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Dynamic Financial Analysis. DFA, ERM and DRM Software Table of ContentsDynamic Financial Analysis (DFA) and DFA Software Tools
Dynamic Financial Analysis and its Software ToolsDynamic Financial Analysis (DFA), Enterprise Risk Management (ERM) and Dynamic Risk Modeling(DRM) refer to a structured and disciplined approach to managing risk by applying sophisticated mathematical modeling, which simulates various scenarios and selects best strategies leading to optimum outcomes in any given scenario. Arguably, the terminology and methodology comes from the discipline of Dynamic Financial Analysis, which historically was defined, refined and worked into a science and an art form by actuaries in insurance industry. DFA is also an applied study of the impact of different risks associated with the operation of a company and of business strategies contingent upon those risks and the company's long-term vision. Today, DFA methodology is applied to companies in insurance, banking, manufacturing, or any kind of business exposed to risks. Moreover, DFA methodology is expanded to general studies of complex systems, where researchers deal with interrelated input / output parameters and feedback loops, while using stochastic and probabilistic approaches to simulation modeling of the future states of the systems. While DFA, ERM and DRM were defined specifically in actuarial science as applied in insurance industry, in a broad sense they are subsets of dynamic modeling, which deals with interrelations between the input and output parameters of a complex system. Hence, Dynamic Financial Analysis software first evolved in Insurance, but is applied today to a broad spectrum of problems solved through modeling in general finance, banking and other industries. Rather than discussing complex systems and dynamic modeling, which would invariably have to be abstract and mathematical in nature, we will focus instead on a common-sense discussion of commercial and industrial applications of DFA, ERM and DRM, where these terms are often used interchangeably. Interrelations Between Risks and StrategiesMost companies face risks on the asset side of their balance sheet. These risks include market risk - risk of adverse changes in asset prices, interest rate risk - risk of changes in asset values due to changes in real interest rates, credit risk - risk of non-payment from an obligor, foreign currency risk - risk of changes in value for which assets can be exchanged in different currencies, reinvestment risk - risk that any interest payments, coupon payments, or dividends will become ineligible to be reinvested at the initial investment rate of return, liquidity risk - risk that an asset may not be sold at the current market price due to its large size, market illiquidity or other such factors, and inflation risk - risk of changes in asset values due to unexpected changes in the inflation rate. [More about risks...] In addition, insurance companies face risks on the liability side of their balance sheet. These risks include underwriting risk which is associated with the frequency and severity of insured losses, frequency of catastrophic events, fluctuations in expenses, price elasticity, etc. These risks also include reserving risk - risk that the funds reserved by the company to pay for future claims on the policies written in the past become insufficient as those claims develop. [More about risks...] A strategy can be defined as a series of management decisions that are made with the goal of achieving certain objectives, which the senior management deems desirable. DFA modeling recognizes that the identified strategies are not independent, but rather complexly interrelated, and that risks are also correlated not only to strategies but are intertwined with other risks. Helping Management Make Strategic DecisionsDFA helps senior management facing such risks in making strategic decisions. The powerful DFA software products developed by Ultimate Risk Solutions - Risk Explorer™, URS Translator™ for Excel and Model Builder- which were first deployed in insurance and finance industries, may be also utilized by modelers and researchers in many other areas of human endeavor, not limited to DFA, DRM and ERM. Hence, this brief discussion of dynamic analysis and simulation modeling should be construed broadly, just as dynamic modeling and stochastic simulations used in DFA may benefit managers and researchers of a variety of complex systems in various fields, not just insurance, where the DFA methods and DFA software were rigorously defined, developed, refined and honed. Rather than looking at a static picture of an outcome based on an isolated risk, DFA looks at how shifts and fluctuations in external risks, such as interest and inflation rates affect the major risks your organization faces and how those risks interact and correlate with one another. Moreover, it looks at how those risks affect the choice of alternative strategies. The results are measured in financial terms and metrics relevant to CFOs, CEOs, industry and government regulators. DRM simulates a wide span of corporate activities and uses a variety of metrics, such as earnings, return on investment, liability exposure, operational risk, or varying regulatory requirements in order to develop a comprehensive risk profile of a single company. Contingencies for "Improbable" RisksOne of the examples of using DFA is for regulatory reporting. Many financial institutions today must demonstrate adequacy of capital and cash reserves vis-à-vis the "worst case" business scenarios, which - while being so unlikely as to be often deemed "unthinkable" - may and do come along with certain non-zero probability, as students of finance know from the now text-book cases of spectacular financial disasters, such as the collapse of Barings Bank, Orange County, California bankruptcy and demise of LTCM, the recent financial history's major fiascos. The Collapse of Barings Bank:In the recent history of spectacular financial disasters the collapse of Britain 's Barings bank in February 1995 is perhaps the quintessential tale of financial risk and information management gone wrong in the environment of virtually no management's oversight and little control. Barings' collapse on February 26, 1995 was brought by the activities of just one rogue trader, Nick Leeson, who lost $1.4 billion by speculating on the Singapore International Monetary Exchange using futures contracts. The failure came to the bank's management as a surprise and was completely unexpected. A single trader acting alone caused the unthinkable - the demise of the Baring financial empire, founded in 1762 and famed for its role in financing the Louisiana Purchase by the United States from France in 1802. By falsifying bank records in 1994, Mr. Leeson booked a staggering profit from futures arbitrage, which ensured that Barings employees earned big bonuses that year and had little incentive to question his unusually high "profits." If anything, till the very collapse of the bank, Leeson was viewed as a celebrity, a star trader who was not to be interfered with. Orange County, California Bankruptcy:When Orange County declared Chapter 9 bankruptcy on December 6, 1994, Robert Citron was its longtime Treasurer-Tax Collector. Borrowing $2 for every $1 on deposit, then using derivatives, Robert Citron, a haughty and unapproachable county treasurer who decided to dabble in high finance, increased the size of the investment pool to $20.6 billion. To get that much money, he went out to the banks repository receipts market and leveraged the County Pools to amounts ranging from 158% to over 292% using U.S. Treasury bonds as collateral. If the interest rates continued to drop (as Robert Citron was betting) his leveraged strategy would be highly profitable. Unfortunately for Orange County , also in 1994, the Federal Reserve Board began and kept on raising interest rates, while Bob Citron kept on buying more of the exotic derivatives, betting that the interest rates would just have to decline. As no one was paying attention to what the glorified public treasurer was doing, the value of the Treasury bonds in the fund's possession declined in the rising interest rates environment. The increasing margin payments were funded in part by a 600 million bond issue made by Orange County . However, this fix proved to be only temporary. In December 1994, Credit Suisse First Boston (CSFB) realized what was going on, and to prevent the problem from snowballing any further blocked the "rolling over" of $1.25 billion in repos, as each "roll over" had to be done at the new interest rate. Orange County could not meet its debt repayment obligations and went bankrupt, having to lay off 3000 public employees and severely cut services. Mr. Citron claimed being financially naïve, through admitted improper funds transfers, served 1000 hours of community service, but never spent a day in jail. The Long Term Capital Management Debacle:Long Term Capital Management (LTCM) was another spectacular disaster, which happened, for a change, to a team of financial wizards. LTCM was a hedge fund started in 1993 by John Meriweather, a legendary bond trader from Salomon Brothers. Two prominent LTCM partners on his team were the famed economists Myron Scholes and Robert Merton, originators of the Black-Scholes options pricing formula (awarded in 1997 the Nobel Memorial Prize in Economics for "a new method to determine the value of derivatives.") The LTCM's team of two Nobel Prize laureates and a star trader bedazzled the financial world with returns nothing short of spectacular, while accompanied by low risk. One of their first trades was betting the farm on a $2 billion trade in the bond market without a dime of their own capital. In its first year of business, with the back wind of the 1994 market volatility, LTCM netted 28% for its investors. That was an incredible feat considering that most investors in financial markets lost money that year. In its second year of operation, LTCM made returns of whopping 59%. At the end of 1995, LTCM was leveraged 28 to 1 (!), and by the spring of 1996, LTCM had $140 billion in assets or thirty times its capital. They went on to earn a profit of $2.1 billion or 41%. This tiny team of star performers made more money than most of the major multinational corporations in 1996. Not even Lucent, McDonalds, Disney, Sears or Nike made as much as LTCM. By the end of the year, the firm had close to $4 billion in equity, still a tiny proportion of nearly $140 billion in debt, and a derivative book of $1.25 trillion. Professors Scholes and Merton were confident of the key assumptions of their model and were counting on the probability of risk to be contained in a normal bell curve of the probability distribution. What they did not count on was the tail of the curve, the remote area a couple of standard deviations away from the "norm", the very tail of the probability curve where the very unlikely events occur. The partners made even bigger bets confident that their "reasonable" expectations of volatility would insulate them from risk. Unfortunately for LTCM, those "unlikely" events came in 1998 in a sudden, fast progression as the markets were shaken by a series of negatives events in Russia , Asia, and China and the revelations by the White House intern Monica Lewinsky about President Clinton's extracurricular interests in things other than affairs of the state. Markets got very nervous, very fast, creating conditions of a "perfect storm," which soon sank LTCM. The company started out the year with $3.6 billion in capital. By September of 1998, LTCM had mounting losses progressing at the speed of half a billion dollars, daily. The hemorrhaging of money from LTCM was so fast and furious - magnified by its huge leverage - that it took an urgent interference by the Federal Reserve Bank, which orchestrated a bailout of LTCM by 14 major banks and investment firms to avoid a potential financial disaster of a world-wide magnitude. The demise of LTCM was abrupt and unexpected: the fund, after all, boasted not one but two Nobel Prize laureates, the men credited with developing the theory for pricing options, the theoretical foundation on which the massive derivatives markets have been established. Bankers viewed Mr. Meriwether & cohorts Scholes and Merton as the Masters of the Universe, the emperors of modern finance. The emperors, it turned out, had no clothes when it came to handling the "unlikely" risks. Given the history of financial disasters, today's regulators and legislators in many countries and the United States require the management to demonstrate that it has considered the risks to its company and is able to manage those risks and meet the company's statutory obligations even in a worst case scenario. Managing ChangeDFA, DRM and ERM are much more than just software modeling tools to keep regulators satisfied, and lawyers at bay. They are primarily used in making key strategic decisions, such as:
DRM aligns the organization's strategies, processes, technology and knowledge with the purpose of improving its ability to evaluate and manage, enterprise-wide, the uncertainties the Enterprise faces as it creates value. ERM is an integrated, forward-looking and process-orientated approach to managing key business risks and opportunities - not only financial risks - with the intent of maximizing value for the enterprise and its shareholders. Invariably, ERM, DFA and DRM involve mathematically and technologically sophisticated modeling, which attempts to "read" into the future of the Enterprise as influenced by a range of socio-economic and market scenarios and events, and is designed to answer certain "what if" questions, including but not limited to certain "worst case scenarios," the probability of which may be low but still in existence. Key to organization's success is the ability to adjust quickly to the rapidly changing socio-economic environments, including events and circumstances that are "unlikely". When such events do occur, the management - armed with decision support tools such as models developed with the use of Risk Explorer™ , is well-prepared to adjust quickly, choosing the optimum strategies in any given set of circumstances. Moreover, Risk Explorer™, Model Builder and URS Translator™ for Excel software allow companies to deploy new models rapidly, thus enabling companies to be agile, easily adjusting to the unforeseen and changing circumstances. Changes in how companies conduct business are not only inevitable but also necessary. However, change for the wrong reason can be destructive. Companies can spend years going down a strategic path only to find out that a strategy invariably falls short of achieving the intended objectives or its stated strategic objectives ended up being inconsistent with the company's long term vision. Companies need to heed the warning, "be careful what you wish for". A "vision" is a concise statement from senior management which defines an intended future state of the company. Several strategies can be used to achieve the vision. A strategy is a major management initiative that helps achieve the overall vision. It can be demoralizing for a workforce to achieve a strategy only to realize that the goals they reached were inconsistent with the company's long-term vision in the first place. Strategies that are consistent with the long-term vision of the Enterprise help its executive management, divisions and employees stay focused on common objectives and align the work of various divisions with the company's main goals. Multiple strategies should come together to support one another, and not work against one another. Finally, executive management needs to select those strategies that address the risks, which have a high probability of success in achieving the desired objectives. This is where a Dynamic Financial Analysis approach helps management in strategic planning. DFA helps companies select strategies that are consistent with their corporate vision and have high probabilities of being implemented successfully. Laymen and even some less-informed practitioners of finance may regard DRM as "some kind of mathematical model" which "probably doesn't work anyway," and was a "pipe dream" of a bunch of "rocket scientists." While all models known to mankind have certain limitations, DFA methodology has proven itself to be a necessary and reliable tool in today's business management. DFA and dynamic risk modeling attracted some of the best names in risk management who worked alongside with the best mathematicians and programmers to create decision-support tools which deliver excellent results, time proven and tested. Risks, Optimum Strategies, and DependenciesAn enterprise-wide risk management practice increases the risk sensitivity of the organization, making it more lithe, agile and capable to successfully respond to the changing world, and reduces the inevitable functional, departmental and cultural barriers that exist in most organizations, thus aligning the functioning of each division of the organization with the strategic goals of the enterprise and its management's vision. The Risk Explorer™, Model Builder and URS Translator™ for Excel allow companies to be highly agile in rapid model creation and deployment, empowering businesses to respond quickly and appropriately to the changing business conditions. By using DFA / ERM methodology in identifying and proactively treating potential risks, the management protects the very existence, the resources, the products and services, and the customers of the Enterprise, as well as identifies and responsibly manages the influence of the Enterprise on Society at large, the influence of the Enterprise on the Financial and labor Markets and on the Environment. In a narrow sense of the term, enterprise risk management (ERM) is similar to operational risk management (ORM) but also includes credit risk and market risk. It is important to recognize all sources of interactions between the risks and strategies and build them into a DRM model. For example, a conservative pricing strategy would negate the growth pricing strategy. An increase in interest rate would require an increase in loss reserves , but would lead to higher investment returns on short-term treasuries. With a high investment return, the company can lower the price without compromising on profit. An overly conservative approach of setting loss reserves would generate the need for pricing increase and would impact the overall capital. DFA methodology is applicable in many disciplines, and mathematical models have become important tools in the study of behavior of various systems, of which an industrial enterprise system, a P/C insurance company, or a service company is but a subset. The complex systems, which may be described by dynamic modeling are diverse, almost unlimited, ranging from the biology of the human body to weather forecasting, to predicting the timing and locations of Earthquakes, to predicting the fluctuations in fish and wild-life populations, to insurance, to military campaigns, to asset-class and individual asset behaviors. For any of these applications, the type of mathematical model employed will depend upon the nature of the system, how broadly the system is defined, and what needs to be learned about the system's behavior. Considerations for building a model may include:
These considerations, and the extent to which a model must emulate all facets of the real system, will determine how simple or sophisticated a model must be. The extent to which certain features of the model are important will determine whether the model is stochastic, primarily stochastic or deterministic; whether it is simple or includes feedback loops, etc. However, different models may include any or all of these features to different degrees. Whereas until recently, actuaries and financial analysts had to rely on teams of programmers engaged in lengthy and labor- and time-consuming program development to realize their DFA vision, today, with software tools like Risk Explorer™ , URS Translator™ for Excel and Model Builder , creating, testing and deploying models into production takes much less time and effort. The DFA software products developed by Ultimate Risk Solutions allow finance professionals to focus on the substance of the model, rather than the process of creating programs. Defining the ProblemA popular cliché is that a well-formulated question is part of the solution. One way of thinking of the steps in building a Dynamic Risk Model is that you must first define the problem: the scenarios (inputs) and the strategic options (outputs) to be evaluated. "Scenario" defines which items are included in the simulation model, the nature of those items and the rules for interaction between the elements in the model. In other words, scenario defines the environment in which alternative strategies are to be evaluated. A "return" metric is one that seeks to maximize something of positive value or minimize something of negative value to the organization. Common examples of return metrics might include operating income, EBITDA, or surplus growth. A "risk" metric is a measurement of the volatility associated with each strategy. A traditional statistical risk metric is the standard deviation or the variance of the values of the return metric, as observed around the mean value. Another risk metric might be the number of times (or probability) that an observation might fall below a minimally acceptable threshold value, or get above a maximally acceptable high value. Thus, for example, a model simulating financial performance of Calpine Corporation run around year 2001 showed the demise of this innovative but highly leveraged company long before it actually happened: in an "unlikely" scenario of a sharply rising gas prices, as they did, more than 3-fold, by the year 2005, Calpine could not have possibly serviced its debt. As the environmentally clean energy source, natural gas, was used in its 92 power plants producing electrical energy in 21 states and Canada was at the time "unthinkable" to be at the 2005 price levels, Calpine's management forged ahead with a highly leveraged, aggressive strategy of building more plants. But in December 2005 Calpine Corporation was in bankruptcy and its management team, which enjoyed a celebrity status circa 2001, was fired. The scenario and the strategies to be evaluated are interrelated. Those items, which are under the management's control, are the items that may be changed as part of certain strategies the management team is evaluating. Hence, the Dynamic Financial Analysis of a problem, for example, a cost-benefit analysis of alternative investments, may be done in a series of steps, which begin with the problem formulation:
There are many examples, most recent and going far back, of successful application of DRM in many fields, with models ranging from rather simple to very complex. DFA, a Historic PerspectiveThe roots of DFA may be ascribed to the post World War II military modeling or "scenario planning" studies developed by the Rand Corporation. A good example of such scenario planning would be its use by Royal Dutch/Shell, one of the prominent users of this methodology. In the early 1970's, Shell began experimenting with scenario planning to identify potential changes in "business as usual" in the oil industry, trying to identify not only the pricing risks, but also optimum responses to them. In one scenario developed by Shell in the early 1980's, they envisioned the conditions in which oil prices would drop precipitously as the result of discoveries and exploitation of new oil fields outside of the regions controlled by the OPEC oil cartel, compounded by determination of oil-consuming nations to become less dependent upon imported oil. Using this scenario as a guide, Shell was able to position itself to rise from the fourteenth to the second place among the oil multinationals during the mid-1980s. Indeed, as prices fell, other companies, which were heavily over-invested, sustained heavy losses. Shell also researched scenarios in which the price of natural gas might fall. It identified a single strongest potential market influence - a liberalization of the policy of the Soviet Union regarding more extensive use of their vast and virtually untapped gas reserves. Accordingly, Shell researchers identified a few politicians, obscure at that time, whose rise to power might lead to just such a change. One of the so identified politicians was Mikhail Gorbachev. When Gorbachev came to power, Shell took it as a harbinger of the eventual drop in natural gas prices, thus saving the company from potentially disastrous long-term investment decisions. The success of Shell over the past thirty years demonstrates the value of their long-term investment in DFA management tools. A company able to measure risks earlier than its competitors can make decisions with an awareness that its competitors do not yet have. A company armed with DFA methodology and tools can respond quickly and successfully to the rapidly changing business environment. Today, with powerful and user-friendly software tools from Ultimate Risk Solutions, such as Risk Explorer™, Model Builder and URS Translator™ for Excel , DFA modeling with rapid prototyping and fast deployment of results is much more available than ever before.
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