Portfolio Diversification:

Research Paper N° 32

FAME - International Center for Financial Asset Management and Engineering

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July 2001

HEC-University of Lausanne, CEPR and FAME

Alive and well

Jean-Pierre DANTHINE

In Euroland !Kpate ADJAOUTEHSBC Republic Bank (Suisse) SA

INTERNATIONAL CENTER FOR FINANCIAL ASSET MANAGEMENT AND ENGINEERING 40 bd du Pont d'Arve • P.O. Box 3 • CH-1211 Geneva 4 • tel +41 22 / 312 0961 • fax +41 22 / 312 1026

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PORTFOLIO DIVERSIFICATION: Alive and well in Euroland !

Kpate ADJAOUTÉ Jean-Pierre DANTHINE

July 2001

Portfolio Diversification :

Alive and well in Euroland !

Kpate AdjaoutéHSBC Republic Bank (Suisse) SA

and

Jean-Pierre DanthineUniversity of Lausanne, CEPR and FAME

July 2001

Abstract.

Diversification opportunities in Euroland appear to have improved significantly since the adventof the euro, thus invalidating the prospects identified in the last years of the convergence-to-EMU period. We identify low frequency movements in the time series of return dispersionssuggestive of cycles and long swings in return correlations. The most recent post-euro period isclearly associated with an important upswing with return dispersions exceeding for the first timetheir peaks of the early nineties.

JEL codes: F30;G11;G15 Key words: portfolio diversification, return dispersion, euro

2

Executive summary

What are the implications for financial returns and diversification opportunities in Europe of the

all important process of economic and monetary integration at work since the beginning of the

nineties? This fascinating question had lead us to look at the time evolution of the matrix of

return correlations a few months only after the birth of the euro. With little choice at the time, we

had defined two post-convergence to the euro periods, one defined with the onset of the

Maastricht Treaty in January 1995, the other one beginning in August 1997. The latter was

motivated by poll results indicating that by that date the overwhelming majority of 200 financial

and economic forecasters predicted that all 10 of the first euro countries would indeed join

EMU, with the remaining uncertainty essentially resolved by January 1998. With this limited

post-convergence and post-euro data, we consistently obtained results showing that return

correlations had been higher in the later “convergence” period than in the pre-convergence one.

The picture was unambiguous: the convergence (cum beginning of the euro) period appeared less

favorable to portfolio diversification. We speculated on the causes of this phenomenon and its

implications for the home bias and the country allocation paradigm.

In the present paper, we have revisited this important issue at the light of more recent data

containing almost two and a half years of post-euro return observations. The more recent data

convey a radically different message. Indeed the pure euro sample is characterized by lower

return correlations than those obtained for the immediately preceding period of the same length,

whether these correlations are computed at the country or at the sector level. These conflicting

results have lead us to adopt an alternative, more discriminating, methodology. Focusing on the

time series of return dispersions, we identified interesting low frequency movements in

dispersions suggestive of cycles and long swings in return correlations. The most recent post-euro

period is clearly associated with a significant upswing with dispersions exceeding for the first time

their peaks of the early nineties. In the absence of the later data, the peaks of the early nineties

were conditioning our previous results. Whether the recent increase in dispersions (decrease in

correlations) is indicative of a trend shift, possibly associated with the advent of the euro, or

constitutes a purely cyclical phenomenon, only the future will tell. The fact of the matter is,

however, that our results clearly invalidate the hypothesis, previously entertained, that

diversification opportunities in the Euro-area have been permanently impaired as a consequence

of the process of economic and monetary integration. They strongly confirm, on the other hand,

the superiority of a model where diversification is sought after simultaneously across country and

sector dimensions over the traditional country allocation model. In fact our analysis tend to

3

suggest that the superiority of the former model may have increased over the very recent past,

too recent however to make much of this tantalizing observation.

4

1. Introduction

In a study building on the first few months of the euro (Adjaouté and Danthine, 2000), we found

that the conditions under which portfolio investors diversify across the Euro-area equity markets

had changed materially in the 1990’s. In particular, we identified a significant increase in the

degree of correlation between national stock indices implying that diversification opportunities

had been significantly reduced over that period. We proposed that the process of economic and

monetary integration at work in Euroland since at least the mid-90’s and culminating with the

advent of the euro on January 1, 1999 was the likely culprit. Within this process, however, the

disappearance of currency risk appeared less important for investors than the convergence of

economic structures and/or the homogenisation of economic shocks (across the Euro-15

member states). That is, the increased stock return correlation was as manifest when we

abstracted from currency fluctuations than when they were computed using effective monetary

returns.

We observed that this evolution should mark the end of pure country allocation strategies within

Europe: the increased conformity of stock returns implied that international diversification across

the Euro-area on the basis of a pure country allocation model had increasingly smaller benefits.

We also argued that the changing economic structures within Europe and the disappearance of

currency risks could have lowered the cost of the home bias within Euroland and confirmed this

intuition if the alternative to staying at home was to diversify with the use of a pure country

allocation model. Further analysis however showed that diversification across both countries and

sectors remained the much superior investment strategy and that, in light of this option, the cost

of the home bias continued to be significant in Europe.

In the present paper, we revisit these issues with the benefit of more than two years of data since

the formal advent of the euro. In particular, we construct weekly returns for country and sectors

indices, starting from October 7, 1988 to March 30, 2001 (652 observations); as in our first paper,

we use Datastream total market indices and the full sample is partitioned in appropriate sub-

samples to study the dynamics of the Euro-land investment opportunity set. Although the

effective replicability and investibility of these indices can be questioned, they nevertheless

represent the theoretical ideal benchmarks to assess portfolio diversification opportunities.

5

2. The long sample

We start by taking full advantage of our sample data to revisit some of the questions addressed in

our previous study. We define a pre-convergence period extending from October 7, 1988 to

December 31, 1994, and a convergence period going from the signature of the Maastricht treaty,

January 1, 1995, to the limit of our data sample, March 30, 2001. We concentrate on the variance-

covariance matrix of returns within Euroland. Building on the results of our previous research,

we exclusively take the viewpoint of Euro investors. That is, we abstract from currency risk and

focus on the changes in the correlation of returns expressed at constant (December 31, 1998)

conversion rates.

As mentioned earlier, the full sample consists of weekly index returns from October 7, 1988 to

March 30, 2001, for the ten early entrants in EMU (Luxembourg has been ignored from the

analysis; note that data for Portugal are not available before February 1990). For each country,

the Datastream total market country index returns and sector indices returns are collected.

Datastream defines 10 sector groups by country, leading in theory to 10 country indices and 100

sector indices for the consolidated data sample. However, some sectors do not exist in certain

countries or do not have a sufficient data history to be considered in the study. This is the case

for the utilities sector, which is not constructed for Ireland, or the information technology sector

which is formally in existence in Portugal since July 1999 only. The consolidated data sample,

comprised of 10 country indices and 86 sector indices is summarized in Table 1 below. Table 2

provides summary statistics on annualized weekly returns using country index returns. As Table 2

shows, there is a noticeable dispersion of risks and returns both within each sub-sample and

across sub-samples. In fact, in the pre-convergence period, the minimum average return is –

2.37% (Portugal) versus a maximum return of +12.91% (Austria) while the comparable statistics

are +1.78% (Austria) and 25.36% (Finland), respectively, during the convergence sub-sample.

The strict euro period shows a minimum country return of –9.13% (Belgium) and a maximum of

18.12% (Finland).

Table 1: Consolidated sample information

Sample Range Max. # observationsper series

Min. # observations perseries

Whole 07/10/88 - 30/03/01 652 402Pre convergence 07/10/88 - 30/12/94 326 76ConvergencePre euro

06/01/95 - 30/03/0110/11/96 – 01/01/99

326117

326117

Euro 08/01/99 - 30/03/01 117 117

6

Table 2: Summary statistics on annualized weekly country returns

Austria Belgium Germany Spain Finland France Ireland Italy Netherlands Portugal

PANEL A: PRE CONVERGENCE Mean 12.91 2.03 7.24 2.26 3.77 5.78 7.27 1.60 8.48 -2.37 Median 9.82 2.96 8.81 -1.13 -4.26 12.87 -0.92 7.70 11.26 -2.61 Std. Dev. 22.62 11.71 14.69 16.63 21.96 14.88 16.94 20.47 10.19 14.00 Jarque-Bera 408.39 27.41 23.75 10.17 21.76 7.16 3.38 12.36 15.27 17.91 Probability 0.00 0.00 0.00 0.01 0.00 0.03 0.18 0.00 0.00 0.00 Observations 326 326 326 326 326 326 326 326 326 260

PANELB: CONVERGENCE

Mean 1.78 12.13 12.77 17.45 25.36 16.89 17.11 14.77 16.83 12.39 Median 5.49 19.52 17.97 25.75 45.71 14.38 22.02 9.76 26.32 10.25 Std. Dev. 14.33 15.60 18.52 18.94 34.21 18.27 17.66 21.15 16.85 18.14 Jarque-Bera 34.20 51.39 62.79 73.05 79.09 2.64 153.12 20.31 90.70 313.51 Probability 0.00 0.00 0.00 0.00 0.00 0.27 0.00 0.00 0.00 0.00 Observations 326 326 326 326 326 326 326 326 326 326

PANEL C: STRICT EURO

Mean 0.16 -9.13 4.08 -1.16 18.12 13.24 1.68 5.27 4.91 -4.06 Median 11.09 6.17 13.76 12.25 32.02 15.73 -2.28 17.72 20.53 -5.19 Std. Dev. 14.03 17.02 21.03 18.19 45.40 19.62 18.81 19.18 15.76 16.79 Jarque-Bera 16.67 2.74 0.66 1.09 5.71 1.31 1.18 0.81 3.28 5.44 Probability 0.00 0.25 0.72 0.58 0.06 0.52 0.55 0.67 0.19 0.07 Observations 117 117 117 117 117 117 117 117 117 117

We start by using country index returns to compute the 10x10 unconditional correlation matrix

for the pre convergence period, which runs from October 7, 1988 to December 30, 1994 (326

observations). These correlation pairs are sorted in ascending order and plotted against the

country pairs. The correlation pairs for the convergence period, covering January 6, 1995 to

March 30, 2001 are then computed and plotted along the pre-convergence correlation pairs.

Our results are illustrated in Figure 1. They are entirely in accordance with what was found in our

previous study. Return correlations are significantly and almost uniformly larger in the second

period than in the first. This supports, for an appropriately long sample of post-January 1, 1999

observations, the views expressed before and summarized in our introduction.

7

Figure 1: Evolution of country pair correlations: before and during convergence

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1 5 9 13 17 21 25 29 33 37 41 45

Country pairs

Cor

rela

tions

Pre Convergence

Convergence

We next perform a similar exercise at the sector level. To have a sense of the sector level

dynamics, we focus on all the available sectors within the EMU countries. For each of the

selected sectors, we construct the m x m correlation matrix, where m is the number of countries

in which the sector is available, and conduct the same analysis as the country level correlation.

For example, for the “General industrials” sector, we have used the weekly returns of that sector

in Austria, Belgium and the other eight countries to build our correlation matrix. In the absence

of any country specific dimension, this correlation will be close to 1, because one is asking what is

the correlation of the “General industrial” sector in France with the same sector in Belgium and

so on.

Results from this exercise are fully in line with those obtained above at the country level and in

our previous study at the sector level. They are not reproduced here to preserve space. When the

angle of view is more than 12 years of data and convergence is identified with the advent of the

Maastricht treaty, the evolution of return correlations is unambiguous.

Table 3 displays the result of a formal Jenrich stability test indicating unequivocally that the

matrices of returns differ significantly over the two sub-samples. This is true at the aggregate as

well as the sector levels. At this stage we are led to conclude that the adjunction of more recent

data does not invalidate the results of our previous study.

8

Table 3: Jenrich test of stability of correlation matrices : Large Sample

Countries (DF=45) Sectors(DF = 3655)Larger Sample 172.844 10486.155

(0.0000) (0.0000)The pre convergence sample goes from7/10/88 to 30/12/1994, while the convergence sample runs from

06/01/1995 to 30/03/2001The degrees of freedom are computed as n(n-1)/2 where n =10 for countries and n=86 for sectors

p-values are given in parentheses

3. The euro sample

We now look more closely and in isolation at the post-January 1, 1999 data. This effectively

means that we replace the broader concept of convergence, associated with the notion of

economic integration, with a narrower definition of the EMU process strictly identified with the

advent of the euro. We define a pre-euro period as corresponding to the same length (as our euro

sample) period prior to January 1999, that is, starting from October 11, 1996. That is, we use

returns from January 8, 1999 to March 30, 2001 (117 observations) for the euro period and the

117 returns preceding January 8, 1999 as the pre-euro period. Again we abstract from currency

fluctuations (before January ’99). Figure 2 displays the results obtained at the aggregate (country

indices) level.

Figure 2: Evolution of country pair correlations: pre euro and euro periods

0.000

0.200

0.400

0.600

0.800

1.000

1 5 9 13 17 21 25 29 33 37 41 45

Country pairs

Cor

rela

tions

Pre Euro

Euro

Somewhat to our surprise, the picture is now radically different. Out of 45 pairwise correlations,

36 are lower in the euro period than in the immediately preceding period. This picture is

representative of what we obtain at the sector level for 7 out of 10 sectors. The only clear

exception is the sector labelled “non cyclical services” where correlations have continued to

increase, while the picture is more blurred in the case of the “cyclical services” and “utilities”

sectors. Details are provided in the appendix.

9

Table 4 confirms that the matrices of correlation are significantly different in the two periods

whether at the aggregate or the sector level.

Table 4 : Jenrich test of stability of correlation matrices : Euro sample

Countries (DF=45) Sectors(DF = 3655)Strict euro 121.229 3940.508

(0.0000) (0.0000)

The pre euro sample goes from 11/10/96 to 1/1/99, while the euro sample runs from 08/01/99 to 30/03/2001The degrees of freedom are computed as n(n-1)/2 where n =10 for countries and n=86 for sectors

p-values are given in parentheses

These results indeed raise a challenge since they are in direct contradiction with those obtained

up to now. Of course, we are now dealing with two relatively short sub-periods, possibly affected

by specific events. A long and slow structural evolution, such as the one associated with

economic and monetary integration we thought we had identified when looking at the long

sample, may be temporarily concealed by one-time shocks that could colour a period as short as

two years. An alternative explanation may also come from the existing literature on world market

integration. Bekaert and Harvey (1995) substantiate the time-varying nature of market integration

and if correlations are to be used as quantitative indicators in that sense, then the evidence

presented here thus far conforms to this view. This observation is also in line with the fact that

return correlations are known to have cyclical characteristics. In view of testing this hypothesis,

we now turn our attention to the time evolution of the cross-sectional dispersion of equity

returns for our entire sample.

4. Cross-sectional dispersion of returns

The evidence stemming from the above analysis suggests a possible cyclical behavior of country

and sector correlations. Traditional time series analysis does not however allow to test the

cyclicality of the correlation matrix. A test based on a rolling correlation matrix would make little

sense, since virtually no change would be observed based on conventional testing procedures,

because of overlapping data. On the other hand, the approach based on two adjacent samples

does not provide any insight into cycles. The strategy we follow to shed light on the existence of

cycles is based on the notion of cross-sectional dispersion introduced by Roulet and Solnik

(2000). The idea behind cross-sectional dispersion is very intuitive and works as follows.

10

Consider n financial assets over a particular investment period; the more dispersed their returns

turn out to be, the more scope there is for portfolio diversification. If on the other hand, the

dispersion of returns is small, the more similar these asset returns are and the less room there is

for diversification. Given that this dispersion is defined in terms of the n assets existing at time t,

a time series of cross-section dispersion of returns can be generated and its properties analyzed in

the standard time series framework. In particular, in the case of country indices with weekly

observations, a standard deviation across the ten index returns can be calculated each week, so

that using the whole sample data at hand we have a sample of 652 weekly dispersions. Similarly,

the 86 sector returns can be used each week to generate a sector cross-sectional standard

deviation. Table 5 below gives a summary of the computed dispersions while Figure 3 displays

the results of this computation by country and sectors. As discussed by Roulet and Solnik (2000),

there is a direct and inverse relation between dispersion and global correlation. Higher dispersion

implies lower correlation and higher diversification gains and conversely so in the presence of

lower dispersion (and higher correlation).

Table 5: Summary statistics on cross-sectional dispersion

Country SectorMean 1.670 2.943

Median 1.525 2.725Maximum 5.070 7.938Minimum 0.441 1.415

Observations 652 652

Figure 3: Country and Sector Cross-sectional Dispersions

0123456789

10/0

7/19

88

11/2

4/19

89

1/11

/199

1

2/28

/199

2

4/16

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3

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4

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98

1/28

/200

0

3/16

/200

1

Date

Disp

ersio

n (%

)

Country_Disp.

Sector_Disp

11

Figure 3 displays the impressive high frequency variability of the weekly dispersion of returns.

While the variability of weekly dispersion dwarfs lower frequency movements, periods of high

dispersion followed by periods of low dispersion can be identified as well. This pattern is

suggestive of cycles in the time series behaviour of cross-sectional dispersions. Observe that the

sector cross-sectional dispersions are almost systematically above the country dispersions (there is

a total of 5 exceptions altogether): as far as risk diversification is concerned, diversification across

countries and sectors remain the much superior alternative.

In order to enlighten the cylicity issue, a trend has to be extracted from this very volatile data. We

apply the Hodrick-Prescott methodology with smoothing parameters of 270 000 and 28 800.

Results, displayed in Figures 4 and 5, provide a consistent message. The following observations

can be made.

1. There are significant low frequency movements in return dispersions, both at the sector

and the country level.

2. The earlier part of our sample, corresponding with the pre-convergence periods, was

marked by two peaks in both country and sector dispersions, leading to a high average

dispersion level.

3. Return dispersions were below average in the middle of the nineties reaching a trough in

late 1996. They have been mostly increasing ever since.

4. In 2000 and 2001, thus for the bulk of the euro-period, return dispersions have been

above their previous peak levels reached in 1990 and 1993.

5. The post-1996 upswing in return dispersions may not have reached its summit at the

country level; it appears to have peaked in mid-2000 at the sector level.

6. Dispersions, once smoothed out, are always higher at the sector level; the difference

between sector dispersions and country dispersions appears to have increased in the latter

part of our sample.

All in all, the more discriminating approach used in this section permits shedding light on our

previous and current results. When we use sample data terminating in April 1999, the correlation

comparisons are conditioned by the two peaks in the early part of the sample and the long

downswing of the mid-nineties, leading to a diagnosis of decreasing dispersions and thus

increasing correlations (from the pre-convergence to the convergence period). This explains our

earlier results. The short euro sample, on the other hand, clearly places the stress on the above

average dispersions of the last few years and thus supports the opposite diagnosis obtained in

Section 3. Table 6 formally documents that the median country dispersion slightly decreased

12

between the pre-convergence to the convergence period while there was a much more significant

upward jump from the pre-euro to the euro period. The dispersion approach illustrated in

Figures 4 and 5 invalidates the hypothesis that diversification opportunities in the Euro-area have

been permanently impaired as a result of the process of economic and monetary integration. In

view of the low frequency movements in the dispersion of returns, two conclusions are equally

plausible: increased economic and monetary integration is the cause of a permanent increase in

the average dispersion level auguring well of diversification opportunities in the new Euro-area;

alternatively and equally likely, the recent upswing in dispersions is a purely cyclical event,

unrelated to the underlying process of integration, and likely to give way to future phases of

lower return dispersions.

Figure 5: Filtered country and sector global dispersions: HP Filter = 28800

00.5

11.5

22.5

33.5

44.5

10/0

7/19

88

9/22

/198

9

9/07

/199

0

8/23

/199

1

8/07

/199

2

7/23

/199

3

7/08

/199

4

6/23

/199

5

6/07

/199

6

5/23

/199

7

5/08

/199

8

4/23

/199

9

4/07

/200

0

3/23

/200

1

Date

Dis

pers

ion

(%)

Disp_cty_hp28800

Disp_sec_hp28800

Figure 4: Filtered country and sector global dispersions: HP filter = 270000

012345

10/0

7/19

88

10/2

0/19

89

11/0

2/19

90

11/1

5/19

91

11/2

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92

12/1

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93

12/2

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94

1/05

/199

6

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/199

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/200

0

3/09

/200

1

Date

Filt

ered

dis

pers

ion

Filt_ Cty_ Disp

Filt_Sec_Disp

13

Table 6: Statistics on country and sector dispersions by sub-sample period

Pre convergence Convergence Pre euro EuroCountry Sector Country Sector Country Sector Country Sector

Median 1.573 2.661 1.488 2.828 1.456 2.758 1.806 3.592 Maximum 4.410 7.625 5.070 7.938 4.687 6.169 5.070 7.938 Minimum 0.441 1.504 0.557 1.416 0.684 1.442 0.749 1.923

5. Conclusions

What are the implications for financial returns and diversification opportunities in Europe of the

all important process of economic and monetary integration at work since the beginning of the

nineties? This fascinating question had lead us to look at the time evolution of the matrix of

return correlations a few months only after the birth of the euro. With little choice at the time, we

had defined two post-convergence to the euro periods, one defined with the onset of the

Maastricht Treaty in January 1995, the other one beginning in August 1997. The latter was

motivated by poll results indicating that by that date the overwhelming majority of 200 financial

and economic forecasters predicted that all 10 of the first euro countries would indeed join

EMU, with the remaining uncertainty essentially resolved by January 1998. With this limited

post-convergence and post-euro data, we consistently obtained results showing that return

correlations had been higher in the later “convergence” period than in the pre-convergence one.

The picture was unambiguous: the convergence (cum beginning of the euro) period appeared less

favorable to portfolio diversification. We speculated on the causes of this phenomenon and its

implications for the home bias and the country allocation paradigm.

In the present paper, we have revisited this important issue at the light of more recent data

containing almost two and a half years of post-euro return observations. The more recent data

convey a radically different message. Indeed the pure euro sample is characterized by lower

return correlations than those obtained for the immediately preceding period of the same length,

whether these correlations are computed at the country or at the sector level. These conflicting

results have lead us to adopt an alternative, more discriminating, methodology. Focusing on the

time series of return dispersions, we identified interesting low frequency movements in

dispersions suggestive of cycles and long swings in return correlations. The most recent post-euro

period is clearly associated with a significant upswing with dispersions exceeding for the first time

their peaks of the early nineties. In the absence of the later data, the peaks of the early nineties

were conditioning our previous results. Whether the recent increase in dispersions (decrease in

14

correlations) is indicative of a trend shift, possibly associated with the advent of the euro, or

constitutes a purely cyclical phenomenon, only the future will tell. The fact of the matter is,

however, that our results clearly invalidate the hypothesis, previously entertained, that

diversification opportunities in the Euro-area have been permanently impaired as a consequence

of the process of economic and monetary integration. They strongly confirm, on the other hand,

the superiority of a model where diversification is sought after simultaneously across country and

sector dimensions over the traditional country allocation model. In fact our analysis tend to

suggest that the superiority of the former model may have increased over the very recent past,

too recent however to make much of this tantalizing observation.

References

Adjaoute, Kpate, and Jean-Pierre Danthine, (2000), “EMU and Portfolio DiversificationOpportunities”, FAME Research Paper # 31, International Center FAME, Geneva

Bekaert, Geert, and Campbell R. Harvey, (1997), “Time-varying World Market Integration”,Journal of Finance, 50, 403-444.

Solnik, Bruno, and Jacque Roulet, (2000), “Dispersion as Cross-Sectional Correlation”, FinancialAnalysts Journal, January/February 54-61.

Appendix

In this appendix we present the evolution of sector pair correlations for the pre-euro and theeuro periods. Our results apply for a sector decomposition across 10 broad and non-overlappingsectors. With the exception of the “Non-cyclical services” sector, and to a lesser extent “Cyclicalservices” and “Utilities”, pairwise correlations appear to have decreased from the pre-euro to theeuro period.

Figure A1: Evolution of sector pair correlations, basic industries: pre Euro and Euro periods

0.000

0.200

0.400

0.600

0.800

1 5 9 13 17 21 25 29 33 37 41 45

Sector pairs

Cor

rela

tions

Pre Euro

Euro

15

Figure A2: Evolution of pair correlations, cyclical consumer goods: pre Euro and Euro periods

-0.200

0.000

0.200

0.400

0.600

0.800

1 3 5 7 9 11 13 15 17 19 21

Sector pairs

Cor

rela

tions

Pre Euro

Euro

Figure A3: Evolution of pair correlations, cyclical services: pre Euro and Euro periods

0.000

0.200

0.400

0.600

0.800

1 5 9 13 17 21 25 29 33 37 41 45Sector pairs

Cor

rela

tions

Pre Euro

Euro

Figure A4: Evolution of pair correlations, general industrials: pre Euro and Euro periods

-0.200

0.000

0.200

0.400

0.600

0.800

1 5 9 13 17 21 25 29 33 37 41 45

Sector pairs

Cor

rela

tions

Pre Euro

Euro

Figure A5: Evolution of pair correlations, information technology: pre Euro and Euro periods

0.000

0.200

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Figure A6: Evolution of pair correlations, non-cyclical services: pre Euro and Euro periods

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Figure A7: Evolution of pair correlations, non-cyclical consumer goods: pre Euro and Euro periods

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Figure A8: Evolution of pair correlations, resources: pre Euro and Euro periods

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Figure A9: Evolution of pair correlations, financials: pre Euro and Euro periods

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Figure A10: Evolution of pair correlations, utilities: pre Euro and Euro periods

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RESEARCH PAPER SERIES

Extra copies of research papers will be available to the public upon request. In order to obtain copies of past or future works, please contact our office at the location below. As well, please note that these works will be available on our website, www.fame.ch, under the heading "Research" in PDF format for your consultation. We thank you for your continuing support and interest in FAME and look forward to serving you in the future.

N° 31: EMU and Portfolio Diversification Opportunities

Kpate ADJAOUTÉ, Morgan Stanley Capital International, Geneva and Jean-Pierre DANTHINE, University of Lausanne, CEPR and FAME, April 2000

N° 30: Serial and Parallel Krylov Methods for Implicit Finite Difference Schemes Arising in Multivariate Option Pricing Manfred GILLI, University of Geneva, Evis KËLLEZI, University of Geneva and FAME, Giorgio PAULETTO, University of Geneva, March 2001

N° 29: Liquidation Risk Alexandre ZIEGLER, HEC-University of Lausanne, Darrell DUFFIE, The Graduate School of Business, Stanford University; April 2001

N° 28: Defaultable Security Valuation and Model Risk Aydin AKGUN, University of Lausanne and FAME; March 2001

N° 27: On Swiss Timing and Selectivity: in the Quest of Alpha François-Serge LHABITANT, HEC-University of Lausanne and Thunderbird, The American Graduate School of International Management; March 2001

N° 26: Hedging Housing Risk Peter ENGLUND, Stockholm School of Economics, Min HWANG and John M. QUIGLEY, University of California, Berkeley; December 2000

N° 25: An Incentive Problem in the Dynamic Theory of Banking Ernst-Ludwig VON THADDEN, DEEP, University of Lausanne and CEPR; December 2000

N° 24: Assessing Market Risk for Hedge Funds and Hedge Funds Portfolios François-Serge LHABITANT, Union Bancaire Privée and Thunderbird, the American Graduate School of International Management; March 2001

N° 23: On the Informational Content of Changing Risk for Dynamic Asset Allocation Giovanni BARONE-ADESI, Patrick GAGLIARDINI and Fabio TROJANI, Università della Svizzera Italiana; March 2000

N° 22: The Long-run Performance of Seasoned Equity Offerings with Rights: Evidence From the Swiss Market Michel DUBOIS and Pierre JEANNERET, University of Neuchatel; January 2000

N° 21: Optimal International Diversification: Theory and Practice from a Swiss Investor's Perspective Foort HAMELINK, Tilburg University and Lombard Odier & Cie; December 2000

N° 20: A Heuristic Approach to Portfolio Optimization Evis KËLLEZI; University of Geneva and FAME, Manfred GILLI, University of Geneva, October 2000

N° 19: Banking, Commerce, and Antitrust Stefan Arping; University of Lausanne; August 2000

N° 18: Extreme Value Theory for Tail-Related Risk Measures Evis KËLLEZI; University of Geneva and FAME, Manfred GILLI, University of Geneva; October 2000

INTERNATIONAL CENTER FOR FINANCIAL ASSET MANAGEMENT AND ENGINEERING 40 bd du Pont d'Arve • P.O. Box 3 • CH-1211 Geneva 4 • tel +41 22 / 312 0961 • fax +41 22 / 312 1026

http://www.fame.ch • e-mail: admin@fame.ch

N° 17: International CAPM with Regime Switching GARCH Parameters Lorenzo CAPIELLO, The Graduate Institute of International Studies; Tom A. FEARNLEY, The Graduate Institute of International Studies and FAME; July 2000

N° 16: Prospect Theory and Asset Prices Nicholas BARBERIS, University of Chicago; Ming HUANG, Stanford University; Tano SANTOS, University of Chicago; September 2000

N° 15: Evolution of Market Uncertainty around Earnings Announcements Dušan ISAKOV, University of Geneva and FAME; Christophe PÉRIGNON, HEC-University of Geneva and FAME; June 2000

N° 14: Credit Spread Specification and the Pricing of Spread Options Nicolas MOUGEOT, IBFM-University of Lausanne and FAME, May 2000

N° 13: European Financial Markets After EMU: A First Assessment Jean-Pierre DANTHINE, Ernst-Ludwig VON THADDEN, DEEP, Université de Lausanne and CEPR and Francesco GIAVAZZI, Università Bocconi, Milan, and CEPR; March 2000

N° 12: Do fixed income securities also show asymmetric effects in conditional second moments? Lorenzo CAPIELLO, The Graduate Institute of International Studies; January 2000

N° 11: Dynamic Consumption and Portfolio Choice with Stochastic Volatility in Incomplete Markets George CHACKO, Harvard University; Luis VICEIRA, Harvard University; September 1999

N° 10: Assessing Asset Pricing Anomalies Michael J. BRENNAN, University of California, Los Angeles; Yihong XIA, University of California, Los Angeles; July 1999

N° 9: Recovery Risk in Stock Returns Aydin AKGUN, University of Lausanne & FAME; Rajna GIBSON, University of Lausanne; July 1999

N° 8: Option pricing and replication with transaction costs and dividends Stylianos PERRAKIS, University of Ottawa; Jean LEFOLL, University of Geneva; July1999

N° 7: Optimal catastrophe insurance with multiple catastrophes Henri LOUBERGÉ, University of Geneva; Harris SCHLESINGER, University of Alabama; September 1999

N° 6: Systematic patterns before and after large price changes: Evidence from high frequency data from the Paris Bourse Foort HAMELINK, Tilburg University; May 1999

N° 5: Who Should Buy Long-Term Bonds? John CAMPBELL, Harvard University; Luis VICEIRA, Harvard Business School; October 1998

N° 4: Capital Asset Pricing Model and Changes in Volatility André Oliveira SANTOS, Graduate Institute of International Studies, Geneva; September 1998

N° 3: Real Options as a Tool in the Decision to Relocate: An Application to the Banking Industry Pascal BOTTERON, HEC-University of Lausanne; January 2000

INTERNATIONAL CENTER FOR FINANCIAL ASSET MANAGEMENT AND ENGINEERING 40 bd du Pont d'Arve • P.O. Box 3 • CH-1211 Geneva 4 • tel +41 22 / 312 0961 • fax +41 22 / 312 1026

http://www.fame.ch • e-mail: admin@fame.ch

N° 2: Application of simple technical trading rules to Swiss stock prices: Is it profitable ? Dušan ISAKOV, HEC-Université de Genève; Marc HOLLISTEIN, Banque Cantonale de Genève; January 1999

N° 1:

Enhancing portfolio performance using option strategies: why beating the market is easy. François-Serge LHABITANT, HEC-University of Lausanne; December 1998

International Center FAME - Partner Institutions

The University of Geneva The University of Geneva, originally known as the Academy of Geneva, was founded in 1559 by Jean Calvin and Theodore de Beze. In 1873, The Academy of Geneva became the University of Geneva with the creation of a medical school. The Faculty of Economic and Social Sciences was created in 1915. The university is now composed of seven faculties of science; medicine; arts; law; economic and social sciences; psychology; education, and theology. It also includes a school of translation and interpretation; an institute of architecture; seven interdisciplinary centers and six associated institutes.

More than 13’000 students, the majority being foreigners, are enrolled in the various programs from the licence to high-level doctorates. A staff of more than 2’500 persons (professors, lecturers and assistants) is dedicated to the transmission and advancement of scientific knowledge through teaching as well as fundamental and applied research. The University of Geneva has been able to preserve the ancient European tradition of an academic community located in the heart of the city. This favors not only interaction between students, but also their integration in the population and in their participation of the particularly rich artistic and cultural life. http://www.unige.ch The University of Lausanne Founded as an academy in 1537, the University of Lausanne (UNIL) is a modern institution of higher education and advanced research. Together with the neighboring Federal Polytechnic Institute of Lausanne, it comprises vast facilities and extends its influence beyond the city and the canton into regional, national, and international spheres. Lausanne is a comprehensive university composed of seven Schools and Faculties: religious studies; law; arts; social and political sciences; business; science and medicine. With its 9’000 students, it is a medium-sized institution able to foster contact between students and professors as well as to encourage interdisciplinary work. The five humanities faculties and the science faculty are situated on the shores of Lake Leman in the Dorigny plains, a magnificent area of forest and fields that may have inspired the landscape depicted in Brueghel the Elder's masterpiece, the Harvesters. The institutes and various centers of the School of Medicine are grouped around the hospitals in the center of Lausanne. The Institute of Biochemistry is located in Epalinges, in the northern hills overlooking the city. http://www.unil.ch The Graduate Institute of International Studies The Graduate Institute of International Studies is a teaching and research institution devoted to the study of international relations at the graduate level. It was founded in 1927 by Professor William Rappard to contribute through scholarships to the experience of international co-operation which the establishment of the League of Nations in Geneva represented at that time. The Institute is a self-governing foundation closely connected with, but independent of, the University of Geneva. The Institute attempts to be both international and pluridisciplinary. The subjects in its curriculum, the composition of its teaching staff and the diversity of origin of its student body, confer upon it its international character. Professors teaching at the Institute come from all regions of the world, and the approximately 650 students arrive from some 60 different countries. Its international character is further emphasized by the use of both English and French as working languages. Its pluralistic approach - which draws upon the methods of economics, history, law, and political science -reflects its aim to provide a broad approach and in-depth understanding of international relations in general. http://heiwww.unige.ch