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The Application of Banking Models to the Electric Power Industry
CHAPTER 9
The Application of Banking Models to the Electric Power Industry: Understanding Business Risk in Today’s Environment Karyl B. Leggio Henry W. Bloch School of Business and Public Administration University of Missouri at Kansas City Kansas City, MO, USA
David L. Bodde International Center for Automotive Research, Clemson University, Clemson, SC, USA
Marilyn L. Taylor Department of Strategic Management, University of Missouri at Kansas City Kansas City, MO, USA
Introduction Investors, Boards of Directors, and strategic planners are responsible for oversight in a corporation and, consequently, need to more fully understand the extent and character of business risk. Yet the complexity of many industries makes this task difficult. A thorough understanding of the risk factors that cause a firm’s earnings to vary will enhance the Board’s, and management’s, ability to anticipate competitive, environmental, regulatory, and legislative changes and their impact upon the firm. In an era where firms are being called upon to meet increasing financial expectations, managing risk, and thus stabilizing earnings, becomes critical. Currently many firms consider the timing and riskiness of anticipated cash flows in their project approval decision processes. However, existing discounted cash flow (DCF) models 134
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do not go far enough in quantifying risk. Recently, firms began implementing enterprise risk management (ERM) systems to help manage business risk. However, many risks faced by a firm are difficult to quantify using an ERM system. Additionally, managing risk is more than protecting shareholders from downside risk; risk management can be a powerful tool for improving business performance since risk arises from missed opportunities as well as from threats to earnings stability (Lam, 2000). The goal of this narrative is to move toward an enhancement to ERM models by more thoroughly discussing what the risks are, what the sources of risk are, and how to improve our capabilities of identification and response to these risks. A better understanding of risk will stem from an enhanced understanding of what is known, unknown, and unknowable in a firm’s operations. Specifically, we will develop a framework to identify more accurately the business risks faced by firms in the electric power industry by utilizing scenario analysis and contingency planning. We will begin by looking at advancements in the banking industry in the field of risk management. Much of the power industry’s current thinking on risk management can be traced to risk management modeling in banking, most specifically stemming from the Basle Capital Accord. We will look at banking requirements for risk management and their applicability to electric power. We will then look at traditional DCF models and their shortcomings, and move to a discussion of real option analysis and ERM. We will discuss the application of ERM modeling in the electric power industry. The primary risks that businesses look to manage fall into the following broad categories: credit risk such as counterparty exposure; operational risk associated with human error or outright fraud; market risk stemming from exposure to swings in interest rates, foreign exchange rates and commodity prices; and business risk arising from competitive factors that impact costs, sales, and pricing policies. We will look at the importance of considering what are the: ●
known risk exposures in the industry and how Boards of Directors and executive teams can best manage these risks,
●
unknown risks but risks that are knowable with new technologies, additional research, or a shift in resources to aid in making the unknown known; and finally,
●
unknown variables that impact a firm yet no amount of research or resources deployed will help to make these variables known at this time.
We will conclude with a discussion of contingency planning and scenario analysis and discuss how these techniques can be used to illuminate and possibly reduce the unknown risks faced by businesses today.
The Banking Industry Most ERM models can trace their roots to the banking industry; in fact, banks have been at the forefront of risk management for the past 25 years. These models of risk management came from the 1988 Basel Capital Accord that was the product of the Basel Committee on Banking Supervision.
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The 1988 Basel Capital Accord required international banks to hold capital equal to a predetermined percentage of the bank’s assets (Darlington et al., 2001). A key outcome of this approach was the Value at Risk (VaR) metric to assess bank’s risk and capital requirements. VaR measures the likelihood, under normal market conditions, that the institute will experience a loss greater than $X. It is typically calculated on a daily basis and is usually based on a 95% or 99% confidence level. In other words, banks are able to calculate, for example, that the bank is 99% confident that losses will not exceed $20 million on any given day. The advantage of VaR is that it calculates one number to quantify a firm’s risk. Bank management can then decide whether they are comfortable with that level of risk exposure and if their portfolio will generate adequate returns given this level of risk. The disadvantage is also in VaR’s simplicity. Typical banks are exposed to a multitude of risks from numerous sources. Many of these risks are difficult to quantify so risk managers make approximations. These approximations can lead to inaccurate calculations as to the bank’s true risk exposure. To enhance the risk assessment of banks, the Basel Committee released a proposal in 1999 to replace the 1988 Accord. The original Accord applied the same risk metric standards to all banks. Over time and separately, banks began developing increasingly sophisticated internal risk measurement metrics. The Banking Supervisors have come to realize that VaR alternatives for measuring risk may be more appropriate depending on the nature of each bank’s primary business focus. Therefore, the 1999 New Basel Capital Accord for banks’ capital requirements allows for alternative risk and credit worthiness metrics. The New Accord’s goal is to more closely align regulatory capital requirements with underlying firm-specific risks while providing bank managers options for assessing capital adequacy. The proposal is based upon three pillars to evaluate risk: minimum capital requirements, supervisory review, and market discipline. According to William J. McDonough, Chairman of the Basel Committee and President and Chief Executive Officer of the Federal Reserve Bank of New York, “This framework will motivate banks to improve continuously their risk management capabilities so as to make use of the more risk-sensitive options and, thus, produce more accurate capital requirements” (Update on the New Basel Capital Accord, 2001). The first pillar of the New Accord allows banks to replace the VaR metric with alternative risk measurement metrics. Also, in addition to evaluating a bank’s credit and market risk exposure, the New Accord requires banks to account for, and reserve capital for, their operational risk. The second pillar of the New Capital Accord requires supervisory oversight to validate the internal risk measurement processes at each bank and to assure the reserve capital is adequate given the level of risk at each bank. Finally, the third pillar focuses on market disclosure. The goal of this pillar is to improve the transparency of each bank’s capital structure, risk exposures, and capital adequacies with the objective being enhanced market discipline for banks. Many firms in the electric power industry have added retail power businesses; some analysts claim these firms now look very similar to banks with similar exposure to credit, market, and operational risk. As a result, an industry has grown of firms developing and
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implementing ERM systems designed especially for energy firms. And, as the banking industry discovered, VaR is not a sufficient metric to use to capture all risks that an energy firm is exposed to. Alternative risk metrics such as risk adjusted return on capital (RAROC) and Capital at Risk (CaR) are now common calculations for the energy industry. The goal of an ERM system is to consistently and accurately capture all of an industry’s risk exposures and determine what level of capital is required to maintain the firm’s credit rating. Advancements in banking risk management will lead to the development of improved risk metrics in the energy industry. Thus, the energy industry will continue to monitor outcomes from the Basle Accords and other bank regulatory changes.
DCF Techniques Firms consider the risk of new investments prior to undertaking a new project. The firm accounts for risk through the capital budgeting function. In capital budgeting decisionmaking, the goal is to identify those investment opportunities with a positive net value to the firm. DCF analysis is the traditional capital budgeting decision model used. It involves discounting the expected, time dependent cash flows to account for the time value of money and for the riskiness of the project via the calculation of a net present value (NPV). The NPV represents the expected change in the value of the firm if the project is accepted. The decision rule is straightforward: accept all positive NPV projects and reject all negative NPV projects. A firm is indifferent to a zero NPV project as no change in current wealth is expected. Today, most academic researchers, financial practitioners, corporate managers, and strategists realize that, when market conditions are highly uncertain, expenditures are at least partially “reversible,” and decision flexibility is present, the traditional DCF methodology alone fails to provide an adequate decision-making framework. It has been suggested that current corporate investment practices have been characterized as myopic due, in large part, to their reliance on the traditional stand-alone DCF analysis (Pinches, 1982; Porter, 1992). An alternative project valuation method is real options analysis (ROA). Real options are a type of option where the underlying asset is a real asset, not a financial asset. In general, real options exist when management has the opportunity, but not the requirement, to alter the existing strategic investment decision. The most general or all inclusive real option is the option to invest (Pindyck, 1991; Dixit and Pindyck, 1994). The analogy is to a financial call option: the firm has the right, but not the obligation, now or for some period of time, to undertake the investment opportunity by paying an upfront fee. For example, by purchasing an option on land, an energy firm has the option to invest in the design and development of a new power plant to be built on that land. As with financial options, the option to invest is valuable due to the uncertainty relating to the underlying asset’s future value where, in this case, the underlying asset is the power plant. The investment rule is to invest when the present value of the benefits of the investment opportunity is greater than the present value of the direct cost of the investment opportunity plus the value of keeping the option to invest “alive.” Begin building the power plant when the value of building the plant now exceeds the sum of the present value of the cost of building the power plant plus the value of keeping the option to build alive. The decision
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to build will be based, in part, on the projected prices of energy and natural gas and the projected available supply and demand for power in the region. Each investment opportunity may be modeled in “total” as an option to invest. However, the investment opportunity itself may contain various individual real options or embedded real options such as the option to invest in a project in stages. Each decision point or “go/no go” decision is another real option to be valued. The complexity of valuing these embedded options is one of the disadvantages of real option analysis; the primary value in a ROA may derive from the process management uses to identify the options in a project. In looking for optionality in a project, a company must evaluate the future and identify the set of possible scenarios that can come about if the firm pursues the project. This requires a reasonable amount of brainstorming and looking at the project or decision from various angles. Typically there are one or two real options that capture the bulk of the uncertainty value in a project, but generating a list of potential future outcomes creates a process of thinking beyond the obvious that benefits the company. We will discuss this process further in the “Scenario Analysis” section.
ERM The problem with using a DCF method of analyzing risk is that it evaluates risk for the company one project at a time. Companies now realize this silo effect of risk management does not accurately depict the risk facing a firm. In some cases, risk in one division counterbalances risk in another division and the overall firm risk is reduced. Alternatively, and more concerning, is similar risks in different divisions may have the effect of amplifying a firm’s exposure. When corporations evaluate project risk or even divisional risk they fail to accurately depict the firm’s exposures; this can have costly ramifications. An ERM program, properly implemented, eliminates the problem of risk management by division. It requires a firm to identify firm-wide risks, quantify these risks, assess correlations, track changes in the organization that led to the risk exposure, and develop appropriate means of managing the risk. The goal of an effective ERM system is similar to the goal of DCF modeling: to improve the quality of decision-making by implementing a structure that identifies risk and analyzes the impact of the risk on firm performance. Whereas DCF analyzes risk on a project by project basis, ERM identifies and manages risk for the entire firm. ERM is defined as “the process of systematically and comprehensively identifying critical risks, quantifying their impacts, and implementing integrated risk management strategies to maximize enterprise value” (Darlington et al., 2001). The key factors are that ERM is a process that needs to be continually monitored and adapted to the changing corporate environment; it is not a one time activity. ERM is a comprehensive system of risk assessment, and it evaluates risk for the firm as a whole. This requires an understanding of the exposures of varying divisions. Finally, management must quantify the impact of risk on the firm. By understanding, mitigating, and managing risk, management is able to ascertain the capital needed to fund and grow the organization.
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Creating an ERM for Power Companies Competitive triage, deregulatory uncertainty, creation of new risk management products – the electric utility industry has coped with major changes such as these issues since its inception. However, the 21st Century has made issues such as these even more salient. For example, the recent industry turmoil from the fallout of the Enron scandal and the transmission-related blackout for a portion of the Eastern United States have been among the events adding to the challenges. The electric power industry has been called to evolve from a monopolistic “cost plus” business model to a competitive marketplace, able to adapt to changing regulatory and legislative agendas. The industry grew as new competitors with alternative sources of power entered the market. Firms split along functional lines, leaving companies that specialize in generation, transmission or distribution of power, sometimes leading to instability in the basic infrastructure of power. As a result of the Enron scandal, energy firms must now consider the governance issues facing their firms in these turbulent times. What accounting and corporate oversight is needed to reassure analysts as to the stability of this industry? What can we learn about risk management from the banking industry? And what can we do to reduce the risk associated with producing a non-storable asset with demand contingent upon the uncertainty of weather. ERM models serve the dual function of providing transparency to the market and the Board and assisting management in assessing the true value and risk of the firm. The business risk facing the power industry dramatically changed following deregulation. What the power industry and regulators know and understand is how to manage a monopolistic industry with no competitors and a rate of return set by state regulators; they have been doing this for decades. But this is the past in the electric utility industry. The new power industry is one of competition and deregulation. While deregulation began decades ago, it continues today. This process has brought great change to the industry; however, it is not done. The Federal Energy Regulatory Committee continues to consider regulatory change while states gradually approve competition for power within their borders. At the same time, pending legislation has far-reaching ramifications for this industry. The risks and competitive environment in power are evolving. Competitive pressures mount in this industry. Record cold in the late 1990s sent power prices skyrocketing and lead to the announcement of many additional power plants that were to be built throughout the U.S. to increase the supply of power and reduce the likelihood of exorbitant power prices in the future. However, a recession and several mild winters left many companies canceling plans to build these plants. What is the status of new power generation in the U.S.? And what international competitive pressures loom for U.S. firms? Questions such as these deserve careful consideration by power executives. The pressure mounts for management and Boards of Directors of energy companies to provide active oversight as to managerial actions. However, the complexity of the industry has made this task increasingly difficult. The use of derivative instruments can be a hedge or risk reduction strategy; however, they can also be used to speculate and increase the riskiness of the firm. And recent spectacular corporate failures due to the misuse of derivatives such as Barings Bank and Orange County tell us that a Board must adequately
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understand and monitor managerial activity. An ERM system must be able to aid the Board in its oversight responsibilities. Unlike other commodities, power is not storable. This trait makes the market for power infinitely more volatile than the market for other commodities. Concurrently, demand for power is highly dependent upon weather, an unpredictable variable. Additionally, the price for power is dependent upon the available supply. As firms cancel plans to build new generating facilities, the future stability of the industry is threatened. Firms must work to understand weather patterns and the latest weather forecasting techniques; must understand what derivative instruments can be used to reduce the firm’s exposure to extreme weather conditions; must work to manage the volatility and the seasonality of the industry; and should model to predict the future demand and supply for power in a given region. These are some of the challenges of developing an efficient ERM system in the power industry given the challenges of the business. The goal of the ERM program is to develop a methodology to adequately identify and model the risk of a project in order to determine what activities will add value to the firm. Efficiently allocating capital is critical to the future success of a power company. In addition to improving the firm’s ability to manage capital, a process for efficiently deploying capital will improve return on equity while ensuring solvency to a standard demanded by debtholders. An effective ERM process must be supported by business tools. ERM will help managers understand risk exposure and the diversifying effects of the business units, improve the firm’s competitive position, increase the company’s access to capital, determine the firm’s optimal capital allocation given a level of risk, and enhance it’s image in the market as an innovative company. Fundamentally, ERM will aggregate risk on an enterprise-wide basis and produce a risk profile of the firm. This model will determine a current value distribution and will allow analysis, prior to spending capital, of the impact potential new capital projects will have on firm value and debt rating. In order to develop this model, a bottom up approach is taken. A 1-year time horizon for analyzing risk is often chosen since it is consistent with financial market reporting models. The key challenge in developing an ERM system is understanding the risks of the entire organization. Managers representing every business unit must be interviewed to determine how each group manages risk, values market opportunities and determines project performance. Furthermore, since the ERM model must be forward looking, an effort must be made to understand the business strategy that drives each group’s approach to the market. Based on the results of these interviews, risk drivers for each group and correlations between risk drivers need to be identified, and an approach to modeling each group’s value distribution then is developed. In the power industry, market risk is heavily driven by two commodities: gas (an input to much of the power produced) and electricity. The general approach underlying ERM is to value an asset portfolio using a set of projections representing the price of each commodity affecting the business. The correlations between various groups’ exposure to commodity risk needs to be measured, then each business unit’s value distribution will be modeled using relevant projections. These distributions are then aggregated to come up with a single enterprise-wide value distribution.
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ERM also is a tool for decision making when evaluating capital projects. To date, most investments are evaluated on a stand-alone basis, with traditional NPV or Internal Rate of Return (IRR) approaches. In these cases, the choice of hurdle rates has been essential to deciding on whether or not to proceed with a project. No formalized consideration has been given to a project’s effect on the firm’s overall risk profile. ERM allows managers to determine a project’s marginal contribution to value, taking into consideration the effects of diversification with existing projects. ERM is a vital tool to help managers determine a firm’s risk profile and to analyze the impact potential capital projects will have on the company’s overall risk. This tool helps managers deploy capital more efficiently throughout the company and will provide executive leadership with the ability to steer the company towards the return on equity and solvency standards demanded by the market. This ability to link project by project decision making with a firm’s overall strategic vision will improve access to capital and increase the value the firm brings to its shareholders. Known Versus Unknown “It’s not what we don’t know that causes trouble. It’s what we know that ain’t so.” –Will Rogers In quantifying risk, one of the difficulties is in identifying risk. From our earliest years in school, we learn by reading textbooks. The assumption is that these books, with their vast resources and authoritative and confident tone, represent all there is to know about a given subject; we grow up thinking that more is known than actually is (Gomory, 1995). By learning and questioning, we stretch our understanding beyond the original text. Unanswered questions lead us to identify what is known and what is unknown. The unknown represents risk. We need to differentiate between the unknown and the unknowable. Quite often, what is unknown today becomes known in the future. Scientific discovery expands the bounds of our thinking. But there is a limit. Some phenomena are unknowable. Take for example, the weather. In determining when to shut down a power plant for routine maintenance, a power company looks at historic weather trends and chooses a month to shut down the plant when temperatures are typically mild and demand for power is typically low; for example, the month of October. The power company cannot know with certainty that the current year’s weather pattern will follow historic weather patterns. This could be the year with an unseasonably hot October and demand for power to run air conditioners exceeds expectations. The actual weather during the next October is unknowable and no amount of modeling will change that. Additionally, the perspective of the provider of knowledge influences what we know. A power plant operator in West Virginia may believe he knows everything there is to know about running a power plant. However, if his knowledge base comes strictly from operating his coal-fired generation facility, that knowledge may be useless to a hydro-powered plant operator in Oregon. Our perspective also influences our understanding of risk. Some individuals are, by nature, more comfortable assuming risk. Acknowledging and considering alternative attitudes towards risk becomes an important component in risk management.
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One method of accounting for differing knowledge, alternative outcomes, and varying attitudes towards risk is with scenario analysis.
Scenario Analysis Not all risks are known; nor are all risks quantifiable. Yet the goal of an ERM system is to capture all risk and factor it into the decision-making process for the firm. The system must be flexible to adapt as new developments become relevant to the decision-making process. Sensitivity and scenario analyses are means of encouraging management to look at an array of possible outcomes and their impact upon the firm’s earnings. Sensitivity analysis and scenario analysis have been used, and are used, to supplement a traditional DCF analysis. Sensitivity, or “what if,” analysis is generally performed on the determinants of the cash flows. It is reasonably easy to perform, helps identify the principle threats to the project, and calculates the consequences of incorrectly estimating a variable (i.e., the effect on NPV). However, sensitivity analysis only evaluates one variable at a time. Scenario analysis does consider internally consistent combinations of variables. It leads into challenge thinking. The goal is for teams to challenge traditional beliefs and push at the boundaries of knowledge which may lead to innovative ideas and approaches. Scenarios allow management to factor uncertainty into their decision-making process. Management looks at all possible outcomes without assigning probabilities for the likelihood of an outcome occurring. In considering what could happen, management may uncover previously unheard of opportunities as well as identifying additional risk factors in the project. “The one enduring competitive advantage of an organization is it’s ability to learn better and faster than its competition.” – Arie de Geus Scenario analysis and contingency planning allow organizations to adapt faster than the competition. Teams look at all possible developments in a project. This allows flexibility in divisional strategy to react to events that management has now considered. This process makes management more flexible; they spend time thinking beyond the obvious, predictable and desired outcomes. Scenario analysis forces management to think creatively; contingency planning forces management to plan creatively. Scenarios are used to develop an action plan. Scenario analysis should lead to the conclusion that the original strategy is sound or that there are warning signals that need to be attended to. It forces an understanding of the causal relationship between seemingly unrelated factors. The goal of ERM is to understand risk, align the firm’s strategy with corporate objectives, and minimize the probability of unexpected outcomes. Risk management is, and should be, a strategic priority. Scenario analysis and contingency planning work together to minimize the unknown which leads to the ability of a firm to account for and manage risk.
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References Basel Committee on Banking Supervision at the Bank for International Settlements, “The New Basel Capital Accord,” CH-4002, Basel, Switzerland, 2001. Darlington, Angela, Simon Grout and John Whitworth, “How Safe is Safe Enough? An Introduction to Risk Management”, The Staple Inn Actuarial Society, June 12, 2001. Dixit, Avinash and Robert Pindyck, “Investment Under Uncertainty: Keeping One’s Options Open,” Journal of Economic Literature, Nashville, December, 1994. Gomory, Ralph E., “An Essay on the Known, the Unknown, and the Unknowable”, Scientific American, June, 1995. Lam, James, “Enterprise Risk Management and the Role of the Chief Risk Officer,” ERisk, March 25, 2000. Pinches, George, “Myopia, Capital Budgeting and Decision-Making”, Financial Management, 11(3), 1982, 6–20. Pindyck, Robert, “Irreversibility, Uncertainty, and Investment”, Journal of Economic Literature, Nashville, September, 1991. Porter, Michael, “Capital Disadvantage: America’s Failing Capital Investment System”, Harvard Business Review, Boston, September/October 1992.
The Application of Banking Models to the Electric Power Industry: Understanding Business Risk in Today’s Environment Karyl B. Leggio Henry W. Bloch School of Business and Public Administration University of Missouri at Kansas City Kansas City, MO, USA
David L. Bodde International Center for Automotive Research, Clemson University, Clemson, SC, USA
Marilyn L. Taylor Department of Strategic Management, University of Missouri at Kansas City Kansas City, MO, USA
Introduction Investors, Boards of Directors, and strategic planners are responsible for oversight in a corporation and, consequently, need to more fully understand the extent and character of business risk. Yet the complexity of many industries makes this task difficult. A thorough understanding of the risk factors that cause a firm’s earnings to vary will enhance the Board’s, and management’s, ability to anticipate competitive, environmental, regulatory, and legislative changes and their impact upon the firm. In an era where firms are being called upon to meet increasing financial expectations, managing risk, and thus stabilizing earnings, becomes critical. Currently many firms consider the timing and riskiness of anticipated cash flows in their project approval decision processes. However, existing discounted cash flow (DCF) models 134
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do not go far enough in quantifying risk. Recently, firms began implementing enterprise risk management (ERM) systems to help manage business risk. However, many risks faced by a firm are difficult to quantify using an ERM system. Additionally, managing risk is more than protecting shareholders from downside risk; risk management can be a powerful tool for improving business performance since risk arises from missed opportunities as well as from threats to earnings stability (Lam, 2000). The goal of this narrative is to move toward an enhancement to ERM models by more thoroughly discussing what the risks are, what the sources of risk are, and how to improve our capabilities of identification and response to these risks. A better understanding of risk will stem from an enhanced understanding of what is known, unknown, and unknowable in a firm’s operations. Specifically, we will develop a framework to identify more accurately the business risks faced by firms in the electric power industry by utilizing scenario analysis and contingency planning. We will begin by looking at advancements in the banking industry in the field of risk management. Much of the power industry’s current thinking on risk management can be traced to risk management modeling in banking, most specifically stemming from the Basle Capital Accord. We will look at banking requirements for risk management and their applicability to electric power. We will then look at traditional DCF models and their shortcomings, and move to a discussion of real option analysis and ERM. We will discuss the application of ERM modeling in the electric power industry. The primary risks that businesses look to manage fall into the following broad categories: credit risk such as counterparty exposure; operational risk associated with human error or outright fraud; market risk stemming from exposure to swings in interest rates, foreign exchange rates and commodity prices; and business risk arising from competitive factors that impact costs, sales, and pricing policies. We will look at the importance of considering what are the: ●
known risk exposures in the industry and how Boards of Directors and executive teams can best manage these risks,
●
unknown risks but risks that are knowable with new technologies, additional research, or a shift in resources to aid in making the unknown known; and finally,
●
unknown variables that impact a firm yet no amount of research or resources deployed will help to make these variables known at this time.
We will conclude with a discussion of contingency planning and scenario analysis and discuss how these techniques can be used to illuminate and possibly reduce the unknown risks faced by businesses today.
The Banking Industry Most ERM models can trace their roots to the banking industry; in fact, banks have been at the forefront of risk management for the past 25 years. These models of risk management came from the 1988 Basel Capital Accord that was the product of the Basel Committee on Banking Supervision.
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The 1988 Basel Capital Accord required international banks to hold capital equal to a predetermined percentage of the bank’s assets (Darlington et al., 2001). A key outcome of this approach was the Value at Risk (VaR) metric to assess bank’s risk and capital requirements. VaR measures the likelihood, under normal market conditions, that the institute will experience a loss greater than $X. It is typically calculated on a daily basis and is usually based on a 95% or 99% confidence level. In other words, banks are able to calculate, for example, that the bank is 99% confident that losses will not exceed $20 million on any given day. The advantage of VaR is that it calculates one number to quantify a firm’s risk. Bank management can then decide whether they are comfortable with that level of risk exposure and if their portfolio will generate adequate returns given this level of risk. The disadvantage is also in VaR’s simplicity. Typical banks are exposed to a multitude of risks from numerous sources. Many of these risks are difficult to quantify so risk managers make approximations. These approximations can lead to inaccurate calculations as to the bank’s true risk exposure. To enhance the risk assessment of banks, the Basel Committee released a proposal in 1999 to replace the 1988 Accord. The original Accord applied the same risk metric standards to all banks. Over time and separately, banks began developing increasingly sophisticated internal risk measurement metrics. The Banking Supervisors have come to realize that VaR alternatives for measuring risk may be more appropriate depending on the nature of each bank’s primary business focus. Therefore, the 1999 New Basel Capital Accord for banks’ capital requirements allows for alternative risk and credit worthiness metrics. The New Accord’s goal is to more closely align regulatory capital requirements with underlying firm-specific risks while providing bank managers options for assessing capital adequacy. The proposal is based upon three pillars to evaluate risk: minimum capital requirements, supervisory review, and market discipline. According to William J. McDonough, Chairman of the Basel Committee and President and Chief Executive Officer of the Federal Reserve Bank of New York, “This framework will motivate banks to improve continuously their risk management capabilities so as to make use of the more risk-sensitive options and, thus, produce more accurate capital requirements” (Update on the New Basel Capital Accord, 2001). The first pillar of the New Accord allows banks to replace the VaR metric with alternative risk measurement metrics. Also, in addition to evaluating a bank’s credit and market risk exposure, the New Accord requires banks to account for, and reserve capital for, their operational risk. The second pillar of the New Capital Accord requires supervisory oversight to validate the internal risk measurement processes at each bank and to assure the reserve capital is adequate given the level of risk at each bank. Finally, the third pillar focuses on market disclosure. The goal of this pillar is to improve the transparency of each bank’s capital structure, risk exposures, and capital adequacies with the objective being enhanced market discipline for banks. Many firms in the electric power industry have added retail power businesses; some analysts claim these firms now look very similar to banks with similar exposure to credit, market, and operational risk. As a result, an industry has grown of firms developing and
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implementing ERM systems designed especially for energy firms. And, as the banking industry discovered, VaR is not a sufficient metric to use to capture all risks that an energy firm is exposed to. Alternative risk metrics such as risk adjusted return on capital (RAROC) and Capital at Risk (CaR) are now common calculations for the energy industry. The goal of an ERM system is to consistently and accurately capture all of an industry’s risk exposures and determine what level of capital is required to maintain the firm’s credit rating. Advancements in banking risk management will lead to the development of improved risk metrics in the energy industry. Thus, the energy industry will continue to monitor outcomes from the Basle Accords and other bank regulatory changes.
DCF Techniques Firms consider the risk of new investments prior to undertaking a new project. The firm accounts for risk through the capital budgeting function. In capital budgeting decisionmaking, the goal is to identify those investment opportunities with a positive net value to the firm. DCF analysis is the traditional capital budgeting decision model used. It involves discounting the expected, time dependent cash flows to account for the time value of money and for the riskiness of the project via the calculation of a net present value (NPV). The NPV represents the expected change in the value of the firm if the project is accepted. The decision rule is straightforward: accept all positive NPV projects and reject all negative NPV projects. A firm is indifferent to a zero NPV project as no change in current wealth is expected. Today, most academic researchers, financial practitioners, corporate managers, and strategists realize that, when market conditions are highly uncertain, expenditures are at least partially “reversible,” and decision flexibility is present, the traditional DCF methodology alone fails to provide an adequate decision-making framework. It has been suggested that current corporate investment practices have been characterized as myopic due, in large part, to their reliance on the traditional stand-alone DCF analysis (Pinches, 1982; Porter, 1992). An alternative project valuation method is real options analysis (ROA). Real options are a type of option where the underlying asset is a real asset, not a financial asset. In general, real options exist when management has the opportunity, but not the requirement, to alter the existing strategic investment decision. The most general or all inclusive real option is the option to invest (Pindyck, 1991; Dixit and Pindyck, 1994). The analogy is to a financial call option: the firm has the right, but not the obligation, now or for some period of time, to undertake the investment opportunity by paying an upfront fee. For example, by purchasing an option on land, an energy firm has the option to invest in the design and development of a new power plant to be built on that land. As with financial options, the option to invest is valuable due to the uncertainty relating to the underlying asset’s future value where, in this case, the underlying asset is the power plant. The investment rule is to invest when the present value of the benefits of the investment opportunity is greater than the present value of the direct cost of the investment opportunity plus the value of keeping the option to invest “alive.” Begin building the power plant when the value of building the plant now exceeds the sum of the present value of the cost of building the power plant plus the value of keeping the option to build alive. The decision
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to build will be based, in part, on the projected prices of energy and natural gas and the projected available supply and demand for power in the region. Each investment opportunity may be modeled in “total” as an option to invest. However, the investment opportunity itself may contain various individual real options or embedded real options such as the option to invest in a project in stages. Each decision point or “go/no go” decision is another real option to be valued. The complexity of valuing these embedded options is one of the disadvantages of real option analysis; the primary value in a ROA may derive from the process management uses to identify the options in a project. In looking for optionality in a project, a company must evaluate the future and identify the set of possible scenarios that can come about if the firm pursues the project. This requires a reasonable amount of brainstorming and looking at the project or decision from various angles. Typically there are one or two real options that capture the bulk of the uncertainty value in a project, but generating a list of potential future outcomes creates a process of thinking beyond the obvious that benefits the company. We will discuss this process further in the “Scenario Analysis” section.
ERM The problem with using a DCF method of analyzing risk is that it evaluates risk for the company one project at a time. Companies now realize this silo effect of risk management does not accurately depict the risk facing a firm. In some cases, risk in one division counterbalances risk in another division and the overall firm risk is reduced. Alternatively, and more concerning, is similar risks in different divisions may have the effect of amplifying a firm’s exposure. When corporations evaluate project risk or even divisional risk they fail to accurately depict the firm’s exposures; this can have costly ramifications. An ERM program, properly implemented, eliminates the problem of risk management by division. It requires a firm to identify firm-wide risks, quantify these risks, assess correlations, track changes in the organization that led to the risk exposure, and develop appropriate means of managing the risk. The goal of an effective ERM system is similar to the goal of DCF modeling: to improve the quality of decision-making by implementing a structure that identifies risk and analyzes the impact of the risk on firm performance. Whereas DCF analyzes risk on a project by project basis, ERM identifies and manages risk for the entire firm. ERM is defined as “the process of systematically and comprehensively identifying critical risks, quantifying their impacts, and implementing integrated risk management strategies to maximize enterprise value” (Darlington et al., 2001). The key factors are that ERM is a process that needs to be continually monitored and adapted to the changing corporate environment; it is not a one time activity. ERM is a comprehensive system of risk assessment, and it evaluates risk for the firm as a whole. This requires an understanding of the exposures of varying divisions. Finally, management must quantify the impact of risk on the firm. By understanding, mitigating, and managing risk, management is able to ascertain the capital needed to fund and grow the organization.
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Creating an ERM for Power Companies Competitive triage, deregulatory uncertainty, creation of new risk management products – the electric utility industry has coped with major changes such as these issues since its inception. However, the 21st Century has made issues such as these even more salient. For example, the recent industry turmoil from the fallout of the Enron scandal and the transmission-related blackout for a portion of the Eastern United States have been among the events adding to the challenges. The electric power industry has been called to evolve from a monopolistic “cost plus” business model to a competitive marketplace, able to adapt to changing regulatory and legislative agendas. The industry grew as new competitors with alternative sources of power entered the market. Firms split along functional lines, leaving companies that specialize in generation, transmission or distribution of power, sometimes leading to instability in the basic infrastructure of power. As a result of the Enron scandal, energy firms must now consider the governance issues facing their firms in these turbulent times. What accounting and corporate oversight is needed to reassure analysts as to the stability of this industry? What can we learn about risk management from the banking industry? And what can we do to reduce the risk associated with producing a non-storable asset with demand contingent upon the uncertainty of weather. ERM models serve the dual function of providing transparency to the market and the Board and assisting management in assessing the true value and risk of the firm. The business risk facing the power industry dramatically changed following deregulation. What the power industry and regulators know and understand is how to manage a monopolistic industry with no competitors and a rate of return set by state regulators; they have been doing this for decades. But this is the past in the electric utility industry. The new power industry is one of competition and deregulation. While deregulation began decades ago, it continues today. This process has brought great change to the industry; however, it is not done. The Federal Energy Regulatory Committee continues to consider regulatory change while states gradually approve competition for power within their borders. At the same time, pending legislation has far-reaching ramifications for this industry. The risks and competitive environment in power are evolving. Competitive pressures mount in this industry. Record cold in the late 1990s sent power prices skyrocketing and lead to the announcement of many additional power plants that were to be built throughout the U.S. to increase the supply of power and reduce the likelihood of exorbitant power prices in the future. However, a recession and several mild winters left many companies canceling plans to build these plants. What is the status of new power generation in the U.S.? And what international competitive pressures loom for U.S. firms? Questions such as these deserve careful consideration by power executives. The pressure mounts for management and Boards of Directors of energy companies to provide active oversight as to managerial actions. However, the complexity of the industry has made this task increasingly difficult. The use of derivative instruments can be a hedge or risk reduction strategy; however, they can also be used to speculate and increase the riskiness of the firm. And recent spectacular corporate failures due to the misuse of derivatives such as Barings Bank and Orange County tell us that a Board must adequately
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understand and monitor managerial activity. An ERM system must be able to aid the Board in its oversight responsibilities. Unlike other commodities, power is not storable. This trait makes the market for power infinitely more volatile than the market for other commodities. Concurrently, demand for power is highly dependent upon weather, an unpredictable variable. Additionally, the price for power is dependent upon the available supply. As firms cancel plans to build new generating facilities, the future stability of the industry is threatened. Firms must work to understand weather patterns and the latest weather forecasting techniques; must understand what derivative instruments can be used to reduce the firm’s exposure to extreme weather conditions; must work to manage the volatility and the seasonality of the industry; and should model to predict the future demand and supply for power in a given region. These are some of the challenges of developing an efficient ERM system in the power industry given the challenges of the business. The goal of the ERM program is to develop a methodology to adequately identify and model the risk of a project in order to determine what activities will add value to the firm. Efficiently allocating capital is critical to the future success of a power company. In addition to improving the firm’s ability to manage capital, a process for efficiently deploying capital will improve return on equity while ensuring solvency to a standard demanded by debtholders. An effective ERM process must be supported by business tools. ERM will help managers understand risk exposure and the diversifying effects of the business units, improve the firm’s competitive position, increase the company’s access to capital, determine the firm’s optimal capital allocation given a level of risk, and enhance it’s image in the market as an innovative company. Fundamentally, ERM will aggregate risk on an enterprise-wide basis and produce a risk profile of the firm. This model will determine a current value distribution and will allow analysis, prior to spending capital, of the impact potential new capital projects will have on firm value and debt rating. In order to develop this model, a bottom up approach is taken. A 1-year time horizon for analyzing risk is often chosen since it is consistent with financial market reporting models. The key challenge in developing an ERM system is understanding the risks of the entire organization. Managers representing every business unit must be interviewed to determine how each group manages risk, values market opportunities and determines project performance. Furthermore, since the ERM model must be forward looking, an effort must be made to understand the business strategy that drives each group’s approach to the market. Based on the results of these interviews, risk drivers for each group and correlations between risk drivers need to be identified, and an approach to modeling each group’s value distribution then is developed. In the power industry, market risk is heavily driven by two commodities: gas (an input to much of the power produced) and electricity. The general approach underlying ERM is to value an asset portfolio using a set of projections representing the price of each commodity affecting the business. The correlations between various groups’ exposure to commodity risk needs to be measured, then each business unit’s value distribution will be modeled using relevant projections. These distributions are then aggregated to come up with a single enterprise-wide value distribution.
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ERM also is a tool for decision making when evaluating capital projects. To date, most investments are evaluated on a stand-alone basis, with traditional NPV or Internal Rate of Return (IRR) approaches. In these cases, the choice of hurdle rates has been essential to deciding on whether or not to proceed with a project. No formalized consideration has been given to a project’s effect on the firm’s overall risk profile. ERM allows managers to determine a project’s marginal contribution to value, taking into consideration the effects of diversification with existing projects. ERM is a vital tool to help managers determine a firm’s risk profile and to analyze the impact potential capital projects will have on the company’s overall risk. This tool helps managers deploy capital more efficiently throughout the company and will provide executive leadership with the ability to steer the company towards the return on equity and solvency standards demanded by the market. This ability to link project by project decision making with a firm’s overall strategic vision will improve access to capital and increase the value the firm brings to its shareholders. Known Versus Unknown “It’s not what we don’t know that causes trouble. It’s what we know that ain’t so.” –Will Rogers In quantifying risk, one of the difficulties is in identifying risk. From our earliest years in school, we learn by reading textbooks. The assumption is that these books, with their vast resources and authoritative and confident tone, represent all there is to know about a given subject; we grow up thinking that more is known than actually is (Gomory, 1995). By learning and questioning, we stretch our understanding beyond the original text. Unanswered questions lead us to identify what is known and what is unknown. The unknown represents risk. We need to differentiate between the unknown and the unknowable. Quite often, what is unknown today becomes known in the future. Scientific discovery expands the bounds of our thinking. But there is a limit. Some phenomena are unknowable. Take for example, the weather. In determining when to shut down a power plant for routine maintenance, a power company looks at historic weather trends and chooses a month to shut down the plant when temperatures are typically mild and demand for power is typically low; for example, the month of October. The power company cannot know with certainty that the current year’s weather pattern will follow historic weather patterns. This could be the year with an unseasonably hot October and demand for power to run air conditioners exceeds expectations. The actual weather during the next October is unknowable and no amount of modeling will change that. Additionally, the perspective of the provider of knowledge influences what we know. A power plant operator in West Virginia may believe he knows everything there is to know about running a power plant. However, if his knowledge base comes strictly from operating his coal-fired generation facility, that knowledge may be useless to a hydro-powered plant operator in Oregon. Our perspective also influences our understanding of risk. Some individuals are, by nature, more comfortable assuming risk. Acknowledging and considering alternative attitudes towards risk becomes an important component in risk management.
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One method of accounting for differing knowledge, alternative outcomes, and varying attitudes towards risk is with scenario analysis.
Scenario Analysis Not all risks are known; nor are all risks quantifiable. Yet the goal of an ERM system is to capture all risk and factor it into the decision-making process for the firm. The system must be flexible to adapt as new developments become relevant to the decision-making process. Sensitivity and scenario analyses are means of encouraging management to look at an array of possible outcomes and their impact upon the firm’s earnings. Sensitivity analysis and scenario analysis have been used, and are used, to supplement a traditional DCF analysis. Sensitivity, or “what if,” analysis is generally performed on the determinants of the cash flows. It is reasonably easy to perform, helps identify the principle threats to the project, and calculates the consequences of incorrectly estimating a variable (i.e., the effect on NPV). However, sensitivity analysis only evaluates one variable at a time. Scenario analysis does consider internally consistent combinations of variables. It leads into challenge thinking. The goal is for teams to challenge traditional beliefs and push at the boundaries of knowledge which may lead to innovative ideas and approaches. Scenarios allow management to factor uncertainty into their decision-making process. Management looks at all possible outcomes without assigning probabilities for the likelihood of an outcome occurring. In considering what could happen, management may uncover previously unheard of opportunities as well as identifying additional risk factors in the project. “The one enduring competitive advantage of an organization is it’s ability to learn better and faster than its competition.” – Arie de Geus Scenario analysis and contingency planning allow organizations to adapt faster than the competition. Teams look at all possible developments in a project. This allows flexibility in divisional strategy to react to events that management has now considered. This process makes management more flexible; they spend time thinking beyond the obvious, predictable and desired outcomes. Scenario analysis forces management to think creatively; contingency planning forces management to plan creatively. Scenarios are used to develop an action plan. Scenario analysis should lead to the conclusion that the original strategy is sound or that there are warning signals that need to be attended to. It forces an understanding of the causal relationship between seemingly unrelated factors. The goal of ERM is to understand risk, align the firm’s strategy with corporate objectives, and minimize the probability of unexpected outcomes. Risk management is, and should be, a strategic priority. Scenario analysis and contingency planning work together to minimize the unknown which leads to the ability of a firm to account for and manage risk.
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References Basel Committee on Banking Supervision at the Bank for International Settlements, “The New Basel Capital Accord,” CH-4002, Basel, Switzerland, 2001. Darlington, Angela, Simon Grout and John Whitworth, “How Safe is Safe Enough? An Introduction to Risk Management”, The Staple Inn Actuarial Society, June 12, 2001. Dixit, Avinash and Robert Pindyck, “Investment Under Uncertainty: Keeping One’s Options Open,” Journal of Economic Literature, Nashville, December, 1994. Gomory, Ralph E., “An Essay on the Known, the Unknown, and the Unknowable”, Scientific American, June, 1995. Lam, James, “Enterprise Risk Management and the Role of the Chief Risk Officer,” ERisk, March 25, 2000. Pinches, George, “Myopia, Capital Budgeting and Decision-Making”, Financial Management, 11(3), 1982, 6–20. Pindyck, Robert, “Irreversibility, Uncertainty, and Investment”, Journal of Economic Literature, Nashville, September, 1991. Porter, Michael, “Capital Disadvantage: America’s Failing Capital Investment System”, Harvard Business Review, Boston, September/October 1992.