Heterogenous systematic risk in electricity distribution the case of sweden
This presentation is the property of its rightful owner.
Sponsored Links
1 / 17

Heterogenous systematic risk in electricity distribution - The case of Sweden PowerPoint PPT Presentation


  • 108 Views
  • Uploaded on
  • Presentation posted in: General

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

Download Presentation

Heterogenous systematic risk in electricity distribution - The case of Sweden

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


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

    • Estimate of reasonable cost of capital

      • Required return to capital

      • Appropriate cost of debt


Weighted Average Cost of Capital


Capital Asset Pricing Model (CAPM)


Weighted Average Cost of Capital-with CAPM


Scematic respresentation of WACC


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 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

  • 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

  • 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

  • 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

  • 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 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 stepEstimation of size effects 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

  • No significant relationship between cost of debt and size

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


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%


  • Login