firm that it would want to guard against. As a result, it might establish a threshold that it will engage in products where there is a 97.5 percent chance that returns would not fall below 5 percent. Notice that since half of the outcomes fall above a 15 percent return and that 47.5 percent of the outcomes fall between 5 and 15 percent (one half of the 95 percent frequency assuming +/–2 standard deviations from the mean), then the portion of the area under the distribution accounting for returns worse than 5 percent would be 2.5 percent.
Such use of statistics provides risk managers with easy-to-apply metrics of how much risk may exist and how much risk should be tolerated based on other considerations such as the likelihood of insolvency. But blind use of statistics can at times jeopardize the company should actual results begin to vary significantly from historical performance. In such cases formal measures of risk as based on statistical models must be validated regularly and augmented when needed by experience and seasoned judgment. Such considerations bring to mind the need to characterize risk management in situational terms for the existence of uncertainty in any risk management problem implies that circumstances specific to each problem can and will affect outcomes that might not be precisely measured using rigorous analytical methodologies based on historical information.
Situational Risk Management
As the phrase implies, situational risk management is a way of assessing risk that takes into account the specific set of circumstances in place at the time of the assessment. It could include the market and economic conditions prevailing at the time, the set of clients or customers of a set of products posing risk, their behavior, business processes, accounting practices, and regulatory and political conditions, among other factors to take into consideration. And complicating the problem a bit more is the need to take these factors into account in projecting potential future outcomes. All of this may seem daunting to the risk manager who is facing how to assess risk based on the unique situation of the particular problem.
If we could teleport back to 2004 into a major mortgage originator’s risk management department, it might provide some insights into the nature of situational risk management. Consider the heads of risk management of two large mortgage originators facing whether to expand their mortgage production activities. Both firms face extraordinary pressures on their businesses due to commoditization of prime mortgages that are typically sold to the government-sponsored enterprises Fannie Mae and Freddie Mac. As a result, prices for these loans have squeezed profit margins to a point that other sources of revenue are required for the long-term sustainability of the franchise. As a result, one of the companies, X Bank (a mortgage-specializing thrift) decides that it needs to compete with other major players in loans that feature riskier combinations than they have traditionally originated. X Bank has over time acquired other smaller thrifts and banks focused on mortgage lending and this has led to a number of deficiencies and gaps in the way mortgage loans are underwritten. Fortunately, the economic environment has been extremely favorable, with low interest rates and high home price appreciation contributing to low default rates. These conditions thus have masked any problems that might cause X Bank higher losses for the time being. The other bank, Z Bank faces the same conditions; however, it is more diversified as a commercial bank and in growing organically over time has put in place strong processes and controls for all facets of the underwriting and servicing segments of the mortgage business. Further distinguishing the two firms is their differing reliance on analytic methods and data. X Bank has employed for several years relatively sophisticated data mining and simulation-based techniques to assess risk. Meanwhile Z Bank has just begun to develop risk data warehouses and building modeling capabilities to assess mortgage credit risk. It normally used simple measures of default risk that do not take into consideration possible changes in market conditions that could affect future credit risk outcomes. In its place, Z Bank has come to rely on the expertise of former underwriters put into their Quality Control department. Their job principally has been to perform postorigination reviews of originated mortgages and determine whether there have been any defects in the underwriting process that could pose risk to the firm.
In deciding whether to take on additional credit risk, X Bank relies on what it believes to be its comparative advantage: risk analytics. With losses on riskier segments of their business extraordinarily low, X Bank is satisfied that its estimates of credit risk are stable and reflect the underlying conditions in the market. Given this view, X Bank elects not to build up much of a quality control unit or to integrate their findings into credit-risk discussions. Z Bank, on the other hand, recognizes its limitations in its analytic capabilities and that even if it had such an infrastructure, it would be of limited value since the current environment is completely unlike any seen in recent memory. Consequently, they believe that using analytics exclusively to assess the amount of credit risk in their portfolio would need to be augmented by other factors including input from seasoned underwriters who have experience originating riskier mortgages.
The decision framework that both firms use to determine the amount of product risk each is willing to take on is dependent upon the common and unique set of circumstances (the situation) each bank confronts. X Bank believes it has better information and analytics by which to expand its business and be more competitive against other firms like Z Bank. At the same time, the QC department of Z Bank has concluded that the risks involved in expanding the product underwriting criteria are not sufficiently well understood to warrant taking on what appears to be higher risk. Z Bank management concurs with this conclusion despite the toll on market share this decision will cause, based on an understanding of the limitations of their data and analytics to accurately assess the amount of credit risk that could potentially accumulate should market conditions appreciably change.
By late 2007, the results from X and Z Banks’ decisions are clear. In the years following the original decision, the economy stalled, leading to one of the worst housing markets since the Great Depression. With home prices depreciating at double-digit rates and unemployment rising to 10 percent, credit losses on the riskier mortgages grew to levels that were multiples above what X Bank had estimated them to be in 2004. With their loan-loss reserves well understated for this risk and their capital levels weakening, X Bank experiences a run on its deposits that eventually leads to the closure of the bank by its regulator. In the years leading up to this event, X Bank had become the dominant mortgage originator, but did so at the expense of good risk management practices. Meanwhile, Z Bank largely avoided the mortgage credit meltdown by staying the course with its existing product set. That strategy wound up costing the firm several points of market share, but in the aftermath of the crisis the bank managed to pick up a major mortgage originator and through that combination regained a top-three position in the market while effectively managing its risk exposure.
A lesson from this example is that risk management decisions are highly dependent on the unique situation of the firm and it is essential that risk managers have their pulse on the factors that drive risk-taking. Dissecting the hypothetical case, X Bank risk managers relied too heavily on analytics at the expense of seasoned judgment, which in a period of unusually good credit performance should have signaled a greater emphasis on understanding the processes and controls underlying the underwriting activity. The situation in this case for X bank featured an accommodating economic environment, strong analytic capabilities based on historical information, aggressive management orientation toward market share at the expense of prudent risk-taking, and a limited appreciation for underwriting experience. Z Bank, facing the same economic conditions, came to a different conclusion and set of outcomes as a result. But in several important respects its situation was much different. It recognized its limitations in data and analytics and acknowledged its prowess in understanding the underwriting process and controls required to originate mortgages that could withstand different market conditions. Futhermore it had a management team that embraced its risk manager’s recommendations – not an insignificant factor that led to Z Bank’s making the right risk decision in the end.
Situational risk management thus is a case-by-case assessment of the factors influencing risk decisions. Figure 2.2 provides a framework for conceptualizing situational risk management. The primary activities of the risk manager of identifying, measuring,
