Post och Telestyrelsen

WACC FOR THE FIXED TELECOM- MUNICATIONS IN SWEDEN

| 26 OCTOBER 2007 WACC for the Fixed net in Sweden

TABLE OF CONTENTS

Chapter 1 Preface...... 3

Chapter 2 Main findings...... 4

Chapter 3 Methodology...... 5

Chapter 4 Financial gearing...... 6 4.1. Method...... 6 4.2. Estimate ...... 7 4.3. Regulatory reference...... 8

Chapter 5 Risk rate ...... 9 5.1. Method...... 9 5.2. Estimate ...... 10

Chapter 6 Tax rate ...... 11

Chapter 7 Debt Risk Premium...... 12 7.1. Method...... 12 7.2. Estimate ...... 12 7.3. Regulatory reference...... 13

Chapter 8 Equity Risk Premium ...... 15 8.1. Method...... 15 8.2. Estimate ...... 16 8.3. Regulatory reference...... 16

Chapter 9 Beta...... 18 9.1. Method...... 18 9.2. Beta estimation ...... 20 9.3. Regression of fixed net beta...... 22 9.4. Fixed net beta in Sweden...... 23 9.5. Regulatory references...... 25

References...... 26

Annex A – Companies in the study...... 28

Annex B – Regulatory references ...... 30

Annex C – Beta estimations...... 31

2 WACC for the Fixed Telecommunications net in Sweden

Chapter 1 PREFACE

Copenhagen Economics has been commissioned by Post och Telestyrelsen (PTS) to under- take a study on the weighted average cost of capital (WACC) for the fixed telecommuni- cations network in Sweden. PTS has found that TeliaSonera has significant market power in the Swedish market for fixed (wholesale) access and has therefore imposed remedies– such as cost based pricing. The cost of capital will be used to determine these prices.

The report from Copenhagen Economics has been prepared by a team consisting of project manager M.Sc. Petter Berg, M.Sc. Jonatan Tops, M.Sc. Marcin Winiarczyk and Ph.D. Hen- rik Ballebye Olesen. Professor Thore Johnsen at the Norwegian School of Economics and Business Administration has provided quality assurance.

Copenhagen, 26 October 2007

Henrik Ballebye Olesen Senior Economist, Copenhagen Economics

3 WACC for the Fixed Telecommunications net in Sweden

Chapter 2 MAIN FINDINGS

The Swedish Regulatory Authority (PTS) has found that TeliaSonera has significant market power in the Swedish market for fixed (wholesale) access, and that there is a need for ex ante regulation. PTS has therefore imposed a series of remedies on the markets such as access ob- ligations and cost based pricing. This requires that the cost of capital is calculated. For this purpose, Copenhagen Economics has been commissioned to calculate the weighted average cost of capital (WACC) for the fixed net.

TeliaSonera has several business activities apart from the fixed network operations, all with different systematic risk. A regulation based on the company’s overall systematic risk will therefore not only reflect the risk of the regulated business segment and thus not give correct investment incentives. We will, using a divisional approach, separate the systematic risk be- tween the different business segments in order to calculate the cost of capital from the fixed telecommunications net.

We find that the fixed net operation is TeliaSoneras least risky segment.

Following best practice in estimating the cost of capital for regulatory purposes, we propose that a nominal pre-tax WACC of 9.2% is used in the regulation of the Swedish fixed tele- communications network.

Table 2.1 Calculation of WACC – Fixed telecommunications net Low gearing High gearing Risk free rate 4.19% 4.19% Debt Risk Premium 0.80% 1.30% Cost of debt 3.6% 4.0%

Risk free rate 4.19% 4.19% Equity Risk Premium 4.75% 4.75% Levered beta 0.80 1.05 Cost of Equity 7.98% 9.18%

Gearing 30% 50% Tax rate 28% 28% Post-tax WACC 6.7% 6,5% Pre-tax WACC 9.3% 9.1% Mid-point 9.2% Source: Copenhagen Economics.

In order to benchmark our results, we compare our estimates of the different parameters in the WACC to the decisions of other European regulatory authorities. In some cases, we also compare the results to other utility sectors.

We find that our estimates fit well with the established regulatory standards and current regulatory estimates.

4 WACC for the Fixed Telecommunications net in Sweden

Chapter 3 METHODOLOGY

PTS uses a Long Run Incremental Costs model (LRIC) in its regulation of the fixed tele- communication net. The principle behind this model is that regulated firms should be able to retrieve their additional costs caused by the activity in the regulated market. The WACC is a central parameter in these calculations.

The WACC is defined as:

WACC  g  (1 T)  (DRP  R f )  (1 g)  (R f   j ERP)

where g is the gearing i.e. the ratio between debt and capital employed; T is the tax rate; DRP is the Debt Risk Premium, i.e. the difference between the risk free rate of return and

the interest of debt; Rf is the risk free interest rate; ERP is the Equity Risk Premium or the required interest on a relevant market portfolio above the risk free rate; and ßj is the sensitiv- ity of the return on asset j relative to a market portfolio.

The WACC depends on the risk, and the risk may be different for different divisions of a telecommunication operator. The risk may differ between fixed telecommunication net- works and mobile telecommunication. Hence, a central question in the regulation is whether the WACC should be different for fixed telecommunication networks than the WACC for other businesses.

Based on empirical evidence, telecommunication regulators in other countries have found that the systematic risk is lower for fixed telecommunication services than for mobile com- munication services.1 For this reason some regulatory authorities have decided to differenti- ate the WACC for the fixed communications network from mobile and other services. The differences in WACC arise because fixed telecommunication networks and other services have different levels of systematic risks, i.e. different dependence on economy wide effects. This implies that the equity beta, which is an important determinant of the WACC, is lower for the fixed telecommunication network than for other services.

We are convinced that it, albeit issues regarding the precision of the estimates, is correct from a regulatory viewpoint to differentiate the WACC between fixed telecommunication networks and other services. We have therefore analysed the systematic risk arising from the fixed telecommunications net.

Our conclusions are in line with previous findings; we find the fixed telecommunications network to have the lowest systematic risk of the main operations carried out by telecom- munication operators, which implies lower WACC. However the range for the systematic risk on the fixed telecommunications net is relatively large.

1 ERG (2006).

5 WACC for the Fixed Telecommunications net in Sweden

Chapter 4 FINANCIAL GEARING

Financial gearing describes the relation between the company’s debt and equity. It is the share of the assets that are financed by interest bearing debt and is defined as D/(E+D), where D is the debt and E the equity capital.2 High gearing means debts are high in relation to equity capital.

4.1. METHOD Book value normally underestimates the value of equity.3 Calculations of the financial gear- ing should therefore be based on market values as opposed to book values. We base the valuation of equity on market values. The market value is calculated by multiplying the number of outstanding shares with the price of one share.

In order to calculate the market value of debt, one has to value every debt instrument sepa- rately. It is straightforward to value bonds, but a firm’s balance sheet usually also contains many other types of interest bearing debt instruments. This makes market valuation of debt a complex task. It is therefore common to use the book value of debt as a proxy for the mar- ket value.4 We base the valuation of debt on book values.

There are two main methodologies to decide what gearing level should be used in the calcu- lation of cost of capital. The first method is to use the regulated company’s actual gearing. However, a company’s actual gearing may deviate from its long run capital structure and is, therefore, not in line with the LRIC-methodology. Furthermore, the LRIC-methodology is based on the costs of an efficient operator, and the regulated company’s gearing may not be efficient. The second method is to assess a target gearing by estimating the best practice from a peer group. This approach implicitly assumes that the companies in the peer group have, on average, an optimal gearing. We choose to use the target gearing approach.

A review of the approaches used by European regulatory authorities reveals that the choice of method differs. The review neither supports nor challenges our decision to use a target gear- ing to estimate an efficient gearing level, cf. Table 4.1. However, we note that where actual gearing has been used, it approximately coincides with the average gearing of our peer group, cf. Table 4.1.

2 Invested capital (capital employed) is by definition equal to D+E, i.e. the sum of interest bearing debt and equity. 3 Present value of growth opportunities is, for example, not included in the book value. In addition, accounting principles can also decrease the usefulness of book values. 4 This approach is used by other regulatory authorities and is also used in AMI (2003).

6 WACC for the Fixed Telecommunications net in Sweden

Table 4.1 Regulatory references on estimating gearing Actual gearing Target gearing Denmark (43%) Belgium (35%) Germany (36%) Finland (30%) Netherlands (38%) (40%) UK (33%) Switzerland (49%) Note: Countries in which the method to estimate gearing is not publicly available are excluded. Denmark uses a combination of actual gearing and regulatory benchmark. Source: See Annex B

4.2. ESTIMATE As stated in section 4.1, we use a peer group to establish a target gearing. The peer group consists of incumbent integrated operators in western European countries. We have found twelve comparable operators. A full description of them is found in Annex A. Their gearing ranges from 12% to 67% with an average of 39%. As there are outliers in the data, we focus on the median, which in this case is the same as the peer group average. The fact that Telia- Sonera’s gearing level is significantly lower than its peer’s, further strengthens our certainty that a target gearing should be used.5

Figure 4.1 Gearing for European integrated operators

80%

70%

60%

50%

40%

30%

20%

10%

0%

BT KPN OTE TDC

Belgacom Telefónica TeliaSonera Telecom Italia France Télécom Portuga l Tele com Telekom Austria Deutsche Telecom Note: The gearing is measured from the total equity in august 2007 and interest bearing debt from annual reports in 2006. Both the median and the average are 39%. The dotted lines illustrate our chosen gearing range. Source: See Annex A

Based on the median gearing and the distribution of gearing in the peer group, we decide to use a gearing range of 30% to 50% for the fixed telecommunications market in Sweden. The midpoint of the range is close to the peer group median and the range captures most of the integrated operators in the peer group.

5 TeliaSonera has a gearing level of 12%.

7 WACC for the Fixed Telecommunications net in Sweden

4.3. REGULATORY REFERENCE We have reviewed the gearing estimates used by other European authorities in the regulation of fixed telecommunications network operators. The lowest gearing used by a national regu- latory authority is 30% (Finland) and the highest gearing used is 52% (Portugal). Hence, the review suggests that our gearing range of 30% to 50% is well in line with international practice. In fact, the midpoint of our range, 40%, is almost the same as the average gearing level used by regulators across Europe.

Figure 4.2 Regulatory reference on gearing

60%

50%

40%

30%

20%

10%

0%

Belgiu m Finland France Portugal Denmark Germany Netherlands Switzerland United Kingdom

Note: The gearing levels in UK and Denmark are averages of two scenarios. The average gearing is 39%. Source: See Annex B

8 WACC for the Fixed Telecommunications net in Sweden

Chapter 5 RISK FREE RATE

The risk free rate is the expected return on an asset which theoretically bears no risk at all. In practice, it is not possible to find an asset that is free of all risk. However, freely traded in- vestment-grade government bonds are generally regarded as having close to zero default risk and zero liquidity risk. 6

5.1. METHOD Since there are government bonds with different durations, we have to decide which dura- tion gives the best estimate of a risk free rate. A bond with a shorter maturity is normally more volatile than a bond with longer maturity, which speaks to the advantage of the one with longer duration. On the other hand, the yield on a bond with long duration is more prone to also include an inflation risk premium which should not be included in a nominal WACC.

The risk free rate should be measured in the same currency as the cash flows are measured.7 Thus, bonds nominated in Swedish Kronor should be used to determine the risk free rate for TeliaSonera.8

The LRIC-model is based on a forward looking principle. Hence, an ex ante WACC re- quires that all variable estimates should be forward looking, reflecting the current expecta- tions for the relevant future period. Furthermore, the timing of the risk free rate should be consistent with the timing of the equity and debt risk premiums.

The risk free rate to be used can be an average of a historical period or the last observation. The last observation is generally considered to best include future expectations of the risk free rate. However, estimates based on singular observations can be affected by temporal fluctuations in rates and are therefore not suitable for regulatory purposes. We propose that an average should be used to smooth out these temporal fluctuations. The procedure of aver- aging recent historical rates is also referred to as standard practice by the Independent Regu- latory Group (IRG).9

Since the risk free rate varies over a business cycle due to changes in the expected inflation rate we recommend that PTS updates the WACC-estimate for future major changes in the Swedish interest rate level, i.e. update the risk free rate of return. On the contrary, the re-

6 Since governments control the printing of currency they will always be able to convert the security back to liquid assets and we consider the default risk of a country like Sweden to be negligible. 7 Damodoran (1998) denotes this “the consistency principle”. It is also described in Brealey, Myers and Allen (2006) 8 This is relevant also for foreign, international investors, and for TeliaSonera’s international operations. The con- version over time of TeliaSonera’s return to/from other currencies will generally reflect the effects of different infla- tion rates and thus reflect differences between the interest rates for different currencies. Hence, the same currency should be used to measure cash flow and risk free rate. Since cash flows are reported in SEK, the Swedish risk free rate should be used. 9 IRG (2007) p.15

9 WACC for the Fixed Telecommunications net in Sweden

maining variables in the WACC calculation have a more structural nature and are thus more stable. Hence there is less need for frequent update of these estimates.

5.2. ESTIMATE We claim that the risk free rate should be based on a stable estimate. We therefore assess the standard deviation of 3, 4, 7 and 10-year Swedish government bonds and find that the stan- dard deviation is lowest for the 10-year bonds. We further find the rates for bonds of differ- ent duration to be similar, implying that there is no difference in expected future inflation between them. 10 We therefore choose to use a nominal government bond with a 10-year ma- turity to estimate the risk free rate.11 It should however be mentioned that the differences be- tween the bonds are negligible.

Further, in order to mitigate the sensitivity to the date of measuring, we chose to use a 6- month average to estimate the risk free rate. The rationale for restricting the average to six months instead of 12-months used in the previous WACC-model, is that the risk free rate shall be based on expectations of future returns, as opposed to historical returns. The meth- odology is well in line with the IRG practice mentioned above.

In our calculations, we estimate the risk free rate used as a 6-month average of the return of a Swedish nominal 10-year government bond, is 4.19%, cf. Figure 5.1.

Figure 5.1 6-month moving average

6 5 4 3 2 1 0 2003-03-03 2003-06-03 2003-09-03 2003-12-03 2004-03-03 2004-06-03 2004-09-03 2004-12-03 2005-03-03 2005-06-03 2005-09-03 2005-12-03 2006-03-03 2006-06-03 2006-09-03 2006-12-03 2007-03-03 2007-06-03 2007-09-03

Rf Rf mean

Note: The last observation is September 3, 2007. Source: Swedish Central Bank

10 On the last date in our dataset (31 Aug 2007) the rates for the 5, 7 and 10-year Swedish bonds were 4.21, 4.215, 4.215 respectively. 11 A 10-year government bond is also used by many other national regulatory authorities, for instance in Denmark, Finland and France. It is also the bond used in the previous fixed telecommunications network WACC, see AMI (2003).

10 WACC for the Fixed Telecommunications net in Sweden

Chapter 6 TAX RATE

Most of the market information is based on post-tax figures. We therefore find it convenient to calculate the post-tax WACC and then convert it to pre-tax WACC.

We follow the standard methodology and use the marginal tax rate in our calculations.12 The Swedish corporate tax rate is currently 28%. Hence, the pre-tax WACC will be calculated with the tax rate 28%.

12 See for example AMI (2003), Ofcom (2005).

11 WACC for the Fixed Telecommunications net in Sweden

Chapter 7 DEBT RISK PREMIUM

Debt owners demand a premium above the risk free rate to compensate for the risk of de- fault. This risk premium is denoted debt risk premium (DRP).13 A higher financial gearing increases the risk of default and consequently entails a larger DRP.

7.1. METHOD The DRP can be directly estimated at market value if the companies have issued company bonds. This is the case for many European integrated operators. It is also possible to estimate DRP indirectly, using the relation between credit ratings and debt premium.

We have access to a market based estimate through company bonds, i.e. the difference be- tween the bond yield and the risk free rate, and regard this to give the best estimate. The time period used to estimate the debt risk premium should be in accordance with the time period used to estimate the risk free rate and equity premium, i.e. the estimate should be forward looking. Since our perspective is one of an international investor and since, as al- ready noted in Chapter 3, we do not know whether a particular company is efficient, we again use a benchmark to reach an estimate.

7.2. ESTIMATE We base the estimate on the European integrated operators’ company bonds. We use 10- year bonds, as this coincides best with the time period used to estimate the risk free rate and equity risk premium. As we used the peer group median to estimate the gearing, the median debt premium should be a good estimate for the premium. All operators included in the peer group have a credit rating close or equal to TeliaSonera’s credit rating.14

The average corporate bond spread is 117 points.15 Since the average is heavily influenced by a few operators, we again use the median value, 105 points, as a benchmark, cf. Figure 7.1. Based on this median, we define a range of 80 to 130 points.16 The midpoint is slightly higher than TeliaSonera’s bond spreads of 96-105 points. However, this is expected due to TeliaSonera’s low gearing.

13 It is sometimes also referred to as Debt Default Premium (DDP) as the premium only compensates for the ex- pected loss from default. 14 They have ratings in the range BBB+ to A while TeliaSonera has A-. 15 Interest rate spreads are measured in points. There are a hundred points to one percent. 16 105 is the midpoint in the range.

12 WACC for the Fixed Telecommunications net in Sweden

Figure 7.1 Bond spread over national risk free rate

200 180 160 140 120 100 80 60 40 20 0

OTE

KPN 2007 Telenor 2007 Belgacom 2006 TeliaSonera 2005TeliaSonera 2007 Telecom Italia 2005 France TelekomFrance 2005 Telekom 2007 Telekom Austria 2005 Deutsche Telekom 2006

Note: The dotted lines illustrate our proposed range, the companies’ respective rating is found in Annex A. The spreads are based on the situation on 16 august 2007. Source: DataStream.

Another benchmark is the US utility industry which includes not only telecom, but also utilities of other kinds, e.g. gas and electricity infrastructure. It is therefore not an ideal benchmark, but still an illustrative reference point.

We expect the debt risk premium for utilities to be lower than for telecommunications, as there are competing telecommunication infrastructures, whereas energy infrastructure nor- mally has no direct infrastructure competition.

This is confirmed by our comparisons. Table 7.1 indicates that the DRP for US utility bonds with comparable credit ratings range from 69 to 87 points, i.e. lower than the 105 points in our estimation.

Table 7.1 US utility bond spread (10-year bonds) Credit Rating DRP A2/A 69 A3/A- 72 BAA1/BBB+ 87 Source: Bondsonline.com

7.3. REGULATORY REFERENCE We compare our debt risk premium to the premium used by the European regulatory au- thorities when regulating the fixed telecommunications network. The average regulatory DRP is 104 points. As there are outliers in the data, we again consider the median to be a more suitable benchmark. The median is 100 points. The midpoint of our estimated range, 105 points, is in line with the European average regulation, cf. Figure 7.2.

13 WACC for the Fixed Telecommunications net in Sweden

Figure 7.2 Regulatory benchmark on DRP

2,00

1,50

1,00

0,50

0,00 Denmar k Finland France Germany Netherlands Por tugal United Kingdom

Note: The dotted lines illustrate our proposed range. Source: See Annex B

14 WACC for the Fixed Telecommunications net in Sweden

Chapter 8 EQUITY RISK PREMIUM

The Equity Risk Premium (ERP) is the additional return that a market investor requires in order to accept the systemic risk associated with investing in the market portfolio instead of a risk-free asset.

8.1. METHOD When regulating ex ante, the ERP is ideally based on expectations about the future. How- ever, it is difficult to find a robust estimate of expectations. Recent estimates of expected ERP range from near zero to 7%.17 Apart from the intense academic debate on the size of the premium, there is also a discussion about whether there are differences between historical, required, implied and expected ERP. For regulatory purposes IRG states that a downward adjustment of the historical risk premium is justified.18 We propose a methodology in which we use the studies which are most commonly used for regulatory purposes.

The ERP can be measured using a geometric or arithmetic average. The geometric average is the average excess growth rate of equity above a risk free investment and the arithmetic aver- age is a simple average of period excess returns. The arithmetic average will always be higher than the geometric average (for a variable return series), but is still the proper estimate to use in a required return figure like the WACC.19 We therefore use an arithmetic average, which is also the established international regulatory practice.

We should use an international equity risk premium, because the investor perspective in this analysis is that of an international investor. This reflects the important principle in regula- tion, that the companies should not be compensated for any inefficiencies in their opera- tions, investments, or ownership structure. If the regulated company’s owners are less than perfectly internationally diversified, the corresponding costs of such inefficient ownership should thus be paid by the owners themselves, and not be allowed to be passed on to the company’s customers in the form of higher product or service prices.

17 Fernández (2007) reviews the available estimations of equity risk premium. 18 One of the arguments for adjusting the historical risk premium is that it is much easier to diversify ones portfolio today than it has been in the past. IRG (2007) p.17 19 The arithmetic average is the proper statistical estimate for the growth in the expected cash flow value (lognor- mally distributed values). Unlike the geometric average, the arithmetic average will reflect the positive compounding effects of a consecutive run of high returns, and the dampening effects on the future value of consecutive low re- turns.

15 WACC for the Fixed Telecommunications net in Sweden

8.2. ESTIMATE The most commonly used reference for ERP in regulatory purposes is the Dimson, Marsch and Staunton’s (DMS) annually updated study.20 In DMS (2007) the forward looking arithmetic equity risk premium is estimated to be around 5% for the world’s largest markets. DMS (2006) estimate an historical equity risk premium of 4.5%-5%. Another renowned source of estimates is Prof. Damodaran. In 2007 he estimated the forward looking ERP to 4.91%.21

The academic studies yield higher equity risk premium than indicated in recent surveys of financial analysts’ expectations. In the Global Fund manager Survey conducted by Merril Lynch, the mean equity risk premium is 3.5%22 and a survey of Swedish financial analysts’ indicates an equity risk premium of 4.3%23. Based on these findings, the high end of the range of 4,5%-5,0% indicated by the academic studies seems to be high.

Based on the academic studies, but also influenced by the investor surveys, we propose an equity risk premium of 4.75%. Our estimate is well in accordance with European regulatory practice, cf. Figure 8.1.

8.3. REGULATORY REFERENCE We compare our estimate of the equity risk premium to the estimate used by other Euro- pean regulatory authorities. The average estimate used by European regulators is 4.92%. However, as there are outliers that deviate considerably from the average24, the median of 4.80% gives a better illustration of the ERP used by regulators in Europe. We conclude that our estimate is consistent with estimates used by other regulatory authorities.

20 DTe (2005), OFCOM (2005), COMP-COMM (2006), CRAI (2003), Johnsen (2006) and AFORST (2005) all use studies by DMS as their main source when estimating ERP. 21 Damodoran (1998) 22 Merryl Lynch (2007) 23 PWC (2007) 24 Denmark, Netherlands and Greece.

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Figure 8.1 Equity risk premium used in comparable countries

8%

7%

6%

5%

4%

3%

2%

1%

0%

UK Italy Spain Greece Austria France Finland Belgium Norway Denmark Netherlands Switzerland

Source: IRG (2007)

17 WACC for the Fixed Telecommunications net in Sweden

Chapter 9 BETA

Investments always carry risk. The beta is a way of relating the investment’s risk to the mar- ket’s risk. It is measured as the volatility of a stock relative to the overall market volatility. The beta captures the systematic risk, i.e. risks that can’t be eliminated by portfolio diversifi- cation.

9.1. METHOD

Divisional beta The WACC is used to regulate the prices of the fixed telecommunications network. Thus, the regulation should only capture the systematic risk arising from the fixed net. Normally, the overall company beta is used in regulation. However, as soon as a company has several divisions with different risk and financial characteristics, the use of a company WACC be- comes meaningless.25

In telecom regulation, the regulators have to distinguish the different regulated activities from each other and from other non-regulated activities. This has, in most cases, been done by identifying so called pure play operators, i.e. comparable companies who are only active in the regulated industry. This is, for example, the way the mobile telecommunications are regulated in most European countries.26 However, there are no western European operators that provide only fixed net telecommunications services. All telecommunication operators in Western Europe also provide mobile telecommunication services. Thus the issue has to be solved with another methodology.

A company’s beta consists of a weighted average of the betas of the various economic activi- ties that the company is involved in. Telecommunication operators are typically involved in three main types of activities, fixed net services, mobile services and other operations. Thus we can define the telecommunication operators’ betas as:

βCompany= βFixed*wFixed+βMobile*wMobile+βOther*wOther

where β is the beta and w is the weight of each business segment. The weights should reflect the business units’ share of the total company value.

The divisional approach is recognised by the Independent Regulators Group who finds it reasonable from a regulatory point of view to use a differentiated WACC.27

25 Block (2003). 26 This is possible since there are several companies that only provide mobile communication services. 27 IRG (2007).

18 WACC for the Fixed Telecommunications net in Sweden

Our methodological approach to divisional beta is consistent, both with the recommenda- tions in the UK regulators’ report on the cost of capital28, and with the current regulatory model used for the telecommunications sector in the UK29 and Norway30.

Choice of data set When estimating the beta it is necessary to decide on what time period and what frequency to use in the estimations. The estimations will differ depending on these choices, sometimes significantly.

Time period: The most recent time period describes the current beta. However, the beta fluctuates over a business cycle. The CAPM-theory therefore suggests that the beta should be estimated with data covering an entire business cycle. Moreover, estimates based on a limited time period may be affected by specific atypical events in the market that affects the beta temporarily. Making the estimations from a short time period leads to a risk of biased results due to missing information and temporal effects. Thus, there is a trade-off between the rele- vance in the estimation period, due to changes in the underlying economics, and statistically robustness.

Data frequency: The question regarding what frequency of data to use comes down to the selection of whether to use daily, weekly or monthly estimates. They all have their strengths and weaknesses. The choice of frequency is related to the time period selected. If a short time period is used, there is a need for high frequency data in order to have sufficient observations to produce statistically reliable results.

However high frequency data, i.e. daily observations, can lead to significant estimation prob- lems such as:31  Non-synchronous trading bias – where news about the company and market are not translated into prices simultaneously, because the stock exchanges do not open and close at the same time. The speed of translation of information to prices may also be different for frequently and less frequently traded stocks.  Weekend heteroscedasticity – weekend (fri-mon) returns may have greater variance than consecutive weekday returns.  Other issues arising from using high frequency data such as heteroscedasticity, serial correlation, deviations from the normal distribution.

Monthly estimates on the other hand are sensitive to the day of the month on which the ob- servations are made. Switching the estimation date by just a few days can lead to significant

28 Wright, Mason and Miles (2003). 29 Ofcom (2005). 30 Johnsen (2006). 31 Data with these problems will violate the assumptions underlying Ordinary Least Square regression and the CAPM.

19 WACC for the Fixed Telecommunications net in Sweden

differences in the estimated beta. This is a major shortcoming, which casts serious doubt on the use of betas estimated on the basis of monthly data.

We use weekly observation, because they give the most robust results. 32

9.2. BETA ESTIMATION One of the assumptions in the WACC-model used by PTS is that the cost of capital should be calculated from an international investor’s perspective.33 We therefore calculate the betas against the Morgan Stanley World Index, MSCI World.34

As the CAPM-theory is based on predictions that should hold over a business cycle, and since there are no main systematic events that have changed the economic conditions for the telecommunication operators, nor any big disturbances over the estimation period, we de- cide to estimate the betas over a 5 year period. We find that the choice of estimation period, in most cases, does not have a significant effect on the estimate, see for example the case of TeliaSonera in Figure 9.1.

Figure 9.1 Development of TeliaSoneras beta

Beta 1,4 1,2 1 0,8 0,6 0,4 0,2 0 1 2 3 4 5 Yea rs of data

Note: The estimations are based on return indices of the TeliaSonera stock in USD against the MSCI World In- dex. The data set contains weekly observations between 2002-09-02 and 2007-08-31. Source: Copenhagen Economics based on data from Datastream.

We find that TeliaSonera has a significantly higher beta than the average integrated operator in Europe, see Table 9.1.

32 The second trading day of the week is used to avoid weekend heteroscedasticity. 33 PTS (2007), principle 12. 34 The MSCI World Index is a free float- adjusted market capitalization index that is designed to measure global de- veloped market equity performance. As of June 2006 the MSCI World Index consisted of 23 developed market country indices.

20 WACC for the Fixed Telecommunications net in Sweden

Table 9.1 Unlevered betas for European integrated operators Operator Unlevered (asset) beta Robust std. errors TeliaSonera 1,09 0,14 Belgacom 0,69 0,11 BT 0,83 0,13 Deutsche Telecom 0,69 0,14 France Telecom 0,81 0,24 KPN 0,68 0,10 OTE 0,74 0,13 Portugal Telecom 0,44 0,16 Swisscom 0,37 0,01 TDC 0,44 0,16 Telecom Italia 0,45 0,11 Telefonica 0,65 0,12 Telekom Austria 0,35 0,16 Average (except TeliaSonera) 0,60 Note: The estimations are based on return indices of the stocks in USD against the MSCI World Index. The es- timates are based on weekly observations between 2002-09-02 and 2007-08-31. Source: Copenhagen Economics based on data from Datastream.

There is a large difference between the betas for the other integrated operators and the beta for TeliaSonera. Betas are influenced by both market conditions and firm specific effects.

There are two market specific conditions that may have some relevance in this case.

First, the integrated operators provide both fixed and mobile services. From previous regula- tions of the telecommunications sector, we know that the systematic risk is higher for the mobile services than for fixed net services.35 As TeliaSonera has more mobile services than the otherwise comparable integrated operators, we expect TeliaSonera’s beta to be higher.36 This effect is corrected for in section 9.3.

Second, TeliaSonera claims that the node density affects the systematic risk, implying that the systematic risk is higher in areas with a low population density than in densely populated regions, such as central Europe. We have analysed this statement by relating the population density to the asset betas in our sample, see Figure 9.2. Based on the analysis, we cannot rule out that there is an effect from population density on beta. As the population density in Sweden is by far the lowest among the compared countries, part of TeliaSonera’s excessive risk could be explained by the low population density. However, the results are uncertain and the effect appears to be able to explain only a part of the additional risk.

35 See for example ERG (2006). 36 TeliaSonera’s share of mobile telecommunication is 58%, whereas the average of the integrated operators is 49%.

21 WACC for the Fixed Telecommunications net in Sweden

Figure 9.2 Population density and beta

Beta

1,2

1

0,8 0,6 0,4 0,2

0 0 100 200 300 400 500 Population density

Note: Population density is measured as inhabitants / Km2. Source: United Nations World Populations Prospects Report (2004 revision) and Copenhagen Economics.

Firm specific conditions may also have some relevance in this case. In June 2002, the Swed- ish state sold 30% of the shares in an initial public offering, and Telia became a private listed company. Since then, there have been numerous rumours that the Swedish state will further reduce its possessions in the company, and rumours about mergers with other operators, for example Telenor. Such acquisition rumours tend to be pro-cyclical, as merger and acquisi- tions activities generally are more common in a positive economic climate. Hence, when the Morgan Stanley World Index of stock prices is high, the rumours may increase the stock price of TeliaSonera. This will therefore tend to increase the stock’s estimated beta.37 We can not, however, estimate the size of the effects that the rumours concerning TeliaSonera have had on the beta.

In conclusion, TeliaSonera’s estimated beta is significantly higher than the estimates for its European peers. This may partly be explained by the company’s different business mix (higher mobile share), or by period specific market or company factors, e.g. the rumours about changes in the ownership of the company. From a regulatory forward looking perspec- tive, only beta factors, which may be expected to be permanent, should be compensated for. Temporary effects, however, like the ownership rumours, represent an additional risk that an efficient operator does not need to have and such effects should not be included in the regu- latory beta.

9.3. REGRESSION OF FIXED NET BETA To separate the effects of the fixed net from the other activities that TeliaSonera is involved in, we make use of the equation in section 9.1. In principle, the equation could be solved for TeliaSonera itself, if we had access to reliable estimates of beta for comparable companies only involved in mobile telecommunication and other services. However, such a simple es- timate would be unreliable as all the residual effects would be allocated to the fixed net. Us-

37 Gugler, Mueller and Yurtoglu (2006).

22 WACC for the Fixed Telecommunications net in Sweden

ing the methodology in this case would overestimate the beta for TeliaSonera’s fixed net op- erations.

Instead, we estimate the equation from a group of comparable companies. We construct a peer group consisting of twenty western European integrated and mobile operators. A de- scription of the peers can be found in Annex A. The use of both integrated and mobile op- erators enables us to get significant variation in the data which is a desirable feature in a re- gression analysis. For each company in the analysis, we estimate an unlevered (asset) beta and calculate the share of fixed telecommunication, mobile telecommunication and other services.

The weights for each respective business segment should, theoretically, be calculated as the economic value of each segment. However, that information is not available. We therefore base our estimates of value on EBITDA.38 The EBITDA is the operating cash flow and, as it represents the yearly contribution generated by a business segment. Hence, EBITDA is a good estimate of value.

From the data on beta and weights of the business segments, we estimate the equation using a regression analysis in order to infer what the betas for the individual business segments are. The results from the regression analysis are presented in Table 9.2. “Other services” have the highest systematic risk, and fixed net the lowest.39 Due to the uncertainties involved in the regression, we place more emphasis on the direction and the size of the differences of the be- tas, than the absolute estimates themselves.

Table 9.2 Results from regression analysis Fixed net beta Mobile beta Other services beta 0,34 (std. error 0,15) 0,8 (std. error 0,09) 1,7 (std. error 0,57) Note: The full regression output can be found in Annex C. Source: Copenhagen Economics.

We find that the fixed net services are significantly lower than both mobile and other ser- vices. This implies that using an unadjusted beta from an integrated operator would lead to using a too high beta and thus, overstating the cost of capital.

9.4. FIXED NET BETA IN SWEDEN As illustrated in Table 9.1 TeliaSoneras’ beta is significantly higher than its European peers. A part of this can be explained by the company’s higher share of mobile services. However, correcting for this, the TeliaSonera company beta should be 0.61, i.e. slightly over the peer

38 Earnings before interest, taxes, depreciation and amortization. We use the EBITDA per segment stated in each company’s latest annual report. 39 This is consistent with the findings of Ofcom (2005).

23 WACC for the Fixed Telecommunications net in Sweden

average, if it was an average integrated operator.40 However, TeliasSonera has a unlevered beta of 1.09.

The residual risk, which we cannot explain with the model, is thus 0.48 for TeliaSonera.41 A key question is how to handle this risk. In section 9.2 we discussed that this risk may depend on differences in population density and rumours about changes in ownership. Only long term effects should be compensated for in form of a higher beta than the peer group average.

If we assume that all residual risk is caused by market specific differences, i.e. geographic conditions that causes higher beta in Sweden than in other European countries, the fixed net beta should be 0.61.42 If we, on the other hand, assume that all the residual risks are caused by risk specific events, then we should use the estimated fixed net beta of 0.34. We can not estimate the size of these two effects and thus, exactly divide the residual systematic risk be- tween them. Thus, our range for the estimate of the fixed net beta is large, between 0.34 and 0.61. Hence, we must choose a fixed net beta from this interval to calculate the WACC.

We propose that the choice of the fixed net beta value is based on two criteria:

The first criterion is regulatory benchmarking. Studies from other European countries reveal that the beta for the fixed net is approximately 0.6. This speaks in favour of using the high end of the range (0.34 to 0.61), when the fixed net beta is chosen.

The second criterion is regulatory precaution. As described above, it is not possible to de- termine whether the beta is high due to market specific factors in Sweden or due to firm spe- cific factors for TeliaSonera. Moreover the estimates contain statistical uncertainty and should therefore be interpreted with caution. This speaks in favour of using the high end of the interval (0.34 to 0.61), when the fixed net beta is chosen, i.e. 0.61. Any other beta value would be based on an unverified assumption that TeliaSonera temporarily is subject to more systematic risk than it should be over the long run.

In conclusion, we use a beta value of 0.61 in our calculation, i.e. the high end range of the beta range (0.34 to 0.61).

To use the beta in the WACC calculation, we re-lever it with the target gearing range 30%- 50%, using the Modigliani-Miller formula.43

40 This is the so called fit from the regression, see Annex C. 41 TeliaSonera’s unlevered beta, 1.09, minus the regression fit, 0.61. 42 This is calculated by weighing the estimated fixed net beta (0,34) with TeliaSonera’s additional risk (1,09/0,61). 43 We use the assumptions used by Damodaran (2002)  Levered Unlevered  1 (1 Tax)  D / E

24 WACC for the Fixed Telecommunications net in Sweden

9.5. REGULATORY REFERENCES The estimated beta for the fixed net is somewhat lower than the divisional fixed net betas es- timated in the UK and in Norway, who both find betas around 0.6.44

We also benchmark our results with other similar network industries. An analysis of recently estimated betas in the electricity and gas markets shows that the betas for these types of in- dustries are, in most cases, around 0.3, i.e. significantly lower than our estimates for the fixed net, cf. Table 9.3. This is expected since the fixed telecommunication network is subject to direct infrastructure competition in a way that the other compared networks are not.

Table 9.3 Regulatory benchmarks Country (year) Regulated sector Unlevered (asset) beta Sweden (2006) Electricity transmission 0,48 Netherlands (2005) Electricity transmission 0,28 Netherlands (2005) Electricity distribution 0,29 Netherlands (2005) Gas transmission 0,25 Finland (2004) Gas transmission 0,3 Finland (2004) Electricity transmission 0,3 Note: Where beta has been estimated as a range the mid point has been used. Source: Energimundigheten (2007), TenneT (2006) and calculations by Copenhagen Economics.

44 Ofcom (2005) and Johnsen (2006).

25 WACC for the Fixed Telecommunications net in Sweden

REFERENCES

AFORST (2005), Determination of Appropriate Cost of Capital Rates for the Regulated Fixed Services of France Telecom

AMI (2003), Estimating the cost of capital for fixed and mobile SMP operators in Sweden

Block, S. (2003), Divisional Cost of Capital: A Study of its use by Major U.S. Firms, The Engineering Economist, Volume 48, No 4.

Brattle Group (2002), Issues in beta estimation for UK mobile operators

Brealey, Myers and Allen (2006), Corporate Finance, Edition 8

COMP-COMM (2006), Market investigation into supply of bulk liquefied petroleum gas for domestic use

CRAI (2003), Cost of Capital in the UK

Damodaran (1998), Estimating Risk free Rates, Stern School of Business

Damodaran (2002), Investment valuation, Whiley Finance, Second Edition

Dimson, Marsch and Staunton (2007), Global Investment Returns Yearbook 2007

DTe (2005), The Cost of Capital for Regional Distribution Networks

Energimyndigheten (2007), Nätnyttomodellen tariffår 2006 – Beslutsunderlag

ERG (2006), Regulatory Accounting in Practice

Fernández (2007), Equity Premium: Historical, Expected, Required and Implied

Fuller and Kerr (1981), Estimating the Divisional Cost of Capital: An Analysis of the Pure- Play Technique, The Journal of Finance, Volume 36, No 5

Gugler, Mueller & Yurtoglu (2006), The Determinants of Merger Waves; Univ. of Vienna

IRG (2007), IRG – Regulatory Accounting Principles of Implementation and Best Practice for WACC calculation

Johnsen (2006), Kapitalkostnad for norske mobilselskaper

Merryl Lynch (2007), Global Fund Manager Survey

26 WACC for the Fixed Telecommunications net in Sweden

Ofcom (2005a), Ofcom’s approach to risk in the assessment of the cost of capital

Ofcom (2005b), Disaggregating BT’s Beta

PTS (2007) Samråd avseendet behovet av att revidera hybridmodellen för det fasta nätet - Synpunkter på beräkningen avkapitalkostnaden

PWC (2007), Riskpremien på den svenska aktiemarknaden

TenneT TSO (2006), Comparison study of the WACC

Wright, Mason and Miles (2003), A review of certain aspects of the cost of capital - Joint re- port commissioned by U.K. economic regulators (CAA, OFWAT, OFGEM, OFTEL, ORR, OFREG) and the Office of Fair Trading

27 WACC for the Fixed Telecommunications net in Sweden

ANNEX A – COMPANIES IN THE STUDY

In the analysis we make use of a benchmark of peers selected to constitute a good compari- son for the fixed net operations in Sweden. The 12 peers in the study are based on the in- cumbent telecommunication operators in EU 15, cf. Annex A.1. was excluded due to the size of the country, Sweden and Finland were excluded as TeliaSonera is the in- cumbent, and Ireland was excluded as Eirecom is no longer publicly listed. Switzerland has been added to the peer group.

Table Annex A. 1 Integrated European Operators Company Incumbent in Belgacom Belgium BT United Kingdom Deutsche Telecom Germany France Télécom France KPN Netherlands OTE Greece Portugal Telecom Portugal Swisscom Switzerland TDC Denmark Telecom Italia Italy Telefónica Spain Telekom Austria Austria

Further, seven operators mainly active in mobile services in Europe have been included to form part of a larger European telecommunication peer group that is used to estimate the beta for the fixed net, cf. Table Annex A.2.

Table Annex A.2 European Mobile Operators Company Registered in UK Mobistar Belgium Cosmote Greece Drillisch Germany Sonaecom Portugal SFR/ France Telenor Norway

In Table Annex A.3 we illustrate the bond spreads used for calculating the debt risk pre- mium.

28 WACC for the Fixed Telecommunications net in Sweden

Table Annex A.3 DRP for integrated operators Company Maturity Credit Rating DRP Belgacom 2006 2016 A 73 Deutsche Telekom 2006 2016 A- 174 France Telekom 2005 2015 A- 88 France Telekom 2007 2017 A- 98 KPN 2007 2019 BBB+ 143 OTE 2016 BBB+ 112 Telecom Italia 2005 2014 /A 180 Telekom Austria 2005 2017 BBB+ 102 Telenor 2007 2017 BBB+ 116 TeliaSonera 2005 2015 A- 96 TeliaSonera 2007 2017 A- 105 Average 117 Median 105 Source: Datastream

29 WACC for the Fixed Telecommunications net in Sweden

ANNEX B – REGULATORY REFERENCES

We have chosen to benchmark our results on the estimation of the parameters in the WACC against other estimates used by other European regulatory authorities. The countries we use as a benchmark are members of EU15. However, we have removed Luxemburg due to its small size and we have added Norway and Switzerland, who are not members of the EU, but are, neither the less, comparable markets.

The regulatory references are used in most of the above chapters and we have at each time used all data available to us. Unfortunately, we do not have access to every country’s estimate and underlying method for each variable.

Table Annex B.1 Sources of regulatory references Country Source Year UK Ofcom’s approach to risk in the assessment of the cost of capital 2005 Switzerland Kapitalertrag Telekommunikation Festnetz Schweiz Holland The Cost of Capital for KPN's Wholesale Activities : A Final Report for OPTA 2005 Denmark Report on the LRAIC Model: Revised Hybrid Model (version 2.3) IT- og Telestyrelsen 2005 Belgium Décision Seil de Líbpt du 22 Novembre 2006 Concernant le Coût du Capital á Utiliser dans 2006 les Offres de Référence de Belgacom Austria Forward Looking Long Run Incremental Costs for the calculation of interconnection fees 1999 Finland Principer för bedömning av prissättning i fasta nät 2006 Germany Private Correspondence 2007 France Private Correspondence 2007 Portugal Private Correspondence 2007 Greece IRG - Regulatory Accounting Principles of Implementation and Best Practise for WACC cal- 2007 culation Spain IRG - Regulatory Accounting Principles of Implementation and Best Practise for WACC cal- 2007 culation Italy IRG - Regulatory Accounting Principles of Implementation and Best Practise for WACC cal- 2007 culation Norway IRG - Regulatory Accounting Principles of Implementation and Best Practise for WACC cal- 2007 culation

30 WACC for the Fixed Telecommunications net in Sweden

ANNEX C – BETA ESTIMATIONS

In this annex we present the data underlying the regression of the fixed net beta, the output and the statistical test used.

Table Annex C.1 contains all inputs to the regression, i.e. unlevered betas and the share of fixed, mobile and other services. The column “fitted” describes the part of the unlevered beta that is explained by the model. “Residuals” is the difference between the unlevered beta and the fitted values.

Table Annex C.1 Estimated betas Company Unlev beta Share fixed Share mobile Share other residuals fitted Telia Sonera 1,09 0,42 0,58 - 0,48 0,61 Belgacom 0,69 0,47 0,52 0,02 0,08 0,60 BT 0,83 0,36 0,64 - 0,19 0,64 Deutsche Tele- 0,69 0,42 0,58 0,00 0,08 0,61 com France Tcom 0,81 0,38 0,53 0,09 0,10 0,71 KPN 0,68 0,64 0,36 - 0,17 0,51 OTE 0,74 0,44 0,34 0,22 - 0,06 0,80 Portugal Tele- 0,44 0,44 0,48 0,08 - 0,23 0,67 com Swisscom 0,37 0,48 0,48 0,05 - 0,26 0,63 TDC 0,44 0,58 0,34 0,07 - 0,16 0,60 Telecom Italia 0,45 0,56 0,44 - - 0,10 0,55 Telefa 0,65 0,47 0,52 0,01 0,06 0,59 Telekom Aus- 0,35 0,38 0,62 - - 0,28 0,63 tria Vodafone 0,78 - 0,98 0,02 - 0,04 0,82 Mobistar 0,70 - 1,00 - - 0,11 0,81 Cosmote 0,48 - 0,99 0,01 - 0,33 0,81 Drillisch 1,04 - 1,00 0,00 0,23 0,81 Sonaecom 0,97 - 0,91 0,09 0,08 0,89 SFR/Vivendi 1,23 - 0,70 0,30 0,15 1,08 Telenor 0,76 0,13 0,78 0,09 - 0,06 0,82 Source: Copenhagen Economics

Table Annex C.2 Averages Average Unlev beta Share fixed Share mobile Share other Fixed 0,63 46% 49% 4% Mobile 0,85 2% 91% 7% Source: Copenhagen Economics

31 WACC for the Fixed Telecommunications net in Sweden

Table Annex C.3 describes the results from the regression analysis of the fixed net beta.

Table Annex C.3 Regression results Regression results EQ( 1) Modelling Unlevered beta by OLS-CS

Coefficient Std. Error t-value t-prob Part. R^2 Beta mobile 0,8051 0,0923 8,7300 0,0000 0,8175 Beta other 1,7051 0,5748 2,9700 0,0090 0,3411 Beta fixed 0,3431 0,1543 2,2200 0,0400 0,2252 sigma 0,212204 RSS 0.765517309 log-likelihood 4.25059 DW 1.2

no. of observations 20 no. of parameters 3 mean(Unlev beta) 0.709847 var(Unlev beta) 0.0565889 Normality test: Chi^2(2) = 0.96085 [0.6185]

hetero test: F(6,10) = 0.29140 [0.9277]

hetero-X test: F(8,8) = 0.24010 [0.9702]

RESET test: F(1,16) = 1.6510 [0.2171]

Source: Copenhagen Economics

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