The electric utility industry, once an industry that supplied a steady stream of dividends and slow earnings growth, was the subject of investor and government attention due to continuing crises that commenced soon after deregulation in the late 1990’s. During the years after deregulation, California’s electricity industry collapsed, and ENRON, the largest bulk electric power marketer, collapsed, and two of the three investor-owned public utilities in California declared bankruptcy. Additionally, $238 billion market capitalization was destroyed (Seeholzer [1] ), and bond rating down-grades occurred, and a gaping liquidity shortage began to emerge. Roseman and De Martini [2] found that there were 182 utility company bond-rating downgrades and only 15 upgrades. Also, on August 14, 2003, the industry suffered the largest blackout ever that occurred in the Northeastern US, due, in part, to a lack of investment by the industry. The “failure”¹ of regulation leads to reforms toward deregulation that are associated with these events. The industry is partially deregulated as the generation component of the business is unregulated. Additionally, utilities’ bulk power lines or transmission lines had been opened to alternative purveyors of electric power. The electric utility industry remains substantially regulated as the rates of return on all but generation assets are regulated throughout the US.

Utility regulatory policy from a shareholder’s perspective can be viewed as stringent (unfavorable) or lax (favorable). There is no consensus on the characterization of utility regulatory policy in the literature and among practitioners.

This paper searches for information signals of investors on their perspectives toward regulatory policy in electric utility company stock price volatility soon after the deregulation of the US electric public utility industry. Additionally, utility stock returns responses to up and down markets are modeled for evidence of investor information signals of regulatory behavior. Lax regulation should lead to utility stock returns that react strongly to up markets due to weakly-constrained expected upside profits. Utility stock returns should have a small response to down markets with lax regulation. Lax regulation results in earnings levels that have a strong floor that is independent of systematic risk associated with business and earnings cycles. Stringent regulation should generate a large response of utility stock returns to down markets and a small or no response to up markets for the opposite reasons.

Stock returns data and their error distributions usually have skewness and leptokurtosis, or, thick tails, which is well established in the literature. These characteristics cause intercept bias and inefficiency in slope estimates which is discussed in this paper. Therefore, this study applies skewed generalized t (SGT) probability density function (pdf) regression methods that accommodate skewness and kurtosis. Although there are many flexible pdfs that accommodate skewness and thick tails, the SGT has been found to be the pdf that has the best fitting regressions relative to the normal, other pdfs as well as the flexible pdfs for modeling stock returns.

Some of the literature suggests, albeit not strongly, that regulatory policy had been lax, as suggested by Binder and Norton [5] , Nwaeze [6] , and Davidson, Rangan, and Rosenstein [7] , Joskow [8] [9] .

Some industry observers and utility management have concluded that regulation has been stringent. They point to unattractive financial performance and the resulting lack of investment due to uncertainty regarding regulatory treatment of such assets.

Nwaeze [6] [10] suggest that regulators generally do not reduce electricity prices when an electric utility’s actual rate of return on common equity is above the allowed. Joskow [8] [9] also find that utilities do retain profits above the allowed rate of return if electricity rates are not raised. Joskow [8] finds that regulation is relatively lax when factor prices are steady or declining since regulators are not being pressured by customers and utilities. Therefore, the Joskow [8] effect suggests that regulation is stringent when regulators are pressured by customers and utilities, i.e., when the economy is in a recession and stock market prices are depressed.

When general corporate earnings levels are expected to rise, due to an upswing in the business cycle, stock returns rise in such anticipation and an up market occurs. The opposite occurs for down markets. Utility earnings and returns behave in a generally similar manner except that profits may have a strong upside constraint and an associated weak downside constraint, if regulation is stringent. The converse is true with lax regulation. Therefore, one should expect utility stock returns to be relatively more responsive to either down or up markets, depending upon regulatory policy.

Fifty states and the District of Columbia have regulatory jurisdiction of utilities’ retail electric business within their boundaries. Therefore, regulatory policy varies across jurisdictions. Additionally, the US Federal Energy Regulatory Commission regulates the returns on utilities’ wholesale (transmission) assets. In a study sponsored by the US industry trade association, the Edison Electric Institute, Hirst and Kirby [11] found that transmission investment (long-lines used to transmit bulk, wholesale levels of electric power) fell by $115 million annually for 25 years, or $5 billion in 1975 to $2 billion in 2000 in 1997 dollars.

During down markets, corporate profits are expected to fall. Falling expectations for utility profits may obtain but would be dampened under a lax regulatory or moderate (neither lax nor stringent) regulatory regime as utility profitability has a legally defined guideline found in early 20th century case laws of the Hope and Bluefield decisions.² These cases are the standards referred in utility rate cases used for setting allowed rates of returns on common equity. This contrasts with expectations for unconstrained downturns in profits and consequent stock returns for un-regulated firms and markets in general.

This study tests for asymmetry in up and down market utility stock returns as evidence of regulatory policy as perceived by investors soon after deregulation occurred. The change in capital asset market beta (CAPM) associated with the advent of wholesale electric deregulation is estimated and controlled for in the regressions to ascertain how deregulation affected the buffering effect discussed below.

Bawa, Brown, and Klein [12] initially developed the asymmetric response model that was adopted by Pedersen and Satchell [13] for reviewing and testing stock performance measures. That model essentially produces betas for up and down markets, respectively. Asymmetry is captured by differences in up and down market betas. Nwaeze [6] performs a series of regressions of electric utility returns on spreads between actual and allowed rates of return on equity with slope dummies for utility size and regulatory environment. Nwaeze [6] found that stock returns have an asymmetric response to positive and negative abnormal earnings (earnings above or below the allowed rate of return). The empirical results show that positive abnormal earnings have a significant and large positive effect on stock returns and negative abnormal earnings have a significant small negative effect on returns. The notion behind this result is that lax regulation allows for recovery of negative abnormal earnings and utilities retain abnormal positive earnings.

Turning attention to regulation and systematic risk, Peltzman [14] developed the buffering hypothesis that regulation reduces the firm’s systematic risk because utility earnings are protected from demand shocks. It also suggests that the cost of capital may be endogenous as the strength of regulation in a particular state jurisdiction affects the systematic risk of the utility.

The buffering hypothesis has been subject to a number of empirical tests. Davidson, Rangan, and Rosenstein [2] estimate the association between systematic risk of electric utilities and regulatory strength. Categorizing the number of electric utilities by regulatory environment, their results show that roughly 75% of utilities are in jurisdictions that are less binding. They found that regulation affects beta when factor prices, namely, fuel prices, are increasing.

The buffering hypothesis was also tested by Binder and Norton [5] . They concluded that other studies of the buffering hypothesis did not control for other variables that affect risk other than deregulation. They derive an asset beta that is a non-linear function of regulation severity, the Sharp-Lintner core beta (slope in the regression for a demand shock and the returns on the market) and the Ross [15] arbitrage pricing model (APT) core betas (slope coefficients in a regression of demand shocks for a firm on the risk factors common to all security returns). Their empirical results for the electric utility industry support the buffering hypothesis, but their analysis period does not include the mid 1990’s - early 2000’s, the period that involves the fastest movement of the industry toward deregulation.

Binder and Norton [5] find that the decrease in the Sharpe-Lintner core beta indicates that regulators are granting utilities rates of return that are higher than their cost of capital and that the cost of capital or “fair rate of return is a reference to what the lower bound is for the industry.

Nwaeze [10] estimated the impact of the significant electric utility industry reforms adopted over the past 24 years on systematic risk and returns. Using monthly returns for 1976 to 1997, that paper found that deregulation reforms generally reduced the buffering impact and increased systematic risk.

Besanko, Dsouza, and Thiagarajan [16] analyzed electric utility stock price reactions prior to the passing the of the National Energy Policy Act, the legislation that led to the passing of the FERC (Federal Energy Regulatory Commission) Order 888 in April 1996 that commenced wholesale competition and deregulation in the industry. They found that investors had neutral reactions to positive reactions to its passage, yet stock price reactions differed systematically with differences in the utilities’ marginal costs.

Turning to robust estimation of models involving stock returns, this study empirically addresses non-normality of stock returns and associated capital asset pricing model (CAPM) regression error distributions. The distributions of stock returns and their regression errors have both leptokurtosis (thick tails relative to the normal pdf) and skewness. The importance of regression methods that accommodate kurtosis and skewness lies in the bias of the intercept and the inefficiency of the estimates and their resulting statistical tests. Chan and Lakonishok [17] test robust estimation methods compared to OLS for estimating beta. Using both simulations (where true values of the parameters of the distribution are known) of returns and actual returns data, they estimate betas with seven robust methods and OLS. They find that substantial efficiency gains are obtained when returns contain extreme outliers such as returns resulting from good or bad news.

The regression methods used to estimate the models herein use flexible probability density functions that accommodate skewness and leptokurtosis to search for the best fit of the data. Robust estimation of CAPM beta is discussed in Butler, McDonald, Nelson, and White [18] and McDonald and Nelson [19] . Theodossiou [20] and Hansen, McDonald, and Theodossiou [21] discuss a number of flexible pdf’s that can accommodate skewness and kurtosis in the data. These pdf’s include the exponential generalized beta of the second kind (EGB2), the skewed generalized error (SGED), skewed generalized T (SGT), and the inverse hyperbolic sine (IHS) distributions. McDonald, Michelfelder, and Theodossiou [22] use these pdf’s to estimate and test electric utility betas and find that the SGT provides the best fit. McDonald, Michelfelder, and Theodossiou [23] , McDonald and Michelfelder [24] also use these pdf’s on stock returns analysis and financial model regressions such as the CAPM. McDonald, Michelfelder, and Theodossiou [23] also performed robust empirical tests of the CAPM parameters of stocks in general and found that Jensen’s alpha estimation in the single index model was biased when skewness was present in the regression residuals.

Although this investigation has performed the estimations with all of the above mentioned pdf’s along with the symmetric distributions such as the normal, Laplace and the centralized t, the SGT is the pdf chosen for the final estimates, which is highly consistent with much of the aforementioned literature. The SGT estimates are the only set of presented estimates for the sake of brevity. The results of the other pdf’s are available upon request.

The estimation method herein applies robust estimation methods that nests the family of symmetric distributions yet are more general and flexible as they accommodate skewness and kurtosis in the pdf returns data and regression residuals.

The remainder of this paper is organized as follows. Section 3 discusses the returns response models. Section 4 reviews the robust probability density functions for estimation. Section 5 presents the data and estimation results. Section 6 is the conclusion.

The model for estimating asymmetric response of utility stock returns to the market is the asymmetric response model initially developed by Bawa, Brown, and Klein [12] . This model nests the one-factor, mean-variance Sharpe-Lintner CAPM. The Pedersen and Satchell [13] asymmetric response model for examining portfolio performance measures is:

R i , t = α i + β 1 R m , t − + β 2 R m , t + + π i δ t + μ i , t (1)

where R i , t = r i , t − r f , t is excess return for the stock of utility i at period t, r_i,t is the total return on the stock of utility i as defined in more detail below, r_f,t is the risk free rate for period t, R m , t = r m , t − r f , t is the excess market return, r_m,t is the total market return for period t; R m , t − = r m , t − r f , t When r m , t − r f , t < 0 and zero otherwise, and R m , t + = r m , t − r f , t when r m , t − r f , t > 0 . And zero otherwise, and δ_t = 1 when r m , t − r f , t > 0 and zero otherwise,

π i = φ ( β 1 − β 2 ) , φ = E { r m , t − r f , t | r m , t − r f , t > 0 }

and μ i , t is an error term.

The asymmetric responses of stock returns to down and up markets are estimated by tests for difference between β₁ and β₂.

The model for estimating the returns response to the market and deregulation is:

R i . t = α i + α n , i D n , t + α r , i D r , t + ( β i + β i , n D n , t + β i , r D r , t ) R m , t + μ i , t (2)

R m , t − = R m , t for all negative R_m,t’s and zero otherwise; R m , t + = R m , t for all non-negative R_m,t’s and zero otherwise; D_n,t = 1 for all negative R_m,t’s and zero otherwise, and D_r,t = 1 for the post-deregulation period (after April 1996) and zero otherwise. ì_i,t is the error term. This model is the same as Equation (1) discussed above except for the augmentation of the model to include the slope and intercept post-deregulation dummy variables to estimate the buffering effect (see Appendix 2).

Although reform toward introducing competition to the electric utility industry is a process that started in 1978, this paper identifies the issuance of Order 888 on April 24, 1996 by the Federal Energy Regulatory Commission to be the key event in moving the industry toward competition. The main trend toward deregulation occurred between 1990 and 2002 and therefore is the time frame used for data observation. Next the development of the stock total returns for the utilities is discussed.

The utility total stock return, r_i, for period t − 1 to t is

r t = Δ P t + D t P t − 1 = ( P t + D t − P t − 1 P t − 1 ) = ( P t + D t P t − 1 ) − 1

where Δ P t = P t − P t − 1 is the price change of the share from time t − 1 to t, and D_t is the dividend for the period paid at time t.³ The return can be decomposed into its two components, the capital gain and the dividend yields as follows:

1 + r t = P t + D t P t − 1 = ( P t P t − 1 ) ( P t + D t P t ) = ( 1 + Δ P t P t ) ( 1 + D t P t ) = ( 1 + k t ) ( 1 + d t )

where k t = Δ P t / P t − 1 is the capital gain yield or the percent of growth of the stock price from time t − 1 to t and d t = D t / P t is the percent of dividend with respect to the price at the time of payment.

The log-return or continuously compounded return for the period is defined as

l r t = ln ( P t + D t P t − 1 ) = ln ( P t P t − 1 ) + ln ( P t + D t P t ) = ln ( 1 + r t ) = ln ( 1 + k t ) + ln ( 1 + d t ) = l k t + l d t

where l k t = ln ( P t / P t − 1 ) = ln ( 1 + k t ) is the log-capital gain yield and l d t = ln ( 1 + D t / P t ) = ln ( 1 + d t ) is log-dividend yield for the stock. Note that the following relationships exist between the return and log-return measures

r t = e l r t − 1 ,   k t = e l k t − 1     and     d t = e l d t − 1.

The return and log-return for the multi period 0 to s, denoted by r_0,s and lr_0,s, can be computed from the single period returns using the formulas

1 + r 0 , s = ( 1 + r 1 ) ( 1 + r 2 ) ⋯ ( 1 + r s ) = ∏ t = 1 s ( 1 + r t )

and

l r 0 , t = ln ( 1 + r 0 , s ) = ln ∏ t = 1 s ( 1 + r t ) = ∑ t = 1 s ln ( 1 + r t ) = ∑ t = 1 s ln ( 1 + k t ) + ∑ t = 1 s ln ( 1 + d t ) = ∑ t = 1 s l k t + ∑ t = 1 s l d t = l k 0 , s + l d 0 , s

The above formulas assume that the dividend is reinvested each time in the stock of the company immediately after its payment. In such a case, the value of an investment in the stock s periods from now will be

V s = P 0 ( 1 + r 0 , s ) = P 0 exp ( ln ( 1 + r 0 , s ) )

P 0 exp ( ∑ t = 1 s l k t + ∑ t = 1 s l d t ) = P 0 exp ( ∑ t = 1 s l r t )

Given a sequence, V 0 = P 0 , V 1 , V 2 , ⋯ , V s , ⋯ , the return and log-return for each period can be computed easily from them using the equations

r t = V t V t − 1 − 1     and     l r t = ln ( V t V t − 1 )

The flexible pdf’s used for the estimations accommodate skewness and kurtosis. Although not presented in this paper, the normal, centralized t, Laplace, and the flexible pdf’s, exponential beta of the second kind, (EGB2), skewed generalized error distribution (SGED), skewed generalized t (SGT) and the inverse hyperbolic sine (IHS) were used for the regression estimations for comparison and robustness checks. The normal pdf is not appropriate when the error term exhibits a distribution with skewness or thick tails and no symmetric pdf is appropriate when skewness is present in the data or error term. The shape of the normal pdf is completely described by the first and second moments, namely mean and variance. Pdf’s that accommodate flexible values for the third and fourth moments, skewness and kurtosis offer efficient estimates for financial models relative to OLS where skewness or kurtosis is present. Harvey and Siddique [26] , [27] estimate skewness in different industry stock portfolios and market portfolio indices and find substantial evidence of skewness in returns. Theodossiou [20] rejected the assumption of normality, based on tests of skewness and kurtosis for multiple stock exchanges indexes, the Canadian- and Japanese-Dollar exchange rates and the price of gold. Since skewness and kurtosis is found frequently in financial returns, the approach used herein was to consider pdf’s that would appropriately model the asymmetries and thick tails characteristics found in returns data.

Hein and Westfall [28] found that performing event studies with multivariate regressions using traditional dummy variable tests, assuming that the distribution of the error term is normal, can be biased and inefficient and lead to wrong inferences as stock returns are found to be typically non-normally distributed. This paper uses multivariate regressions with event dummies but considered the multitude of pdf’s that accommodate skewness and kurtosis. The chosen flexible pdf is the SGT.

A description of the SGT pdf as well as the other flexible pdf’s considered for the final estimations are found in Hansen, McDonald, and Theodossiou [21] and McDonald, Michelfelder, and Theodossiou [23] . The SGT is presented below as the pdf of choice.⁴

The SGT pdf is specified as:

S G T ( y ; μ , σ , n , k , λ ) = C σ ( 1 + 1 ( ( n − 2 ) / k ) ( 1 + s i g n ( ε ) λ ) k θ k σ k | ε | k ) − ( n + 1 ) / k (3)

where

C = k / ( ( 2 ( n − 2 ) / k ) 1 / k θ B ( 1 / k , n / k ) ) ,

θ = ( k / ( n − 2 ) ) 1 / k B ( 1 / k , n / k ) 0.5 B ( 3 / k , ( n − 2 ) / k ) 0.5 S ( λ ) − 1 ,

S ( λ ) = 1 + 3 ( λ ) 2 − 4 A 2 λ 2 ,

A = B ( ( 2 / k , ( n − 1 ) / k ) ) B ( 1 / k , n / k ) − 0.5 B ( 3 / k , ( n − 2 ) / k ) 0.5 ,

δ = 2 λ A S ( λ ) − 1 ,

B( ) is the beta function, μ and σ are the mean and standard deviation of y, n and k are kurtosis parameters, λ is a skewness parameter obeying the constraint | λ | < 1 , sign is the sign function, and μ = y − μ + δσ is the deviation of y from its mode μ − δσ. Positive values of λ result a positively skewed pdf and vice versa. Note that the parameter δ is the skewness measure SK = (μ − mode(y))/σ. For λ = 0 the SGT yields the GT of McDonald and Newey [29] . Letting n grow indefinitely large gives the SGED and the GED for λ = 0. Standardized values for skewness and kurtosis in the ranges (−∞, ∞) and (1.8, ∞) can be modeled with the SGT.

The asymmetric response models were estimated using maximum likelihood estimation.

This paper investigated the asymmetric response of utility stock price volatility for information signals of investors’ perceptions of utility regulatory policy. Secondly, it estimated the impact of wholesale deregulation on systematic risk, or, the buffering effect associated with deregulation. The results indicate that utility stock returns do respond asymmetrically to daily up and down markets and that the down market response of utility stock returns dominates. This direction of the asymmetry suggests that investors’ perceptions of utility regulatory policy are stringent.

Furthermore, the impact of deregulation on utility stock returns was modeled with pre- and post-wholesale-deregulation estimates of systematic risk. This deregulation required that electric utilities open their bulk power transmission lines to alternative purveyors of electric power. Systematic risk decreased substantially with wholesale competition. The result leads to the notion that the buffering impact was swamped any of a number of possibilities effecting the industry or market. They include the relative risk of the market increased, the cost structure of utilities improved, utilities faced positive demand shocks, prices increased, or the utility industry divested generation to a level that reduced their investment risk.

Some policy implications of these results suggest that stringent or adverse regulation, which attempts to squeeze as much of the financial resources of the regulated utility for lower rates, increases the volatility of utility stocks, investment risk, and therefore the cost of common equity capital. Therefore, lax regulation or regulation that is favorable to investors by reducing risk may have a rate reducing impact as the costs of common equity are less due to lower investment risk. A future area for research is whether lax regulation with a lower cost of common equity capital results in relatively lower rates than in adverse regulatory jurisdictions.

This paper has benefited from many helpful comments of participants at the Rutgers University Center for Research in Regulated Industries Eastern Conference and the Annual Conference of the Multinational Finance Society. The author wishes to thank Panayiotis Theodossiou, Tim Mount, Gene Pilotte, John Broussard, Michael Crew and Uzi Yaari for helpful comments.

The author declares no conflicts of interest regarding the publication of this paper.

Michelfelder, R.A. (2018) Asymmetric Response of Public Utility Stock Returns Volatility to Up and Down Markets and Deregulation. Journal of Mathematical Finance, 8, 576-598. https://doi.org/10.4236/jmf.2018.83037

Symbol Utility Company Name

AEP American Electric Power

CIN Cinergy

CMS CMS Energy

D Dominion Resources

DPL Dayton Power and Light

DTE DTE Energy

DUK DUK Duke Power

ED Consolidated Edison

EDE Empire District

EIX Edison International

ETR Entergy

FPL Florida Power and Light

GMP Florida Public Service

HE Hawaii Electric

IDA Idaho Power

NI NiSource

NU Northeast Utilities

OGE Oklahoma Gas and Electric

PCG Pacific Gas and Electric

PEG Public Service Electric and Gas

PGN Progress Energy

PNM Public Service of New Mexico

PNW Pinnacle West

POM Potomac Electric Power Company

PPL Pennsylvania Power and Light

PSD Puget Power and Light

SCG SCANA Corp

SO Southern Companies

SRP Sierra Pacific Power

TE TECO Energy

TXU Texas Utilities

UIL United Illuminating

UTL Unitil Corp

WEC Wisconsin Electric

WPS WPS Resources

WR Western Resources

The Bawa, Brown, Klein (1981) asymmetric response model can be re-written as follows:

R i . t = α i + D t + + α i − D t − + β i + R m , t + + β i − R m , t − + ε i , t

where D t + = 1 for all positive excess returns and zero otherwise, D t − = 1 for all negative returns and zero otherwise. Add and subtract both sides of the above equation by α i + D t − and β i + D n , t and note

R i . t = α i + ( D t + + D t − ) + ( α i − − α i + ) D t − + β i + ( R m , t + + R m , t − ) + ( β i − − β i + ) R m , t − + ε i , t

can be re-written as:

R i . t = α i + α n , i D n , t + β i R m , t + ( β i − − β i + ) D n , t R m , t + ε i , t

or,

R i . t = α i + α n , i D n , t + ( β i + β i , n D n , t ) R m , t + ε i , t .

In the above equation note that D t + + D t − = 1 for all values of r_m,t and r m , t − = D n , t r m , t . Additionally, D t − is re-written as D_n,t. The final model for estimating asymmetry and the impact of deregulation on returns includes the above model augmented with deregulation slope and intercept dummy variables, D_r,t. The final model is:

R i . t = α i + α n , i D n , t + α r , i D r , t + ( β i + β i , n D n , t + β i , r D r , t ) R m , t + μ i , t

β i , n is the asymmetry coefficient and is the difference between the down and up market betas ( β i − − β i + ) , and β i , r is the coefficient that represents the change in beta due to deregulation and β i is the standard CAPM beta. It follows from the results for the above models that the intercept and slope of the model for the pre-deregulation period are respectively α i and β i and for the post-deregulation period α i + α r , i and β i + β r , i . In this respect, the coefficients α r , i and β r , i show the change in the value of the intercept and slope dummies during the post deregulation period. Statistical significance of these coefficients will indicate that the parameters are different during the two periods. More specifically, a positive β r , i implies that the deregulation period is associated with an increase in systematic risk.