December Point-of-View

Pauline Ahern, CRRA, Principal - AUS Consultants

Pauline Ahern, CRRA, Principal – AUS Consultants

Sponsored and written by Pauline Ahern, Principal, AUS Consultants 

Traditional rate base / rate of return regulation defines the market based cost of equity as the fair rate of return. The landmark U.S. Supreme Court cases, Hope[1] and Bluefield[2] in turn define the fair rate of return as one which is comparable to returns earned by firms of similar risk, assures confidence in the financial integrity of invested capital, maintains and supports the credit quality of the firm and enables the attraction of capital on reasonable terms in competition with firms of similar risk. Hope goes a step further and enunciates what is known as the “end result” doctrine when it states that it is the impact of a rate order, not the theory or methodology of setting rates, which is paramount.

In setting the allowed rate of return for a utility, specifically, the return on common equity is usually estimated using various financial models, such as the Discounted Cash Flow Model (DCF), Risk Premium Model (RPM), Capital Asset Pricing Model (CAPM), and others applied to the market data of a group of comparable utilities. Using both a group of utilities and multiple models adds reliability and accuracy to the estimation.

However, all of these traditional models have restrictive assumptions behind them, which renders any single model inappropriate for exclusive use in ratemaking. In addition, there has been little movement to adopt more recently developed asset pricing models to provide additional evidence for estimating the cost of capital. That model is the Predictive Risk Premium ModelTM (PRPMTM) developed by Richard A. Michelfelder, Ph.D. (Rutgers University – Camden School of Business), Frank J. Hanley (AUS Consultants), Dylan W. D’Ascendis (AUS Consultants) and myself[3].

The PRPMTM is developed from the work of Robert F. Engle who shared the Nobel Prize in Economics in 2003 “for methods of analyzing economic time series with time-varying volatility (“ARCH”)[4]” with “ARCH” standing for autoregressive conditional heteroskedasticity. In other words, volatility changes over time and is related from one period to the next, especially in financial markets. Engle discovered that the volatility in prices and returns also clusters over time, is therefore highly predictable and can be used to predict future levels of risk and risk premiums. The PRPMTM estimates the risk / return relationship directly by analyzing the actual results of investor behavior rather than using subjective judgment as to the inputs required for the application of other cost of common equity models. In addition, the PRPMTM is not based upon an estimate of investor behavior as are the DCF, RPM, and CAPM models, but rather upon the evaluation of the actual results of that behavior, i.e., the variance of historical equity risk premiums. In other words, the predicted equity risk premium is generated by the prediction of volatility (risk).

The PRPMTM is not necessarily superior to other cost of common equity models in its practical results. Yet, the results do indicate that it should be added to the cost of common equity toolkit to provided additional estimates of the cost of common equity in regulatory ratesetting.

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[1] Federal Power Commission v. Hope Natural Gas Co., 320 U.S. 591 (1944).

[2] Bluefield Water Works Improvement Co. v. Public Serv. Comm’n, 262 U.S. 679 (1922).

[3] “A New Approach for Estimating the Equity Risk Premium for Public Utilities”, Pauline M. Ahern, Frank J. Hanley and Richard A. Michelfelder, Ph.D. The Journal of Regulatory Economics (December 2011), 40:261-278.

“Comparative Evaluation of the Predictive Risk Premium ModelTM, the Discounted Cash Flow

Model and the Capital Asset Pricing Model”, Pauline M. Ahern, Richard A. Michelfelder, Ph.D., Rutgers University, Dylan W. D’Ascendis, and Frank J. Hanley, The Electricity Journal (May, 2013).

[4] www.nobelprize.org

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