The WACC for Dutch
Companies

PREPAR[D FOR

ACM

PREPARED BY
Dan Harris
Richard Caidweli
Ying-Chin Chou

3d

July 2015

THE

Braille GROUP

Drink Water

This report was prepared for the ACM. All resuits and any errors are the responsibility
authors and do not represent the opinion of The Brattle Group, mc. or its clients.
Copynght© 2015 The Brottie Group. mc.

of the

Toble of Contents
1.

INTRODUCTION AND SUMMARY

2

II.

SELECTION OF PEERS

4

II.A. Liquidity and Revenue Tests

5

III.

GEARING AND CREDIT RATING

8

IV.

RISK FREE RATE

12

V.

COST OF DEBT

13

V.A. Spread on the Generic Industry Bonds

13

V.B. Spread On the Comparable Bonds

14

VI.

V.B.1.

Generic Utility

14

V.B.2.

Firms engaged in sirnilar activities to drinking water distribution

14

V.C. Conciusions on Debt Spreads

16

COST OF EQUITY

19

VI.A.Market Indices

19

VI.B. Peer Group Equity Betas

19

VI.B.1.

Dimson Adjustments

21

VI.B.2.

Vasicek Correction

22

VI.C. Peer Group Asset Betas

23

VI.D.Asset Beta for Dutch Water Distribution

25

V1.E. Equity Beta for Water Distribution

27

VI.F. The Equity Risk Premium

27

VII. WEIGHTED AVERAGE COST OF CAPITAL

31

APPENDIX 1- STATISTICAL RELIABILITY BETA

32

APPENDIX II BONDS ISSUED BY FIRMS ENGAGED IN SIMILAR ACTIVITIES TO
DRINKING WATER DISTRIBUTION

35

-

1

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1. INTRODUCTION AND SUMMARY
The Dutch Authority for Consumers and Markets (ACM) has commissioned The
Branie
Group to calculate the Weighted Average Cost of Capital (WACC) for drinking
water
distribution companies in the Netherlands. The purpose of the WACC calculation
is to
estimate an allowed return in the context of future price controls.
The ACM has instructed us to calculate the WACC using a methodology that complies with
the relevant decree and ministerial ruling.’ This report therefore represents an update of our
previous advice on the WACC for drinking water distribution, dated 28hI June 2013.2

In
preparing our estimate of the WACC, we use data up to and inciuding March 2015, this being
the most recent data available at the time we started the work. In broad terms, the
methodology we apply estimates the WACC by applying the Capital Asset Pricing Model
(CAPM) to calculate the cost of equity. The risk-free rate is calculated based on the two-year
and five-year average yield on 10-year Dutch government bonds. This resuits in a risk-free
rate of 1.83%.
The Equity Risk Premium (ERP) is calculated using long-term historical data on the excess
return of shares over long-term bonds, using data from European markets. Specifically. the
methodology requires that the projected ERP should be based on the average of the
arithmetic and geometric realised ERP. The methodology also takes note of other estimates of
the ERP, from for example, dividend growth models, on deciding whether any adjustments
need to be made to the final ERP.
3 Based on the available data, we conciude that 5.0%
represents the best estimate of the ERP.

‘The 2
‘Drinkwaterbesluit and the ‘Drinkregelin,g’.
2

The WACC for Dutch Drink Water Companies’, Dan Harris, Renato Pizzolla, The Brattle Group, 28t1
June 2013. Hereafter referred to as the June 2013 WACC report.
Note that the methodology that we apply in this report to estimate the WACC for water distribution
(the ‘Water WACC methodology’) is similar to the ACM’s WACC methodology for electricity and
gas dist.ributionltransmission (the ‘energy networks WACC methodology’) the main differences
being that: a) the Water WACC methodology uses only Dutch government bonds for the risk-free
rate, while the energy networks WACC methodology uses an average of German and Dutch
government bonds. The period over which the yields on these bonds is calculated also differs; and
b) the Water WACC methodology estimates the cost of debt using yields from both specific bonds
and generic bonds, while the energy networks WACC methodology uses only data from generic
bonds.

2

battle.com

The Dutch water firms for which we are estimating the WACC are not publicly traded.
Therefore we have selected a ‘peer group’ of publicly traded water distribution firms, as well
as regulated energy network firms that have similar systematic risk to a regulated water
distribution firm. We use the peer groups to estimate the beta for water distribution. The
methodology specifies that the equity betas are estimated using daily betas taken over three
years and tested for liquidity and statistical robustness. We estimate that the asset beta for
water djstribution in the Netherlands is 0.39, which yields an equity beta of 0.65.
We have examined the gearing and credit ratings of network industries in the peer groups
and for Dutch network flrms. We conciude that a 40% gearing level is a reasonable target for
a Dutch water distribution firm and is consistent with an S&P ‘A’ credit rating.
4
The methodology specifies that the allowed cost of debt should be based on the average cost
of debt for A-rated bonds, and the cost of debt for a group of bonds issued by flrms engaged in
similar activities to drinking water distribution that have a rating at or close to ‘A’

so-called
comparable bonds. We understand that ‘similar activities’ in this context means not only
—

firms undertaking drinking water distribution but also firms engaged in activities such as the
transport and/or distribution of gas and electricity. We identified a group of bonds that fit
these criteria. This methodology results in a pre-tax cost of debt of 2.80%. The cost of debt
inciudes 15 basis points for the cost of issuing debt.
Applying the methodology resuits in an after-tax cost of equity of 5.09% and a nominal pre
tax WACC of 4.17%. Because the Dutch drinking water firms do not corporation pay tax we

apply an effective tax rate of 0%. Table 1 summarises the WACC for Dutch water distribution
and of the inputs which led to the WACC.

Leverage and gearing are usually used interchangeably. Both refer to the percentage of the firm value
that is financed by debt, or the market value of debt divided by the suui of the market value of
debt and the market value of equity.
3 1 brattle.com

Tabe 1: Summary of WACC Calculation
Gearing (DIA) (1)
Gearing (D/E) (21

40.00% Section fl1
66.67% [111(1.11])

Tax rate (3)

0.00% Effective tax rate

Risk free rate (4)
Asset beta 15]
Equity beta (61
ERP 1)

1.83% Section IV
0.39 Table 13
0.65 (SJx(1+(1-13flx(2])
5.00% Section VI.E

ARer-tax cost of equity 18)

5.09% (4)+(5)x(7)

Debt premium 19]
Non-interest fees (101
Pre-tax cost of debt 1111

0.82% Section V
0.15%
2.80% (4)+(9J÷(1O]

Nominal after-tas WACC 1121
Nominal pre-tax WACC (13]

4.17% (1-(1])x18)i-(lJx(1-[3])x(11]
4.17% 1131/(1-131)

II. SELECTION OF PEERS
The Dutch water distribution firms for which we are estimating the WACC are not publicly
traded. Therefore we need to fmnd publicly traded firms which derive the majority of their
profits from water distribution. We cali these firms ‘comparables’ or ‘peers’. We use the peer
groups for two key steps in the WACC calculation:
1. Estimating the beta;
2. Estimating the appropriate level of debt or gearing.
We first identify a group of potential peers. We then apply test to see if the firms’ shares are
sufficiently liquid before deciding on the final peer group.
In determining the number of peers that should be in each peer group, there is a trade-off. On
the one hand, adding more peers to the group reduces the statistical error in the estimate of
the beta. On the other hand, as more peers are added, there is a risk that they may have a
different systematic risk than the regulated firm, which makes the beta estimate worse. In
statistical terms, once we have 6-7 peers in the group the recluction in the error from adding
another firm is relatively small. However, the ACM has expressed a preference for at least 10
peers to be inciuded for the calculation of beta.
To reach the required number of peers we first attempt to inciude companies involved in
similar business lines in the EU. 1f this is not sufficient we use peers from for the US. We have
identified ten listed European water companies which may meet the criteria on sufficient

4 1 brottlecom

revenue and liquidity and are therefore potential peers.
5 However, in anricipation that not all
of the potential peers will meet the criteria to be an actual peer. we inciude two water
companies from the US. For the same reason, we also include seven maor European network
companies as potential peers. Table 2 sumrnarises the potential peers.
Table 2: Firms Selected as Potential Peers
Potential peers

Country

European and US Water Companies
Severn Trent PIC
Pennon Group PLC
Northumbrian Water Group PIC
United Utilities Group PLC
California Water Service Group
Aqua America
Athens Water Supply & Sewage
Tallinna Vesi
Thessaloniki Water and Sewage Company SA
•Dee Valley Group PIC
Eaux de Royan SA
Societe des Eaux de Douai SA

United Kingdom
United Kingdom
United Kingdorn
United Kingdom
United States
United States
Greece
Estonia
Greece
United Kingdom
France
France

European Network Companies
Snam
Terna Rete Elettrica Nazionale
REN Redes Energeticas Nacionais
Red Electrica
Enagas
National Grid
Elia System Operator
-

Italy
Italy
Portugal
Spain
Spain
United Kingdom
Belgium

II.A. LIQUIDITY AND REVENUE TESTS
One of the things that we use the peer group for is estimating the beta for each activity.
Illiquid stocks will tend to underestimate a beta,
6 and so we apply two initial ‘screens’ or

Note that Northumbrian Water Group. which we used as a peer in our June 2013 report. only has
share price data up to 14 October 2011. We discuss the implicatioris of this in section V1.B.
‘For example, suppose that the true beta of a firm was 1.0, so that every day the firin’s true value
moved exactly in line with the market. But the firm’s shares only change price when they are
traded. Suppose that the firm’s shares are traded only every other day. In this case, the firm’s
actual share price will only react to news the day after the market reacts. This will give the
impression that the firm’s value is not well correlated with the market, and the beta will appear to
be less than one. Using weekly returns to calculate beta mitigates this problem, since it is more
likely that the firm’s shares will be traded in the week. However, using weekly returns have other
disadvantages, such as providing 80% less data points over any given period.
5 1 brottle.com

criteria to test whether a firm can be inciuded in our sample for beta.

We first test each firm
to see how frequently its shares are traded, the idea being that more
frequent trading will
give a more reliable beta estimate. Second, we checked peer companies
have annual revenues
of at least €100 million for the last three years. This is a criterion
which we applied in
previous reports for the ACM, the idea being that companies with low
revenue may also be
relatively illiquid.
Figure 1 shows, for the potential beta peers, the percentage of days in which

the arnount of

trading exceeded a given threshold from 1 April 2010 to 31 March 2015.

We have explored
thresholds between €0 and €250,000 of shares traded per day. Clearly, when
the threshold is
zero, all the firms pass the threshold. Six water firms and six networ
k firms exceed the
€250,000 threshold for nearly 100% of the trading days. The Portuguese networ
k firm REN
exceeds the €250,000 threshold by more than 55% and exceeds the 430,000 thresho
ld by 95%.
—

The Greek water firm

—

Athens Water Supply & Sewage exceeds the €250,000 threshold by

33% and exceeds the 450,000 threshold by more than 75%. The rest of
companies

—

the five water
Tallinna Vesi, Thessaloniki Water, Dee Valley, Eaux de Rayan and Eaux de Duai

exceed the €250,000 threshold by less than 10% and exceeds the 430,000 thresho

ld by less

than 50%.
Figure 1: Trading Frequency
—Sevem Trent PIC
—Pennon Group PIC

90%

—Northumbrian Water Group PLC
—United Utikt,es Group PIC
—Cahfomia Water Service Group
70%

—4qua Amenca
—Athens Water Suppty & Sewage

60%

—Tallinna Vesi
—Thessalonlki Water

rso%

—Dec VaIIev Group PIC
—Eaux de Roan 54
Scetc des aux de Douai 54
—Snam

30%

• ----Tema Rete Elettnca Nazionale
-REN

20%

—Red Electrka
10%

—Enagas
Natona$ Grid
0

Elia System Operator

ii

Source: Blocimberg data and Brattie calcufations.

7
N
o trading data for Northumbrian Water Group PLC after 14October2011.
6 1 brottle.com

We defined a share as being sufficiently frequently traded For the purposes of estimating beta
using daily returns if it trades on more than 90% of days in which the market index trades.
Dee Valley, Eaux de Royan and Eaux de Douai failed this test.
Table 3: Liquidity Test

Country
European and IJS Water Companies
Severn Trent PIC
Pennon Group PIC
Northumbrian Water Group PLC
United Utilities Group PLC
California Water Service Group
Aqua America
Athens Water Supply & Sewage
Tallinna Vesi
Thessaloniki Water and Sewage Company SA
DeeValleyGroupPlC
Eaux de Royan SA
Societe des Eaux de Douai SA
European Network Companies
Snam
Terna Rete Elettrica Nazionale
REN Redes Energeticas Nacionais
Red Electrica
Enagas
National Grid
Elia System Operator
-

Liquidity test
Volume as % of days
% of share company
outstanding
traded
[BI
IAI

Average
value
traded (€)
[CI

UK
UK
UK
UK
US
US
6k
EE
6k
UK
FR
FR

1.62%
1.26%
1.71%
1.74%
2.43%
2.17%
0.26%
0.20%
0.16%
0.18%
0.04%
0.04%

100.00% 15,547,344
100.00% 7,953,771
100.00% 8,006,332
100.08% 19,977,836
100.00% 3,313,536
100.00% 12,144,342
97.04%
399,016
99.68%
74,023
96.57%
55,172
72.59%
31,459
36.37%
4,579
16.04%
2,388

IT
IT
PT
ES
ES
UK
BE

1.44%
2.24%
0.31%
3.94%
4.48%
1.09%
0.29%

98.83% 38,436,976
98.83% 29,729,071
99.77%
822,807
99.61% 47,388,257
99.61% 39,274,444
98.52% 65,687.822
99.69% 1,144,403

Notes and sources:
(Aj to [Cl:
Based on data from Bloomberg.
Average data from 01/04/2010 to 31/03/2015.
Northumbrian Water Group PLC was acquired in 2011 so no data after 14 October 2011.

Dee Valley, Eaux de Royan, Eaux de Douai, Tallinna Vesi and Thessaloniki Water all had
revenues less than €100 million. We exclude these five companies from the peers. After
applying our two screens, we have five European water companies. two US water companies
8
and seven European network companies for the beta estimation (see Table 5). All these flrms
meet the trading frequency test and the minimum revenue threshold.

Note that in our June 2013 report, we used SJW Corp as one of the US water firms. In this report we
instead use Aqua Anierica. This is because SJW Corp is only active in California, whereas Aqua
America is active across the US. Using two water firms that have their activities concentrated in
California may bias the resuits, and hence we have introduced Aqua Anierica instead of SJW Corp.
7

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Table 4: Annual Revenues
Annual revenue
2013
2012

2011

2010

1,771
1,233
790
1.565

75
22
36
18

1,711
1.159
738
1.513
502
687
359
51
71
21
36
17

1,704
1.069
705
1,573

13

1,832
1,201
827
1.636
584
762
336
53
74
23
36
15

3,881
1,996
756
1,847
1,206
14809
786

3.848
1,845
789
1.758
1.233
14,359
788

3.946
1,733
811
1,769
1.180
13,832
1,228

3,605
1,631
917
1,647
1,118
14,343
1,211

3,508
1.586
977
1.413
982
14007
1,038

2014
European and US Water Companies
Severn Trent PLC GBP million
Pennon Group PI.C GBP million
Northumbnan Water Group PIC GBP million
United Utilities Group PLC GBP million
California Water Service Group USO million
Aqua America USO million
Athens Water Supply & Sewage EUR million
Tallinna Vesi EUR million
Thessaloniki Water and Sewage Company SA EUR milhon
Dee Valley Group PLC GBP million
Eaux de Royan SA EUR million
Societe des taus de Douai SA EUR milhori

1,857
1,321
1,705
597
780
326
53
72
24

35

560
751
353

53

460
660
379
50

77
21
36
16

European Network Companies
Snam EUR million

Terna Rete Elettrica Nazionale EUR million
REN Redes Energeticas Nacionais EUR million
-

Red Electrica EUR million
Enagas EUR mililon
Natlonal Grid GBP million
the System Operator EUR inihhion

Notes and sources.
Financial Times website.
Annual revenues for Northumbrian Water Group come from Bloomberg.

Tabte 5: Peers Considered in the Beta Estimation
Final peers

Country

European Water Companles

Severn Trent PLC
Pennors Group PLC
Northumbrian Water Group PLC
United Utilities Group PLC
Athens Water Supply & Sewage

United Kingdom
United Kingdom
United Kingdom
United Kingdom
Greece

Cahifornia Water Service Group
Aqua America

United States
United States

US Water Companles

European Network Companies
Snam
Terna Rete Elettrica Nazionale
REN Redes Energeticas Nacionais
Red Electrica
Enagas
National Grid
Ehia System Operator
-

Italy
Italy
Portugal
Spairt
Spain
United Kingdom
Belgium

III. GEARING AND CREDIT RATING
The relevant decree state that the financing structure used for calculating the WACC should
be that which is considered reasonable for drinking water companies given the situation on
the financial markets. The explanatory notes to the decree also state that this value may

8

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deviate from the actual equity capital of the Dutch drinking water companies. Given that the
cost of debt will be based on a hrm with an A rating. we interpret this to mean that the
assumed gearing should also be consistent with an A rating. To determine II the observed
average gearing is consistent with an A rating we have investigated the relationship between
gearing and credit rating for a number of network lirms.
We use the gearing ratio as calculated for Q1 2015, which is the latest data available. The
most recent gearing data gives the best estimate of the future gearing, because gearing is very
sensitive to stock prices, and todays stock price is the best estimate of future stock prices. In
contrast, taking a three year average stock price is not a good predictor of future stock prices.
Using the Q1 2015 gearing also ensures that the gearing is consistent with the A credit rating.
since we have checked this for Q1 2015.
Figure 2 illustrates our findings.
9 The sample demonstrates a weak trend of decreasing gearing
ratios with increasing rat ings. The average gearing of the A rated firms is 39% (see also Table
6), the average gearing of firms rated BBB is 44% and the gearing of BB rated firms is 64%.
While this confirms that gearing is an important factor for credit ratings. another factor
driving credit ratings include the sector in which the firm is active and the countries in
which it operates. Four out of six BBB rating companies operated in the countries at the
centre of the Eurozone sovereign debt crisis and were downgraded from rating A between
late 2012 and mid2013.m As of Q4 2012 the average gearing ratio is just over 50% compared
to around 40% in the beginning of 2010, as a result of increasing debts. Since 2013 the gearing
ratios for these companies have been falling as a result of rising share prices. However, the
ratings remained unchanged at BBB. The only BB rated firm, REN (Portugal), was
downgraded from rating A- to BBB in March 2011 and ftirther downgraded to rating BB+ in
February 2012. The gearing ratio in the same period increases from just below 60% to nearly
70%.”

In this exercise we also include two French energy/water companies with woridwide operations,
Veolia Environnment and Suez Environnment. About a third of revenues of Veolia and Suez come
from water-related business in Europe.
‘°

Snam and Terna were downgraded from A- to BBB+ in July 2013. Red Electiica and Enagas were
downgraded from A- to BBB in October 2012.

I

Unlike the other firms, REN’s gearing raüo increased as a result of reductions in its share price.
9

brottle.com

Figure 2: Gearing vs S&P Credit Rating
12
Ratkig A

7O

Rating S88

Ratkig 86
64%

53%

t

.2

4g%

48%

49

8

48%

48%

42%

4O

36%

II illilli 1

t

E

28%

26

—

-.

•

ci

•

%.I 1
59

0II

E

S
e

t

j

1
•e

0

II
.e
-

‘t

t

‘t

0

-

‘t

b

S

t

1

‘

.

‘t

.

“.

m
‘

t
t

-t

t

—

S

t

Source: Bloomberg and Brattie calculations. Rating. S&P. 5&P LT Local Issuer Credit.

AQua America rating comes from Egan-lones for IC senior unsecured; Sut: Environnment Co rating comes from Moodys
for senior
unsecured debt. Convert both ratings to 5&P rating scale.

Table 6: Average Gearing (D/A) of A rated Peers
Gearing
Company
Califomia Water Service Group
Aqua America
National Grici
Elia System Operator
Suez Erivironnement Co

Country

Rating

DIA

United States
United States
United Kingdom
Belgium
France

A+
AAAA-

28.10%
25.96%
38.83%
52.53%
47.58%

Average
Maximum
Minimum

38.60%

52.53%
25.96%

Notes and sources:
Bloomberg.
Rating as of 17April 2015.
Gearing ratio calculated from Q1 2015 data.

In the past other EU regulators have allowed slightly higher gearing levels
65%

—

up to around
in their WACC calculations. However since 2008 firms have generally had to hold less

debt to maintain an investment grade rating. Targeting an A grade rating
but-one credit rating before debt loses its investment-grade status

12

—

—

—

which is the last

seems prudent.

Rating as of 17 April 2015 and gearing ratio for Q1 2015.
10 1 brattle.com

We also note two other factors relevant to Dutch water distribution. Fjrst. Dutch water

distribution lirms pay no tax. This means that one of the main attractions of debt Financing

—

being that interest is tax deductible —bas no relevance for Dutch water firms. As a result, we
might expect Dutch water firms to have less debt than a comparable firm that pays tax.
Second, and relatedly, we understand that there is a requirement that Dutch water
distribution firms are financed by no more than 70% equity, so in other words that they have
at least 30°/â debt.’
3 This places a minimum or floor on the gearing for Dutch water
distribution firms.
We note that the final WACC resuits are not sensitive to the choice of gearing, as long as the

firms maintain an A credit rating. As gearing increases, the proportion of relatively cheap
debt in the WACC formula increases. However, increased debt means more risk for equity
holders, which resuits in a higher equity beta and a higher cost of equity. The cost of debt
will also start to increase. These two effects
equity and eventually debt costs

—

—

more relatively cheap debt versus increasing

largely offset one another.’
4 As long as the target level of

debt and the credit rating assumed are consistent with one another, and the credit rating is
reasonable given that the country in which the firms operate, then the resuking WACC
should be reasonable. For example, we estimate that the WACC varies by only 0.1 percentage
points (10 basis points) as the gearing increases from 35% to 45%.
Given the observed gearing levels of between 26-53%, the need to maintain an A credit
rating and the relative insensitivity of the WACC to the fïnal choice of gearing (as long as it
consistent with an A rating), a gearing level of 40% is consistent with an A credit rating for
regulated water firms operating in the Netherlands. This level of geanng and the target credit
rating are consistent with actual practice of the Dutch network firms For which credit ratings
are available. It is also consistent with Moody’s requirement for gearing to be between 40%
and 55% to qualify for an A-rating.

This applies at least for the period 2012-2015. See letter from the Ministry of Infrastructure and
Enviromnent to the Chair of the Second House of Parliament, subject ‘Vernwgenskasten voer
drinkwaterbedrijven’, dated 28October2013.
14 The insensitivity
of the WACC to the financing choices under certain assumption is known as the
Modigliani—Miller theorem.
‘

IS

Gearing is only one criterion that Moody’s look at when assigning a rating. Hence a firm that scores
an A rating on gearing may obtain a higher or lower rating than A depending on other ratmg
criteria.

11

brottle.com

We note that in our 2013 report we used a gearing ratio of 50%.

However. the in the 2013
report average gearing for A rated firms was actually 46%, which we
rounded up to 50%. The
average gearing is now 39%, which we round up to 40%.

IV. RISK FREE RATE
The methodology specifies that to calculate the risk-free rate, we

must calculate the average
yield on lO-vear Dutch government bonds over the last five years. and
the average over the
last two years. The risk-free rate is then the average of the two-year and five-ye
ar average.
Figure 3 below shows the movement of the yields on 10-year Dutch govern

ment bonds over
the prior flve years. The yields have declined substantially over the five-ye
ar period, from
near 3.5% to less than 0.5% due to the sovereign crisis in the Eurozone and subseq
uent easing
of monetary policy.
The two-year average yield is 1.54%, and the five-year average is higher

at 2.12%. The

average of these two numbers gives a risk-free rate of 1.83%.
Figure 3: Yied on Dutch Government 10 Year Bonds
4.0

3.5

3.0

,.

2.5

2.0

1.5
11

1.0

0.5

0.0
-.
-

-,

.,

‘.

<

cS
Source: Bloomberg.

.,

,‘

,.

.,

-

.

,

.‘,

‘

‘

S’S’

‘S’

S’>

“

‘>
0

‘>
0

(
0
‘>
0

0
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0

‘.,

0

0

12

bralfle.com

V. COST OF DEBT
The method prescribes that we must estirnate the cost of debt for water distribution by
looking at two different sources of debt yields and spreads: I(
1. Yields and spreads on A-rated Euro bonds with a maturity of 10 years, where the
bonds have been issued by firms active in the industry sector. We refer to these yields
and spreads as ‘generic industry’;’
7
2. Yields and spreads on bond issued by firms that engage in activities which are
comparable to that of drinking water companies and which have a rating of A, A+ or
A- and a maturity of around 1 0-years. In our view ‘activities’ which are comparable to
that of drinking water companies’ in this context means not only firms engaged in
drinking water distribution but also firms engaged in activities such as the transport
and/or distribution of gas and electricity. We refer to these as the ‘comparable’ bonds.
In both cases, we calculate two year average and five year average of the differences between
the bond yields and the relevant government bond rates. We descrihe the resuits below.
V.A.SPREAD ON THE GENERIC INDUSTRY BONDS

The method requires the calculation of the spread of the cost of 10-year debt over the risk
free rate. We take the risk-free rate to be the contemporaneous yield on a Dutch government
l0-year bond. The spread is the difference between the yield on the generic A-rated
industrial Euro-denominated debt with 10 years maturity and the contemporaneous yield on
a Dutch government 10-year bond.
Figure 4 illustrates how this spread has developed over the last five years. During the
Eurozone sovereign debt crisis, the spreads reached over 1%, in particular in reaction to the
risk of a Greek default. The spread has since fallen to around 0.5% since 2013. The average
spread over the last five years is 0.75% and the average spread over the last two years is
0.48%. The average of these two numbers gives a spread of 0.61%.

16

By spread we mean the difference between the debt yield and the corresponding risk-free rate.
By ‘generic’, we mean these are yields for a group of A-rated industrial firms calculated by
Bloomberg, where the individual flrms used in the saniple have not been identifled.
13

bi of tle.com

Figure 4: Spread of 1O-year A-rated European Industrial Debt over 1O-year

Dutch Government

bonds
1.5

1

Afl

1.0

O.5
•

:

gpr

0.0

.ç

‘

c’

.
.‘

,

.

,

‘

.Q’

*‘

‘ç
‘

.ç

.

ç3’

c

Source: Bloomberg and BratIe calculations

V.B. SPREAD ON THE COMPARABLE BONDS

We considered two sources of ‘comparable’ bonds: a generic utility bond and individual
bonds issued by firms engaged in similar activities to drinking water distribution.
V.B.1. Generic Utility

We took the difference between the yield on the generic A-rated utility Euro-denominated
debt with 10 years maturity and the contemporaneous yield on a Dutch government 10-year
bond. The average spread for thè generic A-rated EUR utility bonds was 0.89% over the last
five years and 0.62% over the last two years. The average of these two numbers gave a spread
of 0.75%.

V.B.2. Firms

engaged

distribution

in

simliar

activifies

to

drinking

water

We identified a ‘long-list’ of 1,578 issuers whose bonds are traded and who seemed to be
engaged in similar activities to drinking water distribution. This includes water distribution
companies, but also network companies more generally. To increase the sample size we
14 1 bottle.com

considered firms from around the world, and not only Europe, though we limited the
currencies to GB Pounds Sterling, US Dollars, Canadian Dollars and Euros. We then screened
the long-list to find debt which was rated either A, A+ or A- by Stanciard & Poors (S&P). and
had a maturity of between 9 to 11 years during 1 April 2010 to 1 April 2015. We also
eliminated so-called ‘callable bands’,’
8 ‘putable bonds’,’
9 ‘convertible bonds’
° and ‘sinkable
2
’ Applying these criteria reduced the number of possible bonds to 1 11. From the list
2
bonds’.
of 111, we then checked that the firms were really engaged in activities that could be
considered similar to drinking water distribution. Specifically, we checked that most of the
firms’ revenues were derived from regulated activities in energy or water. Applying this
criterion reduced the number of bond issuers to 30 (9 in water and 21 network companies),
and the number of bond issues to 56. Nine of these bond issuers were owned by six water
companies: Anglian Water, Dwr Cymru Welsh Water, Southern Water, Thames Water,
Yorkshire Water and Affinity Water. Appendix II gives details of the firms considered.
We inciude yields during the period when bonds stili have 9 to 11 year maturity and
calculate spreads against yields of relevant government bonds with l0-year maturity. We
decide the relevant government bond based on the country where the business
predominantly operates. For example, for a bond issued by Elia we use a Belgian government
bond of the same outstanding maturity and of the same currency to calculate the spread.
Comparing all corporate bonds to Dutch government bonds could give misleading results.
18

Callable bonds can be redeemed by the issuer prior to maturity and generally attract a higher yield
than bands that mature on a fixed date. Callable bonds cannot be compared on a like-for-like basis
with Government bonds that have a fixed maturity, which is why we do not use them in our
analysis. Callable bonds generally attract a higher yield because bonds are more valuable if interest
rates fall, but in this scenario the callable bond may be re-deemed. Hence the bond holder has an
asymmetric pay-off.

19

Putable bond gives bond holders options to seli back bonds to issuers at one or several specific dates
before maturity. When interest rate arises, investors could exercise such option and use the
proceeds in higher-yield investments. Bond holders are generally wilhing to accept a lower yield to
have such option.

20 Convertible bond is a type of bond that can be converted into equity at certain dates during its life.
Convertible bond usually attracts a lower yield because investors could convert it into stocks and
receive a higher yield when stock price arises.
21

Sinkable bond is a bond issue backed by sinking ftnd, whch sets aside money on a regular basis to
ensure the repayments will be made. Sinkable bond has less risk to investors and allows the issuers
to offer a lower interest rate to bond holders.
Most of the selected bonds are issued in local currency. There are two exceptions: a USD
denominated bond by Nova Gas Transmission (Canada) and a EUR-denominated bond by AusNet
services (Australia). For simplicity we use US government bonds in USD and German government
bond in EUR to calculate the spreads. Excluding these two bands would change the average spread
by less than 0.1% percentage points.
15

bottle.com

This is because the difference between, for example, the yield on Elia’s bonds and

the yield

on Dutch government bonds is partly due to the additional risk that Elia bas as

a company
(corporate risk), and partly due to country risk. 1f the country risk for Belgium
was
significantly higher for Belgium relative to the Netherlands, then the spread between Elia’s
bonds and Dutch government bonds would exaggerate the actual corporate spread, because
it

would include the additional Belgian country risk which is not relevant for Dutch water
companies.
The average spreads for water peers are 1.16% and 0.99% respectively over the last five years
and over the last two years. Both figures are slightly higher than the average spreads for
network peers, 1.03% and 0.99%. However, the number of water peers is relatively small, and
so the finding of a higher debt premium for that group does not statistically significant.
Accordingly, we use the average spread from the larger group of all peers. Over the last five
years this spread is 1.06% and over the last two years it is 0.99%. The average of these two
numbers gives a spread of 1.02%.
Figure 5: Spread of A-rated Peers over Relevant Government Bonds

2.0
1.8

Fve-ear average

1.6

Two-year ave rage

1.4
1.2

-

1 1L
099

1.0

0,8

:: i
0.2

0.0

1

.99

[.1..

Water companles

099

k 1I
Network companles

V.C.CONCLUSIONS ON DEBT SPREADS
Table 7 summarises the debt spreads for the Generic Industry bonds, the Generic Utility
bonds and the individual bonds of the comparable peers. Table 5 shows that the comparable

16

brattle,com

peers have the highest spreads. followed by the Generic Utility bonds and then the Generic
Industrial bonds.
Table 7: The average spreads on the generic industry and comparable bonds

Genenc

Spread
Conrparatiles
Generic Indrvidual

Industry
[Af

utihty

bonds

[8)

[C[

Fivevear averae

[1] Seenote

075%

o 89%

106%

Two-year average

[2) Seenote

048%

062%

099%

Average

[3f «U*[2[)/2

061%

015%

1 02%

Average between generic industry
and comparables

(4) See note

068%

082%

Notes and sources
1) Average spreads from 01/04/2010 to 31/03/2015.
[2) Average spreads from 01/04/2013 to 31/03/2015
[4[[B[. ([3[[A[.[3)[B[)/2
[4j[C[: {[3)[A[v[3[[C[(/2
Af: D4ference between Bloomberg BFV Eurozone A-rated industry 10-year and NL sovereign
10-year
[8): Differerice between Bloomberg BFV Eurotone Arated utility loyear and NL soneregn 10
year.

[t): Difference between bond vreids of selected peers and sovereign bond yields

We suspected that an important part of the difference between the spread on the Generic
Utility bonds and the spread on comparable peers was to do with hquidity. Investors will
generally demand a higher return for bonds that are Iess frequently traded and are therefore
less liquid. This is known as a liquidity premium. To confirm if the difference was indeed due
to a liquidity premium, we asked Bloomberg
lJtility bonds data

—

—

the data provider that compiles the Generic

for the firms which make up the Generic Utility bonds series. As a proxy

for liquidity, we looked at the value of the bonds outstanding, the logic being that larger bond
issues will tend to be more heavily traded and hence more liquid. Fgure 6 shows that the
average value of the outstanding bond issues for the comparable peers is less than half of that
for the bonds Bloomberg used for calculating generic utility yields.
23 We conciude it is likely
that the higher debt spreads for the bonds of comparable peers is because these bonds are less
liquid than the bonds that make up the Generic Utility set.

The bonds selected by Bloomberg change day by day. These are bonds used as of 13 April2015.
17

brott(e.com

Figure 6: Value of outstanding bond issues for various firms
Bonds issuedby comparabte peers

L400

Generic Utility

‘t
1.000

S

800

Water companies

600

400
200

0

1

II

N.twork companies

1
t Ii 1
iii
i
i
ii1
i
i 1 1 1 iU
t

t

Source: Bloomberg and Brattie cakulations

We also understand from the ACM that the Dutch water distribution firms are relatively
small, and finance their activities using bank debt rather than by issuing bonds. 1f the Dutch
water firms were to issue bonds, they would be at the lower end of the scale in terms of

the
size of the issue. The bonds would also be less liquid than average, and we would expect that
they would command some sort of liquidity premium. Given this context, we think it would
be appropriate to calculate the debt spread for Dutch water distribution usmg the simple
average of the 0.61% spread for the generic industry bonds and the 1.02% spread for the
comparable peers. This resuits in an average spread of 0.82%.
Note that in our 2013 WACC estimate calculated the cost of debt using the spread of generic
utilities, rather than the spreads on individual bonds. Specifically, we took the average spread
of generic industrial bonds and generic utility bonds. The latter had a spread of 1.12%. In

the

current report we have replaced the generic utilities by using individual bonds. In 2015,

the
generic utility bonds had a spread of 0.75%, while the individual bonds have a spread
of
1.02%. Therefore, using the spread on individual bonds, rather than the spread on the generic
utility bonds, has increased the allowed cost of debt. In more detail, the simple average
of the
0.61% spread for the genenc industry bonds and the 0.75% spread for the Generic
Utility
bonds resuits in an average spread of 0.68%, rather than the spread of 0.82% that we actually
use.

brottle.com

VI.COST OF EQUITY
The methodology specifies that the cost of equity will be estimated by applying the Capital
Asset Pricing Model. The CAPM expresses the cost of equity for a business activity as the sum
of a risk-free rate and a risk premium. The size of the risk premium depends on the
systematic risk of the underlying asset, or project, relative to the market as a whole.
24
Because the Dutch water distribution firms are not listed on a stock exchange we cannot
measure the systematic risk directly by measuring the covariance of firm value against the
movement of the market as a whole. In the CAPM this covariance is referreci to as beta.
Accordingly, we estimate the systematic risk for Dutch water distribution using our peer
group of firms which are publicly traded and derive the majority of their profits either from
water distribution, or from a regulated network activity which appears to face similar
systematic risk.

VI.A. MARKET INDICES
The relative risk of each peer, as summarised in its beta parameter, must be measured against
an index representing the overall market. We are of the opinion that a hypothetical investor
in a Dutch water firm would likely diversify their portfolio within the single currency zone
so as to avoid exchange rate risk. Accordingly, to calculate betas we use a broad Eurozone
index for the European companies, and a national index for the US companies and a national
index for the UK companies.
25 Using indices of the currency zone or country concerned
avoids exchange rates movements from depressing betas, and should result in a higher beta
estimate than if we estimated betas against an index derived in a different currency.

VI.L

PEER GROUP EQUITY BETAS

The methodology specifies a three year daily sampling period for the beta. We note that of
the firms we used in our June 2013 report, Northumbrian water was acquired and delisted
from the London Stock Exchange as of 14 October 2011.26 To investigate if jt might stili be
possible to use the beta for Northumbrian Water Group calculated in the three-year period
up to October 2011, we plotted the rolling three-year daily betas for Northumbrian Water
Group and other EU water firms. Figure 7 shows that there is an upward trend in the peers’

24

Further information on assumptions and theory underlying the CAPM can be found in most
flnancial textbooks; see Brealey, Myers, Allen, “Princijples of Corporate Finance’
Respectively Euro Stoxx. S&P 500 and FTSE Ali-Share index.

26

Utility Week, ‘Northumbrian Water acquisition by CM takes effect’, 14/10/2011.
brattle.com

betas since late 2014. Therefore, it seems likely that if we used the beta for

Northumbrian
Water Group calculated in the three-year period up to October 2011, it is likely
that we will
underestimate the true beta as of today. Accordingly, we decided to
exclude the
Northumbrian Water Group from the beta calculation.
Figure 7: 3-year rolling beta for water peers
0.8

0.7

5e’.em Trent PIC
—United UtIities Groio PLC

-

--

-—Pennon Group PLC
-CatifomiaWaterSerwce Group

—

Northumbrun Water Group PLC
AuaAmerica

-——Athens Water Supp!y & Sewage

06

os
0.4

03

0.2

0.1

0.0
OÇ
Ç

Ç’

c
Source: Boomberg and Brattie calculations
tt

ç’

‘

Ending period

We perform a series of standard diagnostic tests to assess if the beta estimates satisfy the
standard conditions underlying ordinary least squares regression, which are detailed

in
Appendix 1. Where a sample has either autocorrelation or heteroskedasticity problems, we
perform a Prais—Winsten regression and use the resulting beta and standard error.

20

brattle.com

Table 8: Equity Betas robust to autocorrelation or heteroskedasticity
Beta

Standard
error

Country
European Water Companies
Severn Trent PIC
Pennon Group PLC
United Utilities Group PIC
Athens Water Supply & Sewage

United Kingdom
United Kingdom
United Kingdom
Greece

0.60
0.53
0.59
0.57

0.06
0.05
0.06
0.14

US Water Companies
Catifornia Water Service Group
Aqua America

United States
United States

0.77
0.68

0.06
0.05

European Network Companies
Snam
Terna Rete Elettrica Nazionale
REN Redes Energeticas Nacionais
Red Electrica
Enagas
National Grid
Elia System Operator

Italy
Italy
Portugal
Spain
Spain
United Kingdom
Belgium

0.73
0.72
0.37
0.74
0.63
0.59
0.30

0.04
0.03
0.04
0.05
0.04
0.04
0.03

-

VI.B.1.Dimson Adjustments
When calculating betas using daily returns, there is a risk that the response of a firm’s share
price may appear to react to the market index the day before or the day after. This could
occur because of differences in market opening times and trading hours, or differences in the
liquidity of the firm’s shares vs. the average liquidity of the market. 1f such an effect is
present, it could affect the beta estimate which is calculated using only the correlation
between the return on the firm’s share on day D and the return on the market index on the
same day.
The Dimson adjustment deals with this effect. We start by performing a regression of the
company returns against the market index returns. We inciude in the regression the market
index returns calculated one day before and one day after the company returns.
27 The Dimson
adjusted beta is the sum of the three coefficients calculated by the regression. 1f the market is
perfectly efficient, all information should be dealt with on the same day. 1f the Dimson
adjusted beta estimate is significantly different from the original beta estimate, this suggests
that information about the true beta may be lost by considering only the simple regression.

More days of leads and lags can be applied, but in this case we look at only one.
21 1 brottle.com

We have performed this test for the firms in our peer groups. The Dimso
n adjustment is
significant for three firms Out of the total sample, suggesting that inform
ation on systematic
risk is contained wjthin the adjacent days. Hence for these three firrns we
take the adjusted
beta. For the remaining firms we take the unadjusted beta. Table 9 shows
both the ‘raw’
unadjusted betas and the Dimson-adjusted betas.
Table 9: Raw and Dimson Adjusted Equity Betas
Raw unadjusted
Beta
Standard
error

Oimuon
djustments
Bela
Standard
error

Dimsan adusted
Beta
Standard

Signihcant
Dimson

error

European Water Companies
Severn Trent PLC
Pennon Group PLC
United Utititres Group PIC
Athens Water Supply & Sewage

United Kingdom
United Kingdom
United Kingdorri
Greece

0 50
053
0.59
0 57

0.06
005
0.06
0.14

0.66
0.56
0.55
122

0.10
0.09
0 10
0.22

No
No
No
Yes

0 60
0.53
0 59
1.22

0 06
0.05
0.06
0.22

US Water Companles
Catifornia Water Service Group
Aqua America

United States
United States

0.77
0 68

0.06
0.0$

0.55
0.63

0.09
0.08

Ves
No

0.55
0.68

0.09
0.05

European Network Companles
Snam
Terna Rete Elettrica Nazioriale
REN Redes Energeticas Nacronais
Red Electrica
Enagas
National Grid
Etia System Operator

Italy
Italy
Portugal
Spain
Spain
United Kingdom
Belgium

0.73
0.72
0.37
0,74
0.63
0.59
0.30

0.04
0.03
0.04
0.0$
0.04
0.04
0.03

0.63
067
0.41
0.92
0,74
0.60
0.33

0.06
0.05
0.06
0.09
0.07
0.06
005

No
No
No
Yen
No
No
No

0.73
0.72
0 37
0.92
063
0.S9
0.30

004
0.03
004
0,09
0.04
0.04
0.03

-

VI.B.2.Vasicek Correction

The Vasicek adjustment is a statistical adjustment which aims to avoid extreme estima

tes of
beta, which could be statistically unreliable, by ‘pulling’ beta estimates toward an estima
te of
beta that is thought to be more reliable the ‘prior expectation’ for beta. The metho
dology
applies the Vasicek adjustments to the observed equity betas. In this case, we have
used a
prior expectation of the beta of 1.0, which is the market average. We considered applyi
ng the
critique of La11y, which among other things argues for using a prior expectation of
the beta
which is specific to the activity in question. However, we could find no objective
way of
deterrniriing the prior expectation of beta which was different from the averag
e of our
sample. Accordingly, we have adopted the more neutral assumption of the prior expect
ation
—

of a prior expectation of beta of 1.0.
The Vasicek adjustment moves the observed beta closer to 1 by a weighting based
standard error of the beta, such that values with lower errors will be given
weighting. The prior expectation of the Beta given in other consultant reports is

on the

a higher

1. which we

Lally, Martin, “An Eamjnation ofBlume and Vasicek Betas’: Financial Review, August 1998.
22

brollle.com

apply here. For the prior expectation of the standard error we use the standard error on the
overall market.
29
Table 10 illustrates the effect of the Vasicek adjustment.
Table 10: Effect of the Vasicek adjustment
Dimson adjusted
Beta
Standard

Market average
Beta

Standard
error

error
•
European Water Companies
Severn Trent PIC
Pennen Group PLC
United Utdities Group PIC
Atbrrts Water Suppip & Sewage

AJ

BI

CI

lol

Weighting
Company
Market
beta
beta

Vasicek
Beta

(EI

1Fl

161
0.61
054

United Kngdom

0.60

0.06

97.5%

2.5%

053
0.59

005
0.06

1.00
1.00
1 00

036

United Kingdom
United kingdom

0.36
0 35

98.4%
97.4%

Greece

1.22

0.22

1.00

0.36

73.5%

1.6%
2.6%
26.5%

0.60
1 16

United States

055

0.09

1.00

0.68

0.05

1.00

0.39
0.39

94.8%

United States

98.6%

5,2%
1.4%

0.58
0.69

talp
tap
Portugal
Spain
Span
United Kingdom

0.73
0.72
0.31

0.04

REN Reden Energeticas Nacionais
Bed Electri:a
Enagas
Natiorial Grid

0.03
0.04

0.36
0.36
0.36

0.92
063

0.09
0.04

0.36
0.36

98.9%
99.2%
99.0%
94.5%
98.8%

1.1%
0.8%
1.0%
5.5%
1.2%

Belgium

0.59
0.30

0.04
0.03

0.36
0.36

983%
99.3%

1.2%
0.7%

0.73
0.73
0.38
0.92
0.63
0.60

Elja Systerr Operator

1.00
1.00
LOO
1.00
1.00
1.00
1.00

US Water Companles
Calitornia Water Service Group
Aqua A,nertca
European Network Conipanles
Snam
Terna Rete Elettrica Nazionale
-

Noten and sourtes:
AL BI: Table 9.
ICI (DI: Assurned,
2/aoIn2,181
1o1
2
).
(EI: 5
1Fl: 1-(E(.

(GI: [Aln(EJ*(C(n(F(

VI.C. PEER GROUP ASSEr BETAS
The measured equity beta measures the relative risk of each company’s equity, which will
reflect the financing decisions specific to each company. As debt is added to the company the
equity will become riskier as more cash from profits goes towards paying debt in each year
before dividends can be distributed to equity. With more debt, increases or decreases in a
firm’s profit will have a larger effect on the value of equity. Hence if two firms engage in
exactly the same activity, but one firm has more debt, that firm will have a higher beta than
the firm with less debt.
To measure the relative risk of the underlying asset on a like-for-like basis it is necessary to
‘unlever’ the betas, imagining that the firm is funded entirely by equity. The resuking beta is

The standard error on the FTSE 100 index is used as a proxy for the European market, and is
reported by the LBS. Valueline reports the standard deviation of all stocks in the US market.
As we are using the market average beta for our prior expectation, it is consistent to use the
standard deviation of the distribution of the betas underlying the market population as the prior
expectation of the standard error.
23 1 brottle.com

0.30

referred to as an asset beta or an unlevered beta. To accomplish the un-levering,
methodology specifles the use of the Modigliani and Miller 3
formula. Table
°

the

11 illustrates

both the equity beta and the asset betas for each ru-m.
Table 11 also shows that the asset beta for Athens Water Supply & Sewerage (Athens

Water)

is significantly higher than the asset betas for the other three European water firms

in the

peer group. Figure 7 also shows that the beta for Athens Water has also been very

volatile.

While Athens Water meets our liquidity test, Table 3 shows that that the volume

of shares

traded as a percentage of the shares outstanding are an order of magnitude lower

than for the
other peers that we use. The large effect of the Dimson adjustment on the beta of
Athens
Water shown in Table 9
also indicates that a lot of trading takes place on the days
following a move in the market. While the Dimson adjustment tries to compensate the
beta
-

—

estimate for this effect, it cannot do so perfectly. A more heavily traded stock should

react to
market events on the same day.
’ Our concern is that the high beta for Athens Water is at
3
least partly driven by a lack of liquidity in the shares. The beta may also be affected by events
specific to Greece. In particular, weexpect that as the risk of a Greek default increases, the
beta for Athens Water will also increase. This is because events that affect the value of the
Greek market will start to affect the value of all flrms in Greece to a similar extent, regardless
of the industry that they are in or the fact that the business has regulated 32
returns. We
conciude that the beta for Athens Water is highly unlikely to be representative for a Dutch
water djstribution firm, and should not be included in the final calculation. In practise.
because the asset beta for Athens Water is so much above the other asset betas, and because
we use the median beta, excluding the Athens Water beta only reduces the median beta of
the European water companies from 0.34 to 0.33.

The specific construction of this equation was suggested by Hamada (1972) and has three underlying
3°
assumptions: A constant value of debt; a debt beta of zero; that the tax shield has the same risk as

the debt.

However, sometimes differences in market opening and closing tixnes can also lead to significant
Dimson adjustments. Hence, a significant Dimson adjustments does not always imply illiquidity.
32

For further discussion of the link between betas for milities and the Eurozone crisis, see “A Tale
of
Two Crises: The Betas of EU Networks”, August 2013 Dan Harris and Francesco Lo Passo
Published by The Brattle Group, mc.

24

brottle.com

Table 11: Equity and Asset Betas

European Water Companles
Severn Trent PLC
Pennon Group PIC
United Utilities Group PLC
Athens Water Supply & Sewage

United Kingdom
United Kingdom
United Kingdom
Greece

Equity
beta
(Al

Geanng
(DIE)
[81

Tax
rate

Asset
beta

[Cl

(01

0.61
0.54
0.60
1.16

101.4%
80.0%
114.4%
15.8%

22.4%
22.4%
22.4%
24.5%

034
0.33
0.32
1.04

Median (1]
Median excl. Athens Water (21

0.34
0.33

0.58
0.69

40.0%
40.0%

0.44
0.55

Median (31

0.50

31.4%
3 1.4%
24.2%
30.0%
30.0%
22.4%
34.0%

0.44
0.43
0.15
0.57
0.41
0.38
0.16

Median (41

0.41

US Water Companf es
California Water Service Group
Aqua America

European Network Companies
Snam
Terna Rete Elettrica Nazionale
REN Redes Energeticas Nacionais
Red Electrica
Enagas
National Grid
• Elia System Operator
-

United States
UnIted States

Italy
Italy
Portugal
Spain
Spain
United Kingdom
Belgium

0.73
0.73
0.38
0.92
0.63
0.60
0.30

5 1.6%
39.4%

97.3%
97.7%
204.4%
89.4%
80.4%
73.9%
128.4%

Notes and sources:
(Al: Table 10.
(BI: Calculated from Bloomberg data. Average vatues from Q2 2012 to Q1 2015.
(C): KPMG. Average values from Q2 2012 to Q1 2015.
(Dl: [AJ/(1+(1-(Cflx(B1).

VI.D. AssEr BETA FOR DUTCH WATER DISTRIBUTION
Table 11 illustrates the median asset betas for European water companies (0.33). US water
companies (0.50) and European network companies (0.41). There are several reasons to
believe that the US water companies have structurally higher betas because of differences in
regulation and the US water industry more generally. US firms have a price cap, rather than a
revenue control. Firms with a price caps tend to have higher betas, because they face volume

risk, which itself tends to be correlated to economic activity. In other words, a downturn in
economic activity could cause a reduction in transported volumes, which in turn leads to
reduced revenues and profits for the network. Hence the price cap increases the correlation
between the firm’s share price and the market index

—

giving a higher beta. In the US, water

firms change their tariff or rates when either the water company or its customers asks for the
tariffs to be changed via a ‘rate case’. Since rate cases are expensive and risky
could change in unpredictable ways

—

—

in that tariffs

they tend to be only brought when a large change in

the market has occurred. Accordingly, there is a qualitative case that the revenues for US
water firms will tend to be more highly correlated with the market, since it is more likely

25

broftle.com

that for example the water Firms’ customers will ask for lower rates when there is a

decrease

in economic activity. This does not occur in Europe, where tariff reviews or price

controls
take place at regular fixed intervals, which are independent of macroeconomic activity.
We
also understand that US water fïrms are engaged in a historically high level
of capital
expenditure. This will lead to increased ‘operating leverage’, which will again
tend to
increases betas, all else being equal. Therefore, we conclude that the betas for LIS water
firms
are likely to overestimate the true beta for a Dutch water distribution flrm.
European network firms have similar regulation to Dutch water distribution firms.

in that
they are subject to a regulated revenue control. However, they are not water firms.
We
expect that water demand may be less sensitive to macroeconomic conditions than
demand
for electricity or gas. While a regulated firm may have a revenue guarantee. a fail
in revenues
may only be compensated in a later period, and the present value of the compensation
may
not be sufficient to offset completely the earlier fall in revenues. Hence, differences
in the
sensitivity of demand to macroeconomic conditions could affect a regulated firm’s
beta. To
the extent that water demand may be less sensitive to macroeconomic conditions
than
demand for electricity or gas, the beta for European network firms may be structurally higher
than the beta for a Dutch water distribution firm.
We conclude that the asset betas we estimate for both US water companies and

European
network firms may overestimate the true beta for a Dutch water distribution firm.
On the
other hand, we prefer to rely on a sample of at least 10 firms in calculating beta. Given this.
our proposal is to give more weight to the European water firms, and Iess weight to the
US
water firms and the European network firms when estimating the asset beta for Dutch water
distribution. Specifically, we give the European water flrms a 50% weight, and the
US water
firms and the European nerwork firms a 25% weight each. Table 12 shows that this
resuits in
an asset beta of 0.39. We note that if we had adopted the same methodology as in our
June
2013 report, and simply taken the median of the European and US water firm betas,
we
would have obtained a similar but slightly lower asset beta of 0.34. The asset beta of

0.39 is

higher than the median European water firms’ asset beta of 0.33, and in our view

is more
likely to overestimate the true asset beta for Dutch water distributiori than to underestimat
e

it.

26

brattle.com

Table 12: Estimation of the Asset Beta for Dutch Water Distribution
Median
beta

Weights

[Al

[BI
50%
25%
25%

European Water Companies*
US Water Companies
European Network Companies

[1]
[2]
[3]

0.33
0.50
0.41

Weighted average

[4]

0.39

Notes and sources:
[1] to [3]:
[Al: Table 11.
[BI: Assumed.
[41[A]: [1][A]x[1][B]+[2][Ajx[2][B]+[3][A]x[3][B].

VLE.EQuITY BETA FOR WATER DISTRIBUTION
We re-lever the asset beta derived for each activity in the previous section to the 40% gearing
of the regulated asset described in Section III.
Table 13 shows that the resulting equity beta is 0.65. Note that the Dutch water distribution
firms are publicly owned and do not pay corporation tax. Accordingly, we assign a zero tax
rate when re-levering the beta.
Table 13: Equity beta
Asset Beta
Gearing (DIA)
Gearing (DIE)
Tax Rate
Equity Beta

[1]
[2]
[3]
[4]
[5]

0.39 Table 12
40% Section III
67% [2]/(1-[2])
0% Effective tax rate
0.65 [1]x(1÷(1-[4])x[3])

VLF. THE EQUITY RISK PREMIUM
The methodology specifies a ‘European’ ERP. That is, it uses an ERP based on the excess
return of stocks over bonds for the major economies of Europe, rather than the ERP based on
only the excess return of shares in the Netherlands. More specifically, the ACM has
determined to use the simple average of the long-term arithmetic and geometnc ERP as the
anchor for the forward-looking ERP estimate. The ACM will then examine other sources of
information on the ERP, in particular evidence of the ERP from Dividend Growth Models,
and use these resuits as a check on the validity of the historical data for the next regulatory

27

brottle.com

period. In line with the ACM’s methodology we present evidence
on the
Europe using both the arithmetic and geometric realised ERP. We then
to the realised ERP based on evidence on the ERP from Dividend

long-term ERP in

consider adjustments

Growth Models.

We note that the ERP derived from the historic realised ERP and

from Dividend Growth

Models are both estimates of the future, expected ERP. That is, they

are both forward looking
estimates of the ERP. The only difference is that one method takes the
past as the best guide
to the future, while the other (the Dividend Growth Models) derive the
ERP estimate from
projections of dividends and earnings.
As we noted in our November 2012 report for the ACM regarding the

WACC methodologv,u

we do not consider that all sources of potential evidence on the ERP have

equal weight. For
example, survey data has often proved an unreliable source for ERP estima
tes, for various
reasons. In our view, as we explained in more detail in our November 2012
report, the data
from the historic, realised ERP is a more reliable basis for the ERP estimate
that estimates of
the ERP derived from Dividend Growth 4
Models.: Accordingly, we do not

simply take the

average of ERP estimates derived from historical data and Dividend Growt

h Models, because
we do not give these sources equal weight. Nevertheless, and consis
tent with the
recommendations in the November 2012 report, we do take account of the eviden
ce on the
ERP from Dividend Growth Models.
Table 14 below illustrates the realised ERP derived from one of the most widely

used sources

for long-run returns, being the data published by Dimson, Marsh and Staunt

on (DMS) for
individual European countries taken from the February 2015 DMS reportY
This report
contains ERP estimates using data up to and inciuding 2014. Table 11 also shows

the simple
and weighted average ERP for the Eurozone. All the ERPs are calculated relativ
e to longterm bonds and the weighting is based on the current market-capitalisation
of each country’s
stock market. Hence, the ERPs of larger markets are given more weight, the idea

being that a
typical investor would have a larger share of their portfolio in countries
with more
investment opportunities.

33

Calculating the Equity Risk Premium and the Risk-free Rate, The Brattie Group (Dan
Harris, Bente
Villadsen, Francesco Lo Passo), 26 November 2012.
See discussion in the November 2012 report (Ibid) Section 4.7 p.36.
Credit Suisse Global Investment Returns Sourcebook 2015, Table 10.

28

brotfle.com

Table 14: Historic Equity Risk Premium Relative to Bonds: 1900— 2014
Geometric
mcm

Risk premiums relative to bands, 1900- 2014
Asithmetic
Average
Stunda,d
mean

(AJ

(8)

2.50
2.30

21.50

Austna

(1)

Belgium

Portugal
Spain
Sweden
Switzerland
United Kingdom

(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)

Europe
World

(16j
(17)

3.10
3.20

4.40

Average Eurozone
Value-weigtsted average Eurozone

(18)
(19)

3.13
3 48

7.62
6.48

Denmark
Finland
France
Germany
Ireland
taly
The Netherlands
Norway

X
(CI

—

Error

2014
marketcmp

S

Smillion

(DI

(E)

12.00
3.35

14.40

100169

4 40

2.00

2.00

3.60

2.80

5.10

8.70
5 30
840
450
6.50

6.90
4 15
6.70
3 55
4.80

1.70
2.80
210

374,059
336052
198.544

1.80
2.70

5.60

4.40

2.10

1,837.847
140,411
541.295
398,313

5.30

3.80

2.60

241,172

7.40
3.90

5.00
2.90

3.10

5.30
3 60

4.15
2.85
4.35

2.00
1.60

61.381
724,418
664,775
1,572,441
3,670,080

3.75
3.85

1 40

3.00
5.00

2 60
310
3 20
2.30
2.60

1.90
3.00

2 10
3.70

5.00

4,50

2.70

1.90

1 60

1,935,091

1.50

4.18

4.98

Nots and sources
(Al, (B(, (0): Credit Swsse Global lnvestrnentmet Retums Sourcebook 2015, Table 10.
(CI: ((A)+(Bfl/2.
(18): Average (1), (2), (4), (5), (6), (7), (8(, (9), (11), (12).
(19) Weighied average (1), (2), (4), (5), (6), (7), (8), (9), (11),(12( by (8].

Table 14 shows that the simple average of the arithmetic and geometnc ERP for the period
1900 to 2014 inciusive was 3.8% if all of Europe is inciuded, and 4.8% if only Eurozone
countries are inciuded. The very low ERP in Denmark and Switzerland in particular lower
the simple average ERP for all of Europe. Using the market size to weight the averages for all
of Europe, the ERP for the Eurozone is 4.98%, which we round up to 5.0%. These figures
reflect the very long run and notably exclude countries in former Eastern Europe. We use
the ERP for the Eurozone, since a Dutch investor is more likely to be diversified over the
same currency zone, rather than to incur additional currency risks by diversifying within
Europe but outside of the Euro zone.
The methodology asks us to also take into account ERP data derived from Dividend Growth
Models. We have obtained and constructed two ERP estimates based on Dividend Growth
37 The Bloomberg estimate shows that the ERPs have been increasing for the past
Models.’
four years. The ERP forecast by Bloomberg is currently above the historically realised ERP at
a little over 10%. The BOE estimates, on the other hand, have been decreasing. The final
estimate available was below the historically realized ERP.
Bloomberg provides market premium by country relative to the ten year government bonds. We
weight the premium by the market capitalization at the end of each calendar year.
Bank of England, “Financial Stability Report,” Nomber 2013, Chart L6.
29 t brottle.com

Figure 8: Eurozone Equity Risk Premiums by Vear

10%

8%
E
E

IS

a

6%

4%

2%

Bloomberg, turozone average

-

—.—Bank of England, two Stoxa
——DMS. arithmetic mean, Eurozone average
0%
2007

2008

2009

2010

201.1

2012

2013

2014

End of calendar year
Source: Bloomberg. Bank of England Nov-13 FSR. various DM5 report5 and Brattle calcutations

Hence, the trend and magnitude of the ERP based on DGM evidence seems to be
contradictory. However, given the state of the Eurozone economies, we find it unlikely that
the ERP has decreased materially since our June 2013 report. Therefore, jt still seems
reasonable not to make any of the downward adjustments that DMS recommend applying to
the historical average ERP, to convert the historical data into an expected, forward-looking
ERP. DMS in essence argue that several factors mean that the historic outturn realised ERP is
likely to overestimate the future ERP, because several events occurred to increase the outturn
ERP which will not happen again. These events include the favourable resolution of many
risks that were present in the last century, which led to unusually high real dividend growth
rates, the reduced risk of holding shares due to advances in technology which made
diversifjcation easier, real exchange rate gains which would not be expected to be repeated.
Correcting for these factors, DMS estimate that the expected arithmetic average ERP over
buis would be 4.5-5%, rather than the observed world ERP of 5.7% over buis, a reduction of
hetween 70 and 120 basis points. 1f we instead take the ‘raw’ historical ERP estimates over
Note that the adjustments to the ‘raw’ historic ERP which DMS recomniend to account for these
factors is distinct and separate from any concern that the use of the arithmetic average historic
ERP may overestjmate the future ERF, if returns are serially correlated over time.
See Credit Suisse Global Investrnent Returns Sourcebook 2015 section 2.6 p.33.
30 1 brottle.com

Jong-term bonds, we obtain a Eurozone average ERP of 5.0%. Hence, by taking into account
the ERP derived from Dividend Growth Models, we increase our estimate of the ERP by
between 70-120 basis points, relative to what would have been the case without a
consideration of the ERP derived from Dividend (;rowth Models.

VII. WEIGHTED AVERAGE COST OF CAPITAL
Based on the preceding calculations and discussions, Table 15 illustrates the overall
calculation of the nominal WACC for drinking water distribution in the Netherlands.
°
4
Table 15: WACC for drinking water distribution
Gearing (DIA) (1]
Gearing (D/E) [2]

40.00% Section III
66.67% [111(1-11])

Tax rate [3]

0.00% Effective tax rate

Risk free rate [4]
Asset beta (5]
Equity beta [6]
ERP [71

1.83% Section IV
0.39 Table 13
0.65 [Slx(1+(1-[3])x[21)
5.00% Section VI.E

After-tax cost of equity [8]

5.09% [4]+[6]x[7]

Debt premium (9]
Non-interest fees [10]
Pre-tax cost of debt [11]

0.82% Section V
0.15%
2.80% [41+(9]+j10]

Nominal after-tax WACC [12]
Nominal pre-tax WACC [13]

4.17% (1-[lflx[81+[lJx(1-[3flx[11J
4.17% [13]/(1-[3])

The method assumes that since the water companies are publicly held and do not pay taxes, a tax
rate of zero should be applied.
31 1 brottle.com

APPENDIX 1

—

STATISTICAL RELIABILITY BETA

We detail the standard diagnostic tests to assess if the beta estimates satisfy

the standard

conditions underlying ordinary least squares regression, which are: that the error

terms in the
regression follow a normal distribution and that they do not suffer from 4
heteroskeclastjcity
’
or 4
autocorrelatio
2
n. Failure to meet these conditions would not invalidate the heta
estimates, but would have the following consequences:
1. Although OLS is stili an unbiased procedure in the presence of heteroskedasticity
andJor autocorrelation, it is no longer the best or least variance estimator.
2. In the presence of heteroskedasticity and/or autocorrelation, the standard error
calculated in the normal way may understate the true uncertainty of the
beta
estimate.
3. Ileteroskedasticity and/or auto-correlation may indicate that the underl
ying
regression is mis-specified (i.e. we have left Out some explanatory variable).
HETEROSKEDASTIcITY

We apply White’s test for heteroskedasticity. Table 6 illustrates the resuits.
Table 16: White’s test for 1
White Stal

p-vatue

Heteronkedascity

United Itingdom
United Kingdom
United Kingdom

0.32
0.17
22.29

0,85
0.92

Greece

3217

0.00
0.00

No
No
Set
Ye

United States
United States

4.28
0.52

0.12
0.77

No
No

Snam
Terna Rete Elettrica Nationale
REN Rede, Energeticas Nacionais
Red Electrica

Italy
Ituly
Portugal
Spain

0,20

0.90
0.54

No
No
No

Enagas
Nation9l Grid
Elia Systern Operator

Spain
United Kingdom

European Water Companles
Severn ‘IrenE PLC
Pennon Group PLC
United UlilitieiGroup PIC
Athens Water Supply & Sewage
US Water Companten
Calitornia Water Service Group
Agua America

European Network Contpanles

-

Betgiuni

1.23
0,19
051
0.05
17.83
7.77

‘

0.91
0.74
0.97
0.00
0.02

No
No
Ves
Ves

The resuits indicate the presence of some heteroskedasticity in the sample. This most likely
relates to the significant increase in market volatility around the heart of the crisis

at the start
of the sample period, and a subsequent decrease, changing the variance of the population over
the sampling period.

Heteroskedasticity means that there exists sub-populations in the sample which have different
variance from others.
42 Auto-correla
tion means that the error terms between periods are correlated.
4t

32

brattle.com

AÏJrOCORRELATION

We also apply the Durbin-Watson test for auto-correlation. Unsurprisingly, this test indicates
a degree of autocorrelation in mostof the regressions, also likely reflecting the development
of the credit crisis and the changing extent of market volatility. The effect of this auto
correlation is that standard errors will over-estimate the precision of the regression. The
resuits are presented in Table 17.
Table 17: Durbin—Watson Test for Auto-correlation
0W Stat

Serial
Correlation

European Water Companles
SevernTrent PIC
Pennori Group PI.C
United Utihties Group PIC
Athens Water Supply & Sewage

United Kingdom
United Kingdom
United Kingdorn

1.66

Greece

1.52

Vet
Vet
Vet
Yes

United States
United States

1.77

Indecisive

163

Vet

Italy
Italy
Portugal
Spain
Spain

1.76

Indecisive

1.70

Vet
Ves

United Kingdom

1.54

No
Yes

Belgium

1.72

Vet

1.64
1.55

US Water Compardes
Cahfornia Water Service Group

Aqua America
European Netwerk Cernpanles
Snam
Terna Pete Elettnca Nationale
PEN Redes Energeticas Nacionan
Red Electrica
-

Enagas
Natlonal Gild

Elia System Operator

1.66
1.60
1.79

Ves

PRAIS-WINSTEN REGRESSIONS

To account for the inciusion of auto-correlation in the sample a standard statistical technique
is to apply a regression using the Prais—Winsten estimation tests. We also control for
heteroskedasticity. The results are presented in Table 18. The corrections for auto-correlation
and heteroskedasticity do not have a significant impact on the resuits.

33 1 brottle.com

Table 18: Prais-Winsten Regressions Resuits
Test for
HeteroSerial
skedascity correlation

ots

—

Country

Beta

Standard

error

GIS (Prao Winsten)
Beta
Staridard
error

European Water Companles
Severn Trent PIC
Pennon Group PIC
United Utilities Group PIC
Athens Water Supply & Sewage

United Kingdom
United Kingdom
United Kingdorn
Greece

No
No
Ver
Ver

Ver
Vet
Ves
Ver

0.60
0.53

0.05

0.59
0.37

005
0.10

079
0.68

0.05

060
033
059

006
00S
006

057

0 14

0.05
004

077
068

006

0.04
0.03

0.73
072
0.37
0,74

004
0.03

0.03

0.59

0.04

0.03

0.30

003

US Water Companlet
California Water Service Group
AquaAmerpca
European Network Conipanles
Snam
Terna Rete Elettrica Nationale
REN- Redes Energeticas Nacionais
Bed Electnca
Enagas
National Grid
Elia System Operator

United States
UnitedStates

ltaJ
Itaty
Portugal
Spain
Spain
United Kingdom
Belgium

No
No

Indecisive

No

Indecisive

0.74

No
No

Vet

073
0.37
0.74

No
No
Yes
Yes

Yer

Vet
Vet
No
Vet

063
0.59

Ves

0.30

0.03
0.04
0.04

34

005

004
005

brotlle.com

APPENDIX II
BONDS ISSUED BY FIRMS ENGAGED IN SIMILAR
ACTIVITIES TO DRINKING WATER DISTRIBUTION
—

Table 19: Bonds issued by firms engaged in similar activities to drinking water
Matorrty
Cvmpny

ANR Pipelrne Cv
Enb,rdge P,pehnel In,
Enkrid8 Pipekne, In,
Fnbrrdge Pipeliees In,
Enb,rdge Ppvhnov lor
6F PIpline, lor
Onbrid
690 Prpel.ne Ce
Nova tla, Transm.soon Ltd
Nova Gal Ttanve,l,slon 1,1,1
Nova Ga Ttavvn,icllon LId
1310 Sy.tettr Operator 5649V
inter P,pelrnn Corrdov nv
NorthernPcrwer9ridyovkshrrePl,C
State 0,14 Evrope Oeoelopn,ont 2014 PLC
BrIt.vlrTranoro International Finance BV
Vier Ga, Trarrvporl G,nbII
Onno, Electro Deirrery Eo LLC
Ilydroctne mc
Nedvrland,eG.a,rcnie NV
Tharne, Water Utililre, Cayn,an Froanr, lid
Ene,i,ltokjiogNV
TenneT Holding BV
AovNet Sern,y,e, Hotd.ngl Ply Lid
Natlonal Grid Gan PLC
93E Reneat, do Ttenlporl d’EIect,lvlte 5*
Tennel Holding BV
BOE Re,eao de Traniport d’OleclrlcIte 5*
Fingrid 01)
Vier Dal Tranr.po,-t Gn,bN
Ene,,, Holding NV
Narional Grrd Ele’ctrkity Tranvvrisraon PI.C
RIO Retean do Transport d’Olect,vite 5*
ArilNet Service, Holding, Fl3 Lid
AltaLink OP
Ar,ghan Water Service, Floarnang PLC
YorhIs,r Water Syroi,e, Bradford F,nanre Lid
Northern Powergrid YorEhjre PLC
TeoneT 1-1014 jng BV
AWD Parenr Cv Lid
Hydro One It
RiO $e.eaa de Transport d Olectrkrte 5*
Nede,land,e Garanie NV
Sosthorn Water Services Finonce LId
Natiorral 0,14 Das PLC
Soolhern Water Service. Finao,e Lrd
Norilrern Powergrrd ‘Forkvhire PLC
Narragaolett Electric Co/The
Nederlandie lla,anw NV
Vier Ga, Trarupoet Gmb+1
Nat,ont Grid Ca PLC
Nai,onat C,rrd Olvcinctty Trarnnrnaion PIC
York,hve Water Service, Odval Financr Lid
0w, Cyn,ra Finandng Ltd
Tirarne, Water Utiliiie, Cayrrv.n Finanve Lid
AltaLink IP
Wale, & Wevr UtiStre, Finance PCC
Thanve, Water Utiht,e, Flnarrve Lid
Northorn Electric Finanne PLC
Narragaroelt Electric Co/The
Affiorty Water Progran,rne F,r,anre md
t’harve, Ware, 1)1114e’, Finanre Lid
O’ic Electric Deiloery (0 [LE
WaIe,6 Went [itilltienyinance PLC
OncorEIectrkDeIrverCa,[LC
A)taLinkLP
Donor tlectr,c Dohoery (0 L(C

Bond yreld

date

Carrenrv

(Al

BI

1 90,21
5-Apr-20
30 Nov-22
15-Feb-24
11-Nov 23
12-Nov 19
35 Feb 24
1-Apr 23
16-Dec 24
27 May 20
13 May 19
3-Feb70
1-Apr 2$
26-Un 22
4-NOv 21
I0-1l 23
1-Sep-22
13-140-22
13 lol.22
19-ion-25
13-Nov70
21-Feb23
24-lol 20
3 Mar 20
12-Sep 23
1 .f4ov 20
3 Feb 21
3Apr-24
12 140-20
26-Jan-22
7Feb24
20.Sep.19
13.Feb-24
6-Nov.23
30-IoI-22
21.Aog.19
17.1.0 20
9-Feb 22
21 Avg.23
1 Jan 20
28’har, 22
13.0cr 21
31 Ma, 21
16-Den 24
II Ma, 26
17jan 20
15 Mat 20
20-lan 21
12 Jan25
27-Jan-25
26-Nov-IR
21.Feb.23
31.Mar.71
18.JuI-22
20 Nov.22
13.Dec.23
30.jue-20
16-Oct 20
l5-Mar-20
30-Sep 22
20.Apr.21
30Sep-20
30-Nov21
O.Sep-2O
3
17 Sep20
30 Sep-20

XI
v,
Sv, 2
(CI 10

USI) 4 25
CAO 4 17
CAO 3 22 3 26
(AD 3.55 356
(All 3 39 343
CAO 393
11513 4 22 4.20
USO 442 442
CAO 3 63 363
1*3) 3.67 3 57
F119 367
tAl) 4.45
tAP 264 264
£119
UIt)
RUS 2 54 254
USt) 361 340
CAO 2,88
611$ 2.19 2.07
1393’ 332 332
OUR
RUS 271 2.56
OUR
08F 4 56
RUS 2 36 235
OUR
11)6 3.80
EUR 257 2.17
EU$
EUS 2 67
1353’ 3.47 347
OUR
OUR 1.99 199
CAO 335 355
tAP 347 3.07
tAP 4 63
tAP 4.59
OUR 330
(lIP 344 356
CAO 4 18
OUR 1.05 2.27
OUR 2.72
tAP 4.59
08F 3 34 3 34
06F 2.82 282
1393’ 457
1250 4.35
OUR 3.35
OUR 1.63 1 63
1353’ 3 13 313
CAO
1383’ 3,39 3,48
tAP 4 26
EUB
CAO 344 3.44
tAP 348 350
663’ S 10
tAP 4 53
UIt) 435
68F 3.48 343
693’ 4.40
USO 3,87
663’ 4.01
U50 4.29
CAO)
USO 4 29

10 yea,
loovrergo (XI

Sv’

2v’

(EI

1F:

2.52
3 17
223
2 22
2 24
307
2 33
293
709
1 90
127
317
169

1 9$
196
1.87
175
2 19

229
2.24

2 32
7 34
293
209
1 90

165

1 99
229

Bond vpread
(XI
5v’
2cr
[GJ
IH
1 74
100
0.99
533
1 14
0.95
189
1 50
1 53
1 76
0.41
129
09€

0.97
1.33
3 35
190
1 50
153
1 76

09$

181
2 19

026
1 14

1 93

2.03

078

004

3 65
1.83

183

091
0,52

052

290
154

194

090
0,63

063

1 90
352

295

077
090

092

1.29
235
219

233
2.46

2 69

0.70
100
1.33
089
1,09
0.93
0,99
0.89
1,02
0.61
1,36
088
1 16
1 07
120
064
0 72
0.97

056
1 19

0.70
1.00
0.87

087

3.20
2.06
2 11
3 24
245
1 56
360
3 14
2 73
091
2 Ii

2 17

2.21

263

112
5.03

0.85

244

100
094
1.43
5.06
120
1 13
1 21
1 29
1,34
130

100
0,59

1 76

249
1,66

tl 91

0 €9

08*
1 16

072
0.97

-

-

3 24
244
2,5.0
3.66
351
314
236
3 19
2.58
2.66
7.99
3.00

2 62

2,43

130

Anroont
aatvtandrng
(II
300.000.000
150,0.00,000

0 56
186
3.01
040
1 14

129
235
214
3 79
3.60

distribution

1 OS

150.000,000
200,000,000
100,900,000
300.000.000
12S,.000
200.000,000
100.006,000
87,000.000
900.000,000
150,000.000
330.000.000
200.000.000
1.500.000.000
750.000,000
800.000,000
640.000.000
500.000.000
000,000.000
500,000,000
590.000,000
500,000,000
278,000,900
$00.000.000
500,000,000
750.000,000
300,000.000
150,000.000
390,000,000
323,920,000
640,000,000
360.000.000
500,000.000
250.000,000
275.000,000
200.000.000
900.000.000
200.000,900
300.400,000
750.000.000
500.000,000
350,000,000
217.395,000
950,000,900
200,000,000
250,000,000
300,000,000
750,000,000
111.106,000
400,000,000
210,692,000
325,000,000
113,900.000
275,000,900
250,000,400
200.000.000
100.000.000
250,000.000
80.000,000
225,000,900
126,218.900
250,000,000
126.278.000
125.000,000
126,278,400

Hate, and SO-arcel
Md veld, to waiar,ty reported by Bloornirerg Gorrernreent bond preld, fror,r Bank of
Canada. Bank of Engiand, Federat Revrrn and De Nedorfat,dodre
Bank
(CI Aoer0ge yreldl from 01/04/2010 until 31/312055 Irociraded) 0 lire yreld, are ie the
date range of 910 11 yearn froin the rnaror,t3 date
For coamople, la bond rnatare, en the 18/01/2022, only yield, reio,rted betweer
18/07/2011 and 13/07/2013 ate con,rdered in the anerage
lol Average ywld, trom 01/04(2013 anti) 31/3/2015 inciaded) t the yreldl are In the date rarrge of It, 11 vears front the nrarot,t9 date
151.9 Aurrage lflyea, government bond yield, In the ,ae,e per,od a, that ol the bord vield, inchaded Gonernineet bood yield, are ara.gned 6a,ed no
the
currency

IG

((‘(0)

4) (011Fl

35 1 brott)e.com