Heterogenous systematic risk in electricity distribution the case of sweden
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Heterogenous systematic risk in electricity distribution - The case of Sweden . Jon Thor Sturluson. Motivation. Electricity distribution and transmission is a natural monopoly Regulation of prices requires: Estimate of cost of operation Historical Efficient benchmark

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Heterogenous systematic risk in electricity distribution - The case of Sweden

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Heterogenous systematic risk in electricity distribution the case of sweden

Heterogenous systematic risk in electricity distribution - The case of Sweden

Jon Thor Sturluson


Motivation

Motivation

  • Electricity distribution and transmission is a natural monopoly

  • Regulation of prices requires:

    • Estimate of cost of operation

      • Historical

      • Efficient benchmark

    • Estimate of reasonable cost of capital

      • Required return to capital

      • Appropriate cost of debt


Weighted average cost of capital

Weighted Average Cost of Capital


Capital asset pricing model capm

Capital Asset Pricing Model (CAPM)


Weighted average cost of capital with capm

Weighted Average Cost of Capital-with CAPM


Scematic respresentation of wacc

Scematic respresentation of WACC


Motivation1

Motivation

  • Estimates of capital costs are often highly aggregate

  • Two initial hypotheses

    • Does size of operations affect cost of capital?

    • Does geographic location affect cost of capital?


Beta and firm size

Beta andfirmsize

  • Negative relationship between size and returns is often suggested

  • Possiblereasons

    • Wrongriskmeasure

    • Wrongreturnmeasure

    • Transactioncosts

    • Vulnerablefirmstendtobe small

    • Cost of andaccesstodebtfinancing/ creditrisk

    • Economies of scaleandscope

    • Concentrationandlack of competition

  • Addingfirmsizemayrenderbetaaninsignificantpredictor of returns

  • Measurement of firmsize (morelater)


Beta and location

Beta and location

  • Operating a network in a rural area may be more risky

    • Higher counterparty risk due to fewer customers

    • Less diversified economy base

    • Regional specific business cycles not diversified

    • Economies of scale and scope

    • Higher operating leverage?


Available data

Available data

  • Data collected and published by the Energy Markets Inspectorate (EMI)

  • Complete financial statements and extensive technical data

  • Panel structure

    • 1998 to 2008

    • 176 / 166 cross sections

    • Extensive adjustments due to mergers and changes in reporting


Two dimension of size

Two dimension of size

  • ENERGY correlates with equity and conventional notions of size

  • NETSIZE depends on geography and agglomeration

  • Together these two variables capture variation in SPARSENESS of networks


Two step method

Two step method

  • Estimate beta for each firm in turn

  • Estimate a model with beta as a dependent variable

  • Three possible sources of heterogeneity considered

    • Spanor size of the network in km of wires (NETSIZE)

    • Size Volume of energy distributed per year (mWh), alternatively total revenu from operations

    • Sparseness or scope in relation to size

    • Other variables consdiered but do not improve fit or change the outcome


1st step fixed effects panel

1st stepfixed-effects panel

Fixed-effects or betas for each firm estimated in a SUR regression

Wide range of betas (not display)

- Some are significant others are not


2nd s tep estimation of size effects on beta

2nd stepEstimation of size effects on beta


Results on beta

Resultsonbeta

  • Spareness effect is significant and nontrivial

  • Parameter estimates are robust to changes in specification

  • The hypothesis that a network with little distribution over a large network is more risky than on average is supported


Results for cost of debt

Results for cost of debt

  • No significant relationship between cost of debt and size

  • Average interest rate premium over 10 year government bonds estimated at 1.19%


Applicability

Applicability

  • Accounting beta need to be translated to market beta

    • Decomposition method (Mendelker and Rhee,1984)

    • Scale w.r.t. existing benchmarks

  • Example

    • Two equally large groups of firms, classified by sparseness

    • Beta scaled to an industry benchmark (0.40)*

    • Sparse firms: b=0.274  rwacc = 4.6%

    • Dense firms: b=0.533  rwacc =5.3%

  • Other assumptions:

  • Risk free rate = 4%

  • Market risk premium = 5%

  • Interest rate premium = 1.19%

  • Marginal tax rate = 38%


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