8906

0688

1219

8807

0488

0118

8707

3087

0212

8608

2686

0311

8509

2 085

040 5

8410

1 784

050 2

8311

1 583

053 1

8212

1 482

062 9

8201

1 281

072 8

8102

1 080

082 2

8003

0 779

091 9

7904

0 478

101 6

7805

0 177

111 0

7705

2 676

1209

7606

2476

0108

7507

2275

0204

7408

1674

0301

7309

1273

0328

across country pairs: ρ(Jpn,HKg),ρ(Jpn,Sng),ρ(HKg,Sng) . . .

Average Correlations Within Pacific Rim

Source: BB&K


9

Regional Comparison

73-81 82-91 92-10/00avg st dev avg st dev avg st dev

Euro-Other Eur 0.28 0.22 0.44 0.21 0.48 0.18within Euroland 0.27 0.22 0.43 0.21 0.47 0.19within Other Eur 0.21 0.22 0.43 0.21 0.45 0.16within Pacific Rim 0.17 0.20 0.30 0.22 0.31 0.20Pac Rim-Other Eur0.13 0.18 0.27 0.19 0.22 0.16Pac Rim-Euro 0.14 0.18 0.25 0.20 0.21 0.16

with USA 0.19 .21 0.23 0.21 0.28 0.18

10

Correlation StabilityLESS

STABLE

MORESTABLE

MOREDIVERSIFYING

LESSDIVERSIFYING

Average of 60day Correlations

NZE,BEL

NZE,UK

.7.6.5.4.3.2.10.0

.3

.2

.1

ITA,POR

USA,CAN

AUS,NZE

GER,NET

GER,SWI

ITA,FRAITA,GER

ITA,NET

CAN,POR

CAN,ITA

CAN,UK

CAN,FRANZE,NET

USA,FRAUSA,SWE

USA,NET

FRA,SPA

UK,SWI

USA,POR

USA,AUT

USA,DEN

USA,JPN

SPA,PORFRA,GER

GER,AUT

FRA,UK

Sta

ndar

d D

evia

tion

of 6

0day

Cor

rela

tions

11

Phantom Diversificationvanishes when most needed

Higher correlations when markets fall.

Higher correlations when markets are volatile.

Does this reflect a real change in the relationships between asset returns?

How inconsistent is this with constant correlation assumption? Normality assumption? Compare with theoretical conditional correlations.

What about “contagion”?

12

Extreme Market Moves

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

bottomdecilereturns

topdecilereturnsC

ondi

tiona

l Cor

rela

tion

Unconditional Correlation

13

Constant Correlation Mandates Higher Conditional Correlation in the Tails

ρ2 + (1-ρ2) Var(x) / Var(x|A)

ρρA=

ρA = f( ρ, )Var(x|A)Var(x)

Significant differences between the conditional correlations are caused by the choice of subsamples alone; they do not necessarily indicate any change in the parameters of the data generating process, merely the time-varying market volatility.

14Expected Increase Conditional Correlation vs.

Unconditional

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Two-sided event probability

5%10%20%

top&bottom decile

50%top&bottom quartile

Exp

ecte

d In

crea

se (b

ivar

iate

nor

mal

rand

om v

aria

bles

)C

ondi

tiona

l Cor

rela

tion

vs. U

ncon

ditio

nal C

orre

latio

n(e

ffect

s w

ould

be

mor

e pr

onou

nced

for d

istri

butio

ns w

ith fa

tter t

ails

)

Source: BB&K


15

Observations

Expect correlations to increase significantly, especially for unconditional correlations between 0.4-0.5, and especially for more extreme conditions:

.4 --> .5+ conditioned on top&bottom quartile returns.4 --> .6+ conditioned on top&bottom decile returns

Correlations do not increase uniformly. Optimal portfolios based on the conditional variance-covariance matrix therefore will differ from those based on the unconditional.

Market returns exhibit significant excess kurtosis & negative skewness--affects will be worse than shown on previous slide.

16Average Correlations With USA vs. Volatility

54321.9.8.7.6.5.4.3

1.0

.9

.8

.7

.6

.5

.4

.3

.2

.1

0.0

-.1Ave

rage

60d

ay C

orre

latio

n ac

ross

cou

ntry

pai

rs:

ρ(U

SA

,UK

),ρ(U

SA

,Jpn

),ρ(U

SA

,Ger

) . .

.

60day Volatility

17Average Correlations Within Euroland vs. Volatility

54321.9.8.7.6.5.4.3

1.0

.9

.8

.7

.6

.5

.4

.3

.2

.1

0.0Ave

rage

60d

ay C

orre

latio

n ac

ross

cou

ntry

pai

rs: ρ

(Fra

,Ger

),ρ(F

ra,It

a),ρ

(Ger

,Ita)

. . .

60day Volatility

18Average Correlations Within Pacific Rim

vs. Volatility

54321.9.8.7.6.5.4.3

1.0

.9

.8

.7

.6

.5

.4

.3

.2

.1

0.0Ave

rage

60d

ay C

orre

latio

n ac

ross

cou

ntry

pai

rs: ρ

(Fra

,Ger

),ρ(F

ra,It

a),ρ

(Ger

,Ita)

. . .

60day Volatility

19

Worse in Euroland?

Does the steeper slope (correlation vs volatility) in Euroland indicate some effect beyond what statistics predicts?

Check by comparing actual increase in conditional correlation for top quartile of volatility with expected. (from Monte Carlo Simulation)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Probably not. The unconditionalcorrelations within Euroland are higher than for other regions, closer to peak impact from sampling effects.

20

WithinEuroland

others

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7Incr

ease

:C

ondi

tiona

l Cor

rela

tion

vs. U

ncon

ditio

nal


Monte Carlo Simulationmedian

Top Quartile of Volatility

21

Summary

Very easy to make compelling, in some cases alarming, pictures.

Especially post mortem.

Autopsy reveals statistics not pathology.

22

2 Aspirin

Use a conditional variance-covariance matrix: • if downside risk control is paramount• when stress-testing portfolios

Significant serial correlation in volatility: around 0.5 for larger markets, 0.3-0.4 for most other markets.• ARCH, GARCH, SWARCH, M ARCH etc.

prescribed by some researchers (see references).

c

23

Sectors vs Countries

Many of the same arguments as for deteriorating ability to diversify.

Premise: stocks more correlated with other stocks in same sector in any market than they are with other stocks in their own market but in different sectors.

Self-fulfilling prophesy?

24

Counterarguments

Structural differences among countries-- taxes, government, regulation, labor, language, accounting,

Asynchronous business cycles,

Asymmetric impact of single monetary policy,

Currency alone does not a single market make (Hong Kong ),

Sell-side ploy.

25

Countries vs. Sectors

Are correlations stronger:• within the same sector across countries?• within the same country across sectors?

I.E. is the German Financial sector more correlated with:• French financials, Swiss Financials, Dutch financials …• or German Utilities, German Consumer Cyclicals …

Use a scatterplot to compare the average correlation for each country--sector index with the indices for:• other countries--same sector• same country--other sectors

26

.7.6.5.4.3.2.10.0

.7

.6

.5

.4

.3

.2

.1

0.0

1/90 - 10/00

countriesmore

important

sectorsmore

important

Average correlation across countries in same sectorρ(GerFinl,FraFinl), ρ(GerFinl,ItaFinl), ρ(GerFinl,NetFinl) ...

ρ(G

erFi

nl,G

erU

til),

ρ(G

erFi

nl,G

erC

onsC

yc),.

..A

vera

ge c

orre

latio

n ac

ross

sec

tors

in s

ame

coun

try

Source: BB&K

Countries vs. Sectors

ITresourcesbasic industriesgeneral industrialsconsumer cyclicalsconsumer noncyclicalscyclical servicesnoncyclical servicesfinancialsutilities

AustriaBelgiumFranceGermanyItalyNetherlandsSpainFinlandIreland

27

Countries vs. Sectors1/98 - 10/00

.7.6.5.4.3.2.10.0

.7

.6

.5

.4

.3

.2

.1

0.0

countriesmore

important

sectorsmore

important

Average correlation across countries in same sectorρ(GerFinl,FraFinl), ρ(GerFinl,ItaFinl), ρ(GerFinl,NetFinl) ...

ρ(G

erFi

nl,G

erU

til),

ρ(G

erFi

nl,G

erC

onsC

yc),.

..A

vera

ge c

orre

latio

n ac

ross

sec

tors

in s

ame

coun

try

Source: BB&K



28

“Risk Map”

Plot each series as a point (x,y) so that proximity on the map corresponds to strength of correlation.

Effectively a least squares fit withρAB = correlation of series A and B zAB = (xA-xB)2+(yA-yB)2

Code the points on the plot & look for clustersby color for countryby shape for sector

Specifically which indices are most/least alike?

29

“Risk Map” 1/90-10/00

2.52.01.51.0.50.0-.5-1.0-1.5-2.0

2.0

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

-2.0

-2.5

Proximity on plot indicates correlation

Source: BB&K



30

“Risk Map” 1/98-10/00

2.01.51.0.50.0-.5-1.0-1.5-2.0-2.5

2.5

2.0

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

-2.0

Proximity on plot indicates correlation

Source: BB&K



31

Opportunity

991210981211

971212961213

951215941216

931217921218

911220901221

891222881223

871225861226

851227841228

831230821231

820101810102

800104790105

780106770107

760109750110

740111730112

108

6

4

2

1.8

.6

.4

.2

.1

Ratio of weekly cross-sectional dispersion in European country indicesto weekly cross-sectional dispersion in European sector indices.

Country approach more favorable

Sector approach more favorableOverall c70 i30 pre ‘85 c57 i43) Source: BB&K

32

Observations

Evidence in the Countries vs. Sectors debate fluctuates over time.

Last two years, the balance of opportunity (as well as sentiment), has shifted to sectors.

Overall, countries have represented the greater opportunity (70/30),

Even pre-1985 when countries were not as dominant. (57/43)

33

References, Further Reading“Pitfalls in Tests for Changes in Correlations,” Boyer, Brian H, Michael S Gibson and Mico Loretan, Board of Governors of the Federal Reserve System International Finance Discussion Papers Number 597, December 1997.• how conditioning on events affects correlations (selection bias)

“Evaluating ‘Correlation Breakdowns’ During Periods of Market Volatility,” Loretan, Mico and William B. English, Board of Governors of the Federal Reserve System International Finance Discussion Papers Number 658, February 2000.• If “contagion” is defined as an elevation in correlations between asset returns,

then contagion is a natural by-product of temporal variation in volatilities.

“Is the Correlation in International Equity Returns Constant: 1960-1990?” Longin, Francois, BrunoSolnik, CEPR Financial Markets Paper, RePEc:cpr:ceprfm:0037, October 1993

“Covariance and Correlation in International Equity Returns: A Value-at-Risk Approach,” Campbell, Rachel, Kees Koedijk, Paul Kaufman May 2000• fat tails in return distribution are better fit by student-t distribution than normal distribution.

“Correlation in International Equity and Currency Markets: A Risk Adjusted Perspective,” Sheedy, Elizabeth, Centre for Studies in Money, Banking and Finance [CMBF] Paper No. 17, June 1997• accounting for volatility clustering effectively eliminates structure in return correlation

GARCH-CC, BEKK, several other models evaluated• daily data significantly improve portfolio efficiency (Sharpe ratio), even when portfolio

adjustments are made only monthly.

34

. . .

“Do World Markets Still Serve as a Hedge?” Erb, Claude B., Campbell R. Harvey, Tadas E. Viskanta, Journal of Investing, Fall 1995 pp26-42• developed and emerging market stocks & bonds• correlations higher for extreme moves, especially negative moves• currency hedging increases correlation

“International Market Correlation and Volatility,” Solnik, Bruno, Cyril Boucrelle, Yann Le Fur. Financial Analysts Journal, Sept/Oct 1996• r(US,XXX) monthly 1959-95 correlations increasing slightly• r(US,XXX) weekly 1982-95 correlations not increasing• correlations fluctuate widely over time• correlation increases during volatile markets

“European Equity Markets and the EMU,” Rouwenhorst, K Geert, Financial Analysts Journal, May/June 1999 pp 57-64.• “no evidence supports the disappearance of differences between EMU countries’ equity returns.”

For very interesting work on emerging markets contagion tests, start with MIT Prof Kristin Forbes’ website & papers, with good links, also.

35

Conclusions

• Our ability to diversify international portfolios does not appear to be deteriorating.

• Correlations are inherently higher in extreme periods. – This is consistent with constant correlations. – Even so, worthwhile to diversify.

• Evidence for a permanent switch from countries- to sectors- focus is not compelling.

Documents

International Equity Market Correlations