20040206Wilson Direct.pdf

DAVID J. MEYER
SENIOR VICE PRESIDENT AND GENERAL COUNSEL
A VISTA CORPORATION
O. BOX 3727
1411 EAST MISSION AVENUE
SPOKANE, WASHINGTON 99220-3727
TELEPHONE: [phone]
FACSIMILE: [phone]
BEFORE THE IDAHO PUBLIC UTILITIES COMMISSION
IN THE MATTER OF THE APPLICATION
OF A VISTA CORPORATION FOR THE
AUTHORITY TO INCREASE ITS RATES
AND CHARGES FOR ELECTRIC AND
NATURAL GAS SERVICE TO ELECTRIC AND
NATURAL GAS CUSTOMERS IN THE ST ATE
ill IDAAO
CASE NO. A VU-04-
CASE NO. A VU-04-
DIRECT TESTIMONY
DR. WILLIAM T. WILSON
SENIOR ECONOMIST
ERNST & YOUNG, LLP
FOR A VISTA CORPOR T A TION
(ELECTRIC AND NATURAL GAS)
I. INTRODUCTION
Please state your name, business address and present position with Ernst
Young LLP?
My name is Dr. William T. Wilson and my business address is 233 South
Wacker Drive, Chicago, Illinois. My present position is Senior Economist.
Would you describe your educational background and professional
experience?
I received a B.A. degree in economics and finance from Towson State
University in 1986. I received both my masters and Ph.D. in economics (with a concentration
in finance) from Purdue University in 1991. I was assistant professor of economics at Ohio
Northern University in Ada, Ohio for three years. My business experience includes five years
as Vice President and Senior Economist at Comerica Bank in Detroit. I am currently Senior
Economist with Ernst & Young.
What is the scope of your testimony in this proceeding?
My testimony in this proceeding will recommend a range of values for the cost
of equity capital for Avista Corporation, dba Avista Utilities, (Avista) to be used in the
revenue requirement calculation in this case. I will describe my methodology for assessing
industry risk and operating company specific risk, discuss how this methodology was
developed, and explain why this methodology provides an important insight into the process
of assessing an electric utility's cost of equity capital.
Are you sponsoring any exhibits along with your testimony?
Yes, I am sponsoring Exhibit No., which was prepared under my direction.
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What are your conclusions regarding the required return on equity for
investors in Avista?
Avista s cost of equity capital is between 13.10% - 13.32% with a bias
towards the high end of the range due to Avista s relative risk ranking in relation to other
regulated electric operating companies.
II. BACKGROUND ON THE OPERATING RISK METHODOLOGY
Please describe the methodology you utilized for conducting your
analysis.
My analysis utilized the Ernst & Young Operating Approach (OPERA)
methodology for electric utilities. This is a straightforward methodology that incorporates
two simple steps to estimate an electric utility operating company s cost of equity capital.
The first step is to compute a target cost of equity for the average risk electric utility
operating company. The second step is to assess the relative risk of a specific operating
company in relation to the universe of other firms in the industry. Prior to discussing the
details of the methodology, I will provide some background on the development of the
methodology as well as some research and analysis for consideration by this Commission.
Why did Ernst Young develop this methodology?
We developed this methodology in response to a disturbing trend we
discovered while doing work in the industry. The volatility of operating earnings as a
percentage of ratebase among regulated electric utility operating companies has markedly
increased during the 1998-2002 period, when compared to prior periods.Weare concerned
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that firms and regulators are not incorporating this marked increase in the volatility of
operating results into the determination of the cost of equity capital.
Please explain what you mean by earnings volatility?
Earnings volatility is calculated as the standard deviation of the return on
ratebase of electric utility operating companies during a given year. Standard deviation is the
traditional measure used by finance professionals to measure earnings volatility. Higher
volatility implies higher risk.
Why is consideration of earnings volatility important?
It is important because it is indicative of the operating risk faced by these
firms. Investors are concerned with both risk as well as return. Economists and finance
professionals consider volatility to be the primary measure of risk. An investment with an
average return of 12% and a standard deviation of 3% is clearly inferior to an investment
yielding a return of 12% but having a standard deviation of only 1.8%. This is due to the
greater probability that, in any given year, the return received will be different from the
expected. For investors, predictability of earnings is an important factor. Therefore careful
consideration of changes to earnings volatility is important for regulators to consider.
What specifically did your work reveal about the volatility of earnings
among utility operating companies?
In examining the period from 1991 through 1997, 116 electric utility operating
companies as a group demonstrated a very tight distribution of return-on-ratebase, with
standard deviation of returns of approximately 1.55% to 1.97%, as shown on page 1 of
Exhibit No.4. In 1998-, standard deviation of returns-on-ratebase jumped to over 2% in
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1998 and almost 3% in 1999. The industry standard deviation of returns reached almost 5%
in 2000 and remained well above 3% in 2001-2002. In discussing the events that occurred in
California from 2000-2002 with industry colleagues, an argument has been made that the
financial results of these firms during this time period are an anomaly and should be excluded
from analysis. While I do not necessarily agree with the logic underlying this argument, the
data excluding the three California IOUs (reducing the sample size to 113 from 2000-2002)
still demonstrate a risk measure approaching 3% during the 2000-2002 timeframe. Thus
earnings volatility among electric utility operating companies as a group has approximately
doubled over the past 5 years. The graph on page 1 of Exhibit No.4 shows this change in
earnings volatility.
What did you observe about the average actual rate of return on ratebase
earned during this same period?
For the twelve-year period 1991-2002, actual returns on ratebase for regulated
electric utility operating companies averaged 8.47% with a high of 8.72% and a low of
06%, as shown on page 2 of Exhibit No.4. Therefore, during this time period, while the
standard deviation of returns across the industry increased, the average return on ratebase for
the industry remained relatively stable.
What is the reaction of a typical investor to these developments?
From the standpoint of a prospective investor, this is not an attractive
scenario. Risk - exemplified by the standard deviation of returns across the industry - is
increasing dramatically, yet returns are remaining the same. Since one of the strongest
principles in the investing world is the risk-return trade-off, an investor who bears a higher
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risk is rewarded with an expectation of higher returns as compensation for bearing that
additional degree of risk. What my work demonstrates is that risk and return are moving in
different directions for regulated utility operating companies. This fact is likely to make
investors less willing to invest capital in companies in this industry.
As industry risk was increasing, what was happening to allowed returns?
As industry risk was increasing, the average allowed return on equity, (i., the
return on equity granted for regulated utility operating companies in rate cases decided in that
year) fell from 11.43% to 11.16%, as illustrated on page 3 of Exhibit No.
What factors were affecting allowed rates of return for utilities?
Since most cost of capital methodologies for electric utilities are highly
dependent upon the risk-free rate, falling interest rates were a significant factor in the
lowering of allowed returns to be granted by state utility commissions. These lower allowed
returns have not recognized the increased volatility of earnings and increased risk in the
industry.
Please explain the composition of the group of electric utility operating
companies from which you developed your conclusions.
It includes the operating entities in the industry with 2002 ratebase assets of
greater than $250 million. A list of the operating companies utilized is provided on page 4 of
Exhibit No.
What sources of data did you use?
The data were gathered from several sources including FERC Form 1 filings
Platt's PowerDat industry database, Regulatory Research Associates reports and SEC filings.
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The data were reviewed thoroughly and adjustments were made to address one-time
charges/credits and data reporting inconsistencies. These adjustments were made to more
accurately reflect the recurring earnings stream investors are valuing. The result of these
adjustments effectively reduced the volatility that investors would have seen in annual
results. That is, apart from these adjustments, the earnings would exhibit more volatility and
more risk.
Please summarize your findings related to these utilities?
A primary concern I have is that the risk profile of electric utility operating
companies is increasing dramatically. However, few in the industry are focusing on this
issue. Most attention is being targeted at the problems of merchant energy companies. There
is a perception that electric utility operating companies are low risk businesses. The results
of this work demonstrate that the perception is incorrect.
What steps did you then take to further your understanding of these
findings?
First, I wanted to analyze the factors driving earnings. Also, I wanted to
develop new tools to permit utility managers and regulators to better understand the changed
risk profile of regulated electric utility operating companies. This risk profile should then be
reflected in setting the rates of return for regulated companies.
How did you translate this work into a cohesive methodology?
Using detailed industry data, I constructed a model that demonstrated a strong
ability to identify relationships between operating, regulatory and franchise factors, and
actual returns. This model allows us to analyze regulated electric utility operating companies
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across the industry and assess their risk relative to one another. By analyzing reward-risk
measures at the industry-level and at the individual firm level, our methodology provides a
fact-based, empirically supported insight into the investment attributes considered by
providers of equity capital.
What is the value of this methodology to firms, regulators and investors?
This methodology provides an analytical tool to assist firms, regulators and
investors to gauge the risk-return characteristics of regulated utility operating companies and
to identify the effect of specific operational and regulatory factors on individual firms. Many
changes have occurred during the past ten years, as discussed in Dr. Avera s testimony.
There is much less consistency across the industry as compared to ten years ago. The
intention of this methodology is to incorporate additional rigor and analysis into the very
complex process of determining a firm s cost of capital.
III. METHODOLOGY STEPS
What are the steps utilized to compute the target cost of equity for the
average risk electric utility operating company?
The first step of the OPERA methodology computes a target cost of equity for
the average risk electric utility operating company by comparing industry return and industry
risk. Utilizing the framework developed by William Sharpe for measuring the return-risk
profile of equity portfolio managers, the OPERA utilizes the Sharpe ratio:
Returns - Risk-Free Rate
Sharpe Ratio -
-------------------------------------
Equation (1)
Risk (the standard deviation of Returns)
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What is the origin of the Sharpe Ratio?
The ratio is named after its founder, William Sharpe, of Stanford University,
who won the Nobel Prize in Economics in 1990 (Harry Markowitz and Merton Miller were
co-recipients).These three Nobel Laureates are credited for creating the intellectual
framework with which money managers evaluate the risks and rewards of their investments.
Sharpe first introduced the ratio in 1966 (Journal of Business January 1966) to gauge the
performance of mutual funds. Today is it is a universally accepted measure of investment
performance.
Is the Sharpe Ratio widely referenced?
Yes. The measure itself has gained considerable acceptance within the field of
finance. Sharpe originally proposed the term reward-to-variability ratio as the name of his
investment performance measure. Other authors have termed the original version the Sharpe
Index (Radcliff, 1990, p. 286) and (Haugen, 1993 , p. 315) and the Sharpe Measure (Bodie
Kane and Marcus 1993
, p.
804), (Elton and Gruber 1991
, p.
652) and (Reilly, 1989
, p.
803).
Generalized versions have also appeared under various names (see for example Capaul
Rowley and Sharpe, 1993, p.33).In more recent literature, Kazemi, Mahdavi and
Schneeweis (January 2003) examine how a portfolio s Sharpe Ratio can be increased even
when the return distribution significantly differs from normal. Lettau and Uhlig (2000) show
how the Sharpe Ratio can provide a convenient tool for theorists searching for models
capable of explaining asset pricing while Kevin Dowd (2000) examines a new way to
improve the Sharpe Ratio.
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Is the Sharpe Ratio easy to understand and use?
Yes. It postulates that investors care about two things: return (over the risk-
free rate) and risk (standard deviation of returns). The Sharpe Ratio is easy to understand
because it is a distinct quantitative measure that can easily be compared across investments.
A fall in this ratio over time would indicate to investors that the rate of return on a particular
investment or fund is falling per unit of risk. For example, an increase in risk (i.e. - a rise in
the standard deviation of returns) that was not compensated for by a commensurate increase
in return would make the investor worse off even if total returns were not falling.
Conversely, an increase in the Sharpe Ratio would indicate that investors ' returns (over the
risk-free rate) are rising for each unit of risk.
Why is the Sharpe ratio important?
The ratio provides an unbiased look at an investment's performance. It is
based solely on a quantitative measure (i.e. no subjectivity). It is a widely held assumption
among economists and finance professionals that investors will only willingly accept higher
risk if they are compensated by higher expected returns (over and above the risk-free rate).
The Sharpe Ratio gives investors an important tool to evaluate and compare the risk-return
characteristics of any given investment.The Sharpe Ratio is also useful in comparing the
performance of different types of investments and different investing styles.
Where is the Sharpe Ratio used and why?
Financial managers use the Sharpe Ratio in some form to evaluate the reward-
risk ratio of an investment. Given the rapid growth of mutual funds across the globe over the
past decade, the ratio is an effective tool to evaluate relative fund performance. For example
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Morningstar (see www.morningstar.com) provides a popular risk-adjusted rating system for
most mutual funds. Because it uses standard deviation, the Sharpe Ratio can be used to
compare risk-adjusted returns across all asset categories. In short, it's ideal for investors and
fund managers to gauge whether they are getting adequate returns relative to the risk they are
bearing.
Since you are using operating returns and Dr. Sharpe s equation is
commonly applied to market returns, is the application here valid?
Absolutely. Since the operating entities are not public companies, we are
substituting accounting or operating returns for market returns.
From an historical perspective, what has the Sharpe Ratio been for
regulated electric operating utilities?
Historical data from 1991 to 1998 demonstrate industry Sharpe Ratios varying
from a high of 2.82 to a low of 2.23 and an average of 2., as shown on page 5 of Exhibit
No.4. From 1999 to 2002, the ratio plummeted to an average of 1., demonstrating that
regulated electric utility companies have not been awarded returns commensurate to the level
of risk.
Why is Allowed ROE utilized in this calculation?
From a practical standpoint, individual state utility regulators, such as the
Idaho Public Utilities Commission, are somewhat limited in affecting either of the other
inputs (Risk-free rate and Volatility) and, therefore, must focus on the piece of the ratio over
which they have influence.
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Is the Sharpe Ratio an appropriate measure to gauge returns for utilities?
Yes. Looking solely at returns (i.e. - return on rate base) for the operating
companies comprising the sample set would have given a false sense of security to investors
because average annual returns have not materially changed for the industry over the past
decade. The same cannot be said about risk. One of the most salient trends that we have
clearly documented has been the increased volatility of returns on rate base experienced by
these same operating companies over the past decade. The precipitous fall in the Sharpe
Ratio documents that investors have not been adequately compensated for the rise in risk.
Why is the fall in the Sharpe Ratio for the industry a problem?
Investors care about risk as much as they care about returns. If investors are
not being adequately compensated for risk, they will take their capital elsewhere. A decrease
in the Sharpe Ratio demonstrates a change from the historic reward-risk ratio has occurred in
the electric utility industry and may indicate that capital attraction could become more
difficult if this trend continues or accelerates.Dr. Avera s testimony highlights investors
heightened perceptions of risk regarding the utility business in general, and utilities in the
Western United States in particular. This has translated into an unwillingness to invest in
utility securities, especially equity, unless the anticipated return is adequate to compensate for
the increased risk. There is no requirement that investors allocate a portion of their portfolio
to utility securities. If they are not comfortable with the reward-risk ratio, they invest in other
industries.
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Why is the Sharpe Ratio relevant to Avista s cost of equity capital?
As stated earlier, investors consider two factors when deploying capital:
risk and 2) return in excess of the risk-free rate. By utilizing the information provided by the
Sharpe Ratio, the Commission can more precisely incorporate the viewpoint of investors into
decisions on equity allowance. Again, an investment with an average return of 12% and a
standard deviation of 3% is clearly inferior to an investment yielding a return of 12% but
having a standard deviation of only 1.8%. Since many of the current methodologies utilized
in estimating a firm s cost of equity do not directly address the volatility of returns - the
primary measure of risk - the additional insight provided by this analysis is helpful in
determining the appropriate cost of equity capital.
How is the Sharpe Ratio used to calculate a target cost of equity for the
average risk electric utility operating company?
The Sharpe Ratio formula can be expressed to solve for equity allowance by
simply rearranging the Sharpe Ratio (Equation 1) and solving for return:
Returns = (Sharpe Ratio * Risk) + Risk-Free Rate Equation (2)
From 1991 to 1998, the average Sharpe Ratio for the industry was 2., as shown on page 5
of Exhibit No.4. Including the very low Sharpe Ratios from the past 4 years, the average
becomes 2.37 over the twelve-year period from 1991 through 2002, as shown on page 5.
Therefore, the use of a Sharpe Ratio of 2.50 is reasonable and will return the industry'
reward-risk ratio almost to the levels observed from 1991 to 1998. For Risk, we observe the
consistent rise in standard deviation of returns across the industry and, for conservatism
purposes, estimate 2.8%. This number represents a decrease from the 2002 figure and is
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slightly less than the trailing 4-year average from 1999-2002, excluding the three California
IOUs, as illustrated on page 1 of Exhibit No.4. For the Risk-Free rate, 5.2% is used
consistent with Dr. Avera s testimony. Substituting each of these averages in Equation (2)
gIves us:
Returns = (2.5 * 2.8%) + 5.2% = 12.
The result is a target cost of equity for the average risk electric utility operating company of
12.2%.
IV. DETERMINING THE COMPANY SPECIFIC OPERATING RISK
Why is it important to examine the operating risks of individual firms?
In order to properly interpret and utilize the industry data discussed in the
previous pages, it is necessary to understand the drivers of the returns of individual
companies. Each regulated electric operating utility is distinct, and is in at least some ways
different from all of its peers. These include, but are not limited to, differences in franchise
territory, customer types, load profiles, regulatory rules, average retail rate and value chain
responsibilities. By analyzing the operating risks of individual firms, a more rigorous and
empirically-based standard can be utilized in identifying which of these operating
characteristics affect shareholder risk, therefore, allowing for the assignment of the most
appropriate equity allowance.
How is the relative risk of Avista calculated?
I analyzed the operational and financial results of 113 regulated electric
utilities over the time period of 2000-2002. By utilizing regression analysis to identify
relationships between variables (regulatory, operating, and franchise variables) and actual
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financial results, I developed a predictive model. Specifically, Return on Ratebase was
utilized as the dependent variable and 12 variables were identified as being significant, six at
greater than 97% confidence. The actual operating results of the utilities were then entered
into the predictive model to produce a set of predicted results. The utilities were then ranked
on a relative risk basis. Firms displaying the greatest negative variance of predicted results
vs. allowed return were designated as highest risk.
What are the twelve variables you identified as impacting returns?
The twelve variables we identified as being significant are as follows:
1. Allowed return - the equity allowance set by the state commissions
2. Test year utilized - forecasted or historical test period
3. Regulatory jurisdiction - multi-state versus single state
4. Fuel cost / total revenue - fuel cost as a percentage of total revenue
5. Load factor - net retail MWh sold / (Peak Load * 8760)
6. Fuel concentration - diversity of fuel use
7. Bad debt - doubtful retail accounts
8. Retail load growth - annual change in retail load growth
9. Retail rate - average retail rate
10. Vertical integration -presence or absence of energy production
, Purchased power / total revenue - cost of purchased power as a share of
revenue
12. Weather - annual cooling degree days.
Page 6 of Exhibit No.4 contains an explanation of each variable.
Could you please provide an example or two, with an explanation, of the
variables used in the regression analysis?
A. Yes. I will discuss load factor and test year.
Q. What does your analysis indicate about load factor?
A. It shows that companies with high load factors have greater risk than companies
with lower load factors.
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Q. Isn t this finding different than what is commonly assumed in the utility
industry?
A. Yes, this finding is different than what has been commonly assumed in the utility
industry. For many years, higher load factors have generally been thought of as a positive
thing for utility companies. This stems from the fact that higher load factors mean greater
system throughput and therefore better utilization of assets, and as a result, lower average
system cost (other things being equal). What our analysis demonstrates, however, is that
firms with higher load factors face greater risk of lower returns.
Several reasons account for this.First, a higher load factor implies a higher
concentration of large, high-usage industrial and institutional customers. Due to their size
and usage patterns, these customers have considerable negotiating leverage with the utility.
This leverage is manifested in a number of ways. Let me suggest two. First, the large
customer can extract concessions from the utility by threatening to close a facility, install
self-generation, move production to another location or seek legislative provisions that would
allow purchases from third party suppliers. Secondly, larger customers tend to be better
represented in regulatory and legislative forums. This increases the likelihood that their
concerns will be addressed. As a result, margins for large, high load factor customers are
often smaller than for smaller, lower load factor customers.
Another factor driving this finding is that the greater concentration of industrial load
exposes the company to greater general macroeconomic risk, since many industrial
customers' business activity is dependent on the economy. Similarly, some utilities have
significant exposure to specific industry risk, e., aluminum markets, steel production, etc.
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As a result, companies with higher load factors are exposed to much greater volume risk than
those companies with lower load factors.
Q. Please explain your finding regarding test year.
I found that companies for which a future test year is used in regulatory
proceedings have lower risk than those for which a historical test year is utilized. The
analysis shows that when regulators use historical test years (versus forecasted) to determine
revenue requirements and set rates, firms earn lower returns. Using a historical test year
results in making rates based upon information that is at least one year old.Even with the
best of intentions, setting prices for next year and following years based upon last year
revenues and expenses is likely to result in suboptimal results. If a utility company has its
revenue requirements, and therefore rates, set on a year prior to when the rates will be in
effect, the rates will recover less than the current total costs (other things being equal). In this
fashion, utilities that are required to use historical test years are in effect, always trying to
play catch up, as new investment and increases in costs are not captured in the current rate
cycle. Most importantly, since a utility s operating and debt costs still have to be paid in full
the short fall directly impacts returns on equity.
Does this model identify the measurable risks impacting returns for
electric utilities?
Yes.The model is built upon empirical data - actual results from 113
operating companies over a three-year period of time. The data were gathered from many
sources including FERC Form 1 filings, Platt's PowerDat industry database, Regulatory
Research Associates reports and SEC filings. The statistical significance of the individual
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variables is demonstrated by the P-values listed in the ANOV A table on page 7 of Exhibit
No.
Why is Return on Ratebase used instead of Return on Equity?
Across the universe of 113 electric utility operating companies, capital
structures vary significantly, and in some instances the difference between actual and
regulatory capital structures may skew results. Utilizing Return on Ratebase normalizes
these variances and focuses upon the return to all capital providers. Additionally, the use of
standard deviation of the Return on Ratebase across the industry in the Sharpe Ratio is used
for conservatism. If standard deviation of the Return on Equity across the industry were to be
utilized, a wider variance would occur.
How is relative risk translated into an estimate for the cost of equity?
In looking at historical Commission decisions across the u.S. for the last ten
years, a range of approximately 224 basis points, on average, separates the highest allowed
return on equity from the lowest allowed return on equity in a given year, as shown on page 8
of Exhibit No.4. This range does not seem inappropriate to differentiate between firms
facing different operating, franchise and regulatory risks. Using this range and our previously
calculated target cost of equity for the average risk electric utility operating company of
12.20%, a bandwidth from 11.08% to 13.32% is established. Since Avista is determined to
have the fourth highest relative risk in the industry (4th out of 113 companies), a range of
13.10% to 13.32%, representing the top decile of risk, is recommended.
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How is it that Avista ranks fourth from the top?
In examining the eleven variables (other than Return on Ratebase) that have
statistical significance in affecting actual returns, A vista is riskier than average on seven, and
about average on the other four. This clearly indicates that Avista faces considerable risk in
many facets of its electric utility business. A few of the top factors affecting Avista s risk
profile in comparison to the 'average' electric utility are Purchased Power / Total Revenue
Average Retail Rate, Vertically Integrated Operations, High Load Factor and Weather.
V. REVIEW OF THE RECOMMENDED COST OF EQUITY t:Q,.R A VISTA
Would you summarize your recommendations for Avista s cost of equity
capital?
Yes. Based upon a thorough and rigorous review of the reward-risk profile of
regulated electric utility operating companies and the specific operating risks of A vista, the
analysis would support a cost of equity capital for Avista between 13.10% and 13.32%.
How does the OPERA methodology assess the relative risk and cost of
capital of the peer group companies presented in Dr. Avera s testimony?
The results of the OPERA analysis for most of the regulated electric operating
companies in Dr. Avera s testimony are presented below. Sempra s San Diego Gas &
Electric was excluded from the analysis due to the unusual market structure and operating
results experienced in California.
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Operating Company
Risk Ranking
Cost of Equity Range
(1 =highest risk)
Arizona Public Service 102 11.19-11.41%
A vista Utilities 13.10 - 13.32%
Black Hills Corp 11.43 - 11.65%
Hawaiian Electric 12.33 - 12.55%
Maui Electric 11.79 - 12.01 %
MDU Resources 11.31 - 11.53%
Northern States Power - MN 12.05 - 12.27%
Northern States Power - WI 12.23 - 12.45%
PNM 12.29-12.51%
PS Colorado 12.93 - 13.15%
Puget Sound 12.81 - 13.03%
Southwestern Public Service Co.13.01 - 13.23%
How does the OPERA methodology assess the relative risk and cost of
capital of the WECC companies?
The results of the OPERA analysis for the WECC companies are presented
below. San Diego Gas & Electric, Pacific Gas & Electric and Southern California Edison
were excluded from the analysis due to the unusual market structure and operating results
experienced in California.
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Operating Company
Risk Ranking
Cost of Equity Range(l=highest risk)
Arizona Public Service 102 11.19-11.41%
A vista Utilities 13.10 - 13.32%
Black Hills Corp 11.43 - 11.65%
EI Paso Electric 11.97 - 12.19%
Idaho Power 12.89 - 13.11
Nevada Power 13.10 -13.32%
Northwestern Energy 13.10 -13.32%
PacifiCorp 12.79 - 13.01 %
PNM 12.29 - 12.51 %
PS Colorado 12.93 - 13.15%
Portland General Electric 13.07 - 13.29%
Puget Sound 12.81 - 13.03%
Sierra Pacific Power 13.10 - 13.32%
Tucson Electric 12.15 - 12.37%
Does this conclude your pre-filed direct testimony?
Yes
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