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Moving from stress tests to broader scenario analyses

Even in the best of economic times, stress testing a bank’s portfolio is a sound practice for assessing the sufficiency of capital reserves as well as the responsiveness of the institution’s infrastructure and reporting process. However, as we all learned in the 2008-09 financial crisis, there were significant weaknesses in these internal stress testing programs. The major shortcoming: an inability to assess vulnerabilities arising from interrelated events across all risk and asset types. The inadequacy prevented institutions from seeing the interactions across risk types that surfaced in the crisis. As a result, “siloed” risk models in various asset classes lacked the synergy and interrelationships to properly forecast the cross-asset impact of abnormal conditions and events.

Today, it’s much clearer that firm-wide stress testing across asset classes, positions and business lines must improve in order to ensure appropriate risk capture, and aggregate stress test and risk calculation results more consistently and effectively. However, until regulators recently stepped up their mandates for stress testing, there was no precedent or strong impetus driving banks to aggregate their information to assess vulnerabilities across risk and asset types. Although the recently completed first round of the Supervisory Capital Assessment Program (SCAP) provided some useful insights into 19 of the largest U.S. banks, those tests were limited by the available data and the restriction to two basic scenarios – a continuing downturn and a slightly more adverse condition.

What’s more, the SCAP tests were a process nightmare. The US Treasury Department designed valid and meaningful stress tests, sent regulatory letters mandating that banks perform the tests, compiled results, compared bank submissions, adjusted values and interpreted the findings. Collectively, those tasks took three to four months. It wasn’t better inside banks.  As a result of these Treasury-orchestrated stress tests, most banks had to create one-time manual processes to comply – processes that will be difficult to extend or repeat.

The Shift to Scenario Analysis
Simply replicating the tests on an industry-wide basis would be challenging enough. But as the thin and limited results of the first round of SCAP show, it’s clear that regulators and institutions alike will need to expand their view from simple stress testing. It’s now clear that the SCAP tests were not a one-time exercise, and that improved stress-testing processes and analytics will remain a high priority for for the foreseeable future. Mastery of these tests will not only be required to respond to regulatory requirements, they will also enable banks to perform ad hoc stress analyses and more effectively manage their economic capital.

But this first phase only hints at the scale and scope of the broader challenge. Going forward, banks will need a broader class of scenario analyses that leverage standardized processes and improved data aggregation.

In simple stress testing, the bank models and calculates the effect on a particular asset class of a change to a single risk factor or set of risk factors by a given amount. For instance, what happens to the mortgage-securities assets if equity prices decrease by 10 percent? This can be used to analyze how sensitive an asset is to a large change in a specific market factor, i.e. how exposed they are.

But banks need to expand stress testing across the portfolio, often called scenario analysis. This involves calculating the effects on a portfolio of stressing all underlying risk factors arising from a pre-defined market scenario. Scenario analysis is better suited to reproducing the effects of historical events, such as Black Monday, a period of loss in the bank’s history or a potential future event.

Integrated framework for Stress Testing
To achieve this broader scope of testing, banks need a framework that allows managers and analysts to easily run many different market and credit risk scenarios across a variety of risk factors and portfolios. Business users and risk analysts need a graphical interface to simplify the building and reviewing of stress test scenarios. The ability to use pre-defined scenarios or create user-defined scenarios is critical as is a robust visualization capability to compare the impact of scenarios across specific key performance indicators.

A framework that enables senior executives and business analysts alike to view consolidated results for a variety of stress test scenarios, and that can extend and enhance the ability to perform sensitivity and “what if” analyses would be a powerful asset, especially in the current  challenging economic conditions.

Coverage of asset types and consistency of criteria are large barriers to more effective stress tests and scenario analyses.  That coverage is hindered because disparate information prohibits a consolidated view of all assets. What’s more, multiple versions of data, valuation methods and models persist throughout the process. 

Financial institutions need an integrated approach to risk evaluation to understand events that have effects across risk types (e.g. market risk, credit risk, and operational risk) that, when combined, could result in exposure beyond isolated test results. Clear regulatory and industry standards for stress tests are essential to align asset classes and risk measurement methodologies across the industry for use within banks and between the banks and regulators. These standards for stress testing and scenario analysis would help remove some of the subjectivity from asset valuation and provide a more consistent risk calculation methodology.

Information from stress testing and scenario analyses provides tremendous benefits to financial institutions. The results point to specific operational gaps, vulnerabilities and threats that the banks can address. This makes the institutions stronger, more competitive and better positioned to provide better services to customers.

Michael Stefanick is Senior Manager of US Risk Practice for SAS. Michael.stefanick@sas.com 

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Three Ways to Improve Stress Testing

  1. Break down the silos – Transition stress testing from its narrow “silo” view to look more broadly across the firm.  Don’t focus solely on areas where there is known elevated risk. Implement a framework to aggregate enterprise position data to look at all asset classes.
  2. Implement a flexible framework for integrated stress testing and ad hoc analyses –  The business driver is to provide a desktop stress testing solution that allows analysts to immediately react to changes in the market by defining possible stresses and scenarios and observing their effects on the exposure in their portfolios, without the need for intervention by another party or department.  
  3. Continually refine the process – Banks should continually test and refine the models to validate the scenarios. Back-testing of models with historical data and events can increase the confidence in those models as part of an overall model-validation strategy to manage model risk.