Scenario analysis is a practice that has existed as long as humans have walked this Earth. A mariner contemplates the possibility that his ship may be afflicted by storms—or pirates, or scurvy. He thinks about how he would respond to each, and what the consequences might be. He toys with different scenarios and different responses. He considers the likelihood of each scenario. Based on that, and what he perceives would be the outcome of each, he plans his next voyage. This sort of "what if" analysis has always been a part of business decision making. Today, what we call scenario analysis is a formalization of the process. Formalized scenario analysis is used in asset-liability management and corporate risk management. It originated in the 1970s and 1980s among banks and insurance companies when volatility in interest rates emerged as a threat to their balance sheets. Its primary use remains the analysis of interest rate risk, but corporations apply it for a variety of risks. Scenario analysis starts with scenarios. A simple analysis might consider three scenarios, say reflecting assumptions that the economy will experience
Typically, more scenarios are used to permit a variety of eventualities to be assessed. A scenario is specified as a set of "paths" that will be taken by relevant risk factors. Risk factors are typically interest rates, but they can also be exchange rates, equity prices, commodity prices, implied volatilities—pretty much any risk factors. Scenarios all have the same horizon and time step—for example, they might specify values of interest rates at six-month intervals over the next ten years. Exhibit 1 illustrates one such scenario.
Such detailed scenarios can accommodate events such as narrowing spreads or flattening of the yield curve. Often, however, scenarios are more simplistic. Five simple scenarios might have interest rates
Exhibit 2 illustrates one such scenario.
Once scenarios have been specified, the next step is to project what will happen under each one. If a bank is performing the analysis, this might entail projecting what effect evolving interest rates will have on the level of demand deposits, commercial loans and residential mortgage loans. Performed at each time step, this will generate paths for the evolution of each. The analysis might project for one scenario, say, declining demand deposits over time, rising and then declining commercial loans, etc. Management actions may also be projected under each scenario. Under one scenario, they might be projected to increase borrowing in the Eurodollar market and later reallocating resources from one business line to another. An elaborate analysis might project, under each scenario, a bank's cash-flow statement and balance sheet at each time step. Output can take many forms. It may focus on economic value, but it is more common to focus on earnings, cash flow or other accounting results. Exhibit 3 illustrates typical output, detailing the evolution of net interest income over time for each of several scenarios.
Scenario analysis is an important tool of asset-liability management. Unlike other tools, such as gap analysis or duration, it is very flexible. Gap analysis cannot address options risk or anticipate the evolution of future business or management decisions. Scenario analysis can. Duration can, if implemented correctly, address options risk, but it only addresses interest rate risk due to parallel shifts in the spot curve. Scenario analysis, by comparison, can consider all sorts of movements in the spot curve—flattening, steepening, becoming inverted, etc. Gap analysis and duration cannot address basis risk. Scenario analysis can.
Scenario analysis has several shortcomings. It only addresses risk due to the specific scenarios considered. Furthermore, there is always a risk that scenarios don't consider a long enough horizon. US thrifts learned this lesson the hard way in the 1980s, when they performed short-term scenario analysis on the mortgage-backed securities (MBSs) they were over-paying for. The analyses captured the attractive short-term earnings from the MBSs, but the horizon didn't extend long enough to predict the crushing effect of the subsequent prepayments. Many of the thrifts failed. Scenario analysis is highly dependent on assumptions. Assumptions must be made about, say, the impact of a flattening term structure on a bank's business lines or the response of management to a decline in demand deposits. Output of the analysis is only as good as these assumptions. The more elaborate the scenario analysis, the more assumptions that must be made. Finally, output of scenario analysis tends to be cumbersome—multiple tables summarizing results as opposed to, say, the single number that is duration. In its early days, scenario analysis was largely a manual process—performed with pencil and paper, and maybe an electronic calculator. This limited the number of scenarios that could be considered. Gathering balance sheet information and other inputs from various departments was a time consuming task. Only simple analyses were performed. Advances in computer technology have changed this. Networked computers and centralized databases makes it easy to gather inputs. Large numbers of scenarios can be considered, and analyses of those scenarios can be elaborate. Sometimes, scenario analysis is performed as a Monte Carlo simulation. A large number of scenarios is randomly generated, and results are calculated under each based upon formulaic assumptions regarding how business will evolve in given circumstances. Output, rather than being presented on a scenario-by-scenario basis, is presented in terms of frequency distributions. Treating these as probability distributions, summary statistics—such as earnings-at-risk—are calculated. Often, you will hear the terms "scenario analysis" and "simulation analysis" used interchangeably. If there is a distinction between them, it is that some practitioners reserve simulation analysis to refer to the above form of Monte Carlo analysis. That is how I use the term in this glossary.
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