The Cost of Equity of Network Operators - Empirical Evidence and Regulatory Practice

In many European countries, the deregulation of energy markets leading to the introduction of unbundling and incentive regulation for utilities firms has made the task of setting an adequate cost of equity more difficult. Firstly, Legal Unbundling led to the creation of many legally independent network operators that have to be regulated separately, excluding the generation or sales activities of mother firms. Identifying adequate costs of capital is thereby complicated by the fact that only very few network operators are traded on stock exchanges. Secondly, the increased pressure through incentive regulation schemes has reinforced the importance of setting the equity return adequately. The approaches chosen by regulatory agencies have often been accompanied by heavy criticism regarding methodology and empirical data sets used. In this context the question arises, how regulators set equity returns for network operators and whether the methodologies applied are in line with state-of-the-art capital market models. This paper therefore starts by providing an overview on empirical results, reviewing major published studies of betas and equity returns regarding utilities and network operators. This research helps to identify and discuss the most important drivers of capital costs which is an indispensable groundwork for determining adequate betas. Additionally, an overview of the current practice of regulatory equity return setting is provided. These results are then compared to an empirical analysis based on a recent data set with more than 20 network operators. Based on this data set the required equity returns according to different methodologies (CAPM, Fama-French-TFM, Ross-APT) are computed. This provides evidence that regulatory practice in Europe and Australia ignores the Fama-French-TFM or the APT, even though notably the Fama-French TFM shows the potential to provide improved estimates of required equity returns. The paper concludes by providing a suggestion on how to put the FF TFM into practice accounting for the size of non-stock listed network operators.Network operator, cost of capital, asset pricing models, regulation, cost of equity

Idiosyncratic risk and the cost of capital - The case of electricity networks

We analyze the treatment and impact of idiosyncratic or firm-specific risk in regulation. Regulatory authorities regularly ignore firm-specific characteristics, such as size or asset ages, implying different risk exposure in incentive regulation. In contrast, it is common to apply only a single benchmark, the weighted average cost of capital (WACC), uniformly to all firms. This will lead to implicit discrimination. We combine models of firm-specific risk, liquidity management and regulatory rate setting to investigate impacts on capital costs. We focus on the example of the impact of component failures for electricity network operators. In a simulation model for Germany, we find that capital costs increase by approximately 0.2 to 3.0 percentage points depending on the size of the firm (in the range of 3% to 40% of total cost of capital). Regulation of monopolistic bottlenecks should take these risks into account to avoid implicit discrimination

Idiosyncratic risk and the cost of capital - The case of electricity networks

We analyze the treatment and impact of idiosyncratic or firm-specific risk in regulation. Regulatory authorities regularly ignore firm-specific characteristics, such as size or asset ages, implying different risk exposure in incentive regulation. In contrast, it is common to apply only a single benchmark, the weighted average cost of capital (WACC), uniformly to all firms. This will lead to implicit discrimination. We combine models of firm-specific risk, liquidity management and regulatory rate setting to investigate impacts on capital costs. We focus on the example of the impact of component failures for electricity network operators. In a simulation model for Germany, we find that capital costs increase by approximately 0.2 to 3.0 percentage points depending on the size of the firm (in the range of 3% to 40% of total cost of capital). Regulation of monopolistic bottlenecks should take these risks into account to avoid implicit discrimination