Firmwide Stress Testing

Firmwide Stress Testing Establishing the Environment WHITE PAPER

SAS White Paper Table of Contents Overview.... 1... 2 Sources.... 4 Models.... 5 Scenarios.... 6 Analysis... 7 Results.... 8 Conclusion.... 9 The content provider for this paper was Samuel A. Muñoz, SAS Americas Risk Practice.

Firmwide Stress Testing Overview The recent global financial crisis revealed that financial institutions and even whole economies are vulnerable and ill-prepared for severe systemic shocks. These crises prove that greater rigor and increased investments in financial risk management are required. Stress testing is an important tool in a holistic risk management regime. Its benefits go a long way to ensure institutions are adequately prepared and capitalized to absorb severe shocks. Regulatory bodies have mandated stress testing as an important part of a risk management regime. The largest financial institutions in the US are required to undergo routine stress tests, as outlined in the Federal Reserve s Comprehensive Capital Analysis and Review (CCAR) and Supervisory Capital Assessment Process (SCAP). Basel II Pillar 1 requires a stress testing program to be in place for European institutions. Emerging market nations acknowledge the importance of the tool, as evidenced by China s initiative in conducting its Financial System Stability Assessment. Since the trend is for increasingly sophisticated and relevant stress tests, it is important that institutions stay ahead of the curve in meeting these demands. In this document, rather than focus on the particulars of regulations, we instead turn our attention to the components needed for establishing a stress testing environment. The target audience includes those individuals responsible for developing, implementing and/ or maintaining a stress testing solution. Specifically, we discuss the following areas: defining and employing a workflow for managing the process. Sources validating the data from external and internal sources. Models examining the role that modeling plays in stress testing and risk factor behavior. Scenarios reviewing the scenarios typically employed in stress tests. Analysis outlining the types of analyses most often used in stress tests. Results reviewing the structure, persistence and downstream uses. Generally, these are the major components of a stress testing solution. The specifics will vary depending on the objectives of the particular stress test. For more information on the practicalities and process of stress testing, please refer to this paper s sister publication A Case Study in Firmwide Stress Testing: Engineering the CCAR Process. 1 1 A Case Study in Firmwide Stress Testing: Engineering the CCAR Process (sas.com/reg/wp/corp/52685). 1

SAS White Paper Sources Models Scenarios Analysis Results A successful stress test requires a clearly defined workflow process to achieve its goals. This process ought to have clearly defined roles, approvals and actions. Each step should be validated and approved before moving on to the next set of tasks so that there is an orderly, fully auditable execution of tasks from initiating the stress test to producing the appropriate reports, schedules and dashboards. Defining a workflow usually involves several departments, groups, teams and committees across an institution. In the example below (Figure 1), the stress testing group orchestrates the process, performs calculations and aggregates data supplied by the lines of business. The committee reviews the results before they are submitted to the regulatory agencies. Each stage is dependent on the prior one giving appropriate review and approval. In the example in Figure 1, a stress testing group is responsible for initiating and orchestrating the process. Such a process is typical in institutions with centralized risk management departments, but is by no means a definitive approach. There are instances where the lines of business are more autonomous and perform more tasks, such as modeling and calculation, with the stress testing group aggregating these results at the parent holding company level. This second approach, however, requires close coordination to ensure that the models and scenarios are consistent across lines of business; if not, there is the risk of aggregating results that are based on different, even incompatible, conditions. 2

Firmwide Stress Testing Stress Test Group Initiate Perform Calculations Request Line of Business Line of Business Send Position Stress Test Group Obtain Scenario and Market Perform Calculations Aggregate Financial Reporting Generate Reports and Schedules Stress Test Committee Generate Reports and Schedules Submit to Regulatory Agency Figure 1: Example workflow. To facilitate an orderly process, a workflow engine and user interfaces are needed. This workflow engine should allow changes to be made easily, give various users access to the process, grant applications across the enterprise and send notifications to the appropriate parties. SAS Studio, included in SAS Model Manager, is a powerful and valuable tool that will streamline the stress testing process and facilitate intra-institutional efficiency. It allows graphical creation, production deployment and interaction from both within SAS and by third-party applications to workflows. The importance of a customized workflow reflecting the realities of the institution cannot be overstated as transparency, auditability and accountability are just as crucial as the results. 3

SAS White Paper Sources Models Scenarios Analysis Results Sources Stress testing is a data-intensive exercise requiring data from disparate sources. It is not uncommon to discover data issues in the calculation and/or reporting phase. Such discoveries incur costly man-hours replaying through the whole process in worst-case scenarios. Verifying the data early and at every stage expedites the process. Generally speaking, required data falls into four categories: 1. Supporting data: macroeconomic, market, interest and foreign exchange rates, commodity and equity prices, etc. 2. Counterparty data: ratings, RSQ ( R-squared or the square of the Pearson product moment correlation coefficient, LGD (Loss Given Default), PD (Probability of Default), etc. 3. Financial instrument and contract data: bonds, derivatives, equities, terms and conditions, etc. 4. Portfolio: position information and hierarchy, which is dependent on all of the above. The undertaking is no small matter with the data interdependency requiring intrainstitutional coordination. By including a task in the workflow that requires responsible parties to verify data, the risk of potential problems downstream is reduced. Also fully vetted extract, transform, load (ETL) jobs will be needed to stage data. Stewards of these ETL jobs should be notified of structural changes in the source system that may invalidate assumptions on the expected format and/or values. SAS provides a variety of data management and integration technologies that can be tailored to a customer s needs. The SAS stress testing solution takes advantage of SAS powerful data management capabilities. The framework includes data management, the SAS Detail Store for Banking (DDS) (Figure 2), SAS Risk Reporting Repository (RRR), access controls, audit, ETL for data access for both SAS and major third-party databases, as well data validation controls. All of the enterprise tools required for stress testing are combined in one holistic package with an emphasis on firmwide stress testing. 4

Firmwide Stress Testing Econometrics Sources Counterparty Portfolio, Positions and Hierarchy Existing Marts and Warehouses SAS Detail Store Market Figure 2: Disparate data sources feeding SAS Detail Store. Sources Models Scenarios Analysis Results Models While this paper does not delve deeply into models, they do serve a crucial role in stress testing and, as such, a basic understanding of their function is warranted for risk technologists. Careful consideration must be paid to how, for example, interest rates will behave, how credit rating migrations occur between the various ratings or how volatility is defined. An important consideration in model selection is how the risk factors move in tandem during times of duress. Especially important are modeling risk factors and econometric correlations. In addition to standard models (Table 1), SAS provides a framework that allows proprietary and third-party models to be created and used. The flexibility of being able to define, modify and preserve models increases stress testing value-add over closed systems that are locked into one vendor s implementation. Risk Factor Interest Rate FX Rate Options Volatility Subrisk Model Vasicek, Ho-Lee, Hull-White Geometric Brownian Motion Cox, Ingeroll and Ross GARCH Correlation Table 1: Examples of models supported by SAS. 5

SAS White Paper Sources Models Scenarios Analysis Results Scenarios While there are several types of stress tests that can be performed sensitivity analysis, Monte Carlo simulations, reverse stress testing regulators are increasingly focusing on scenario stress testing. This focus is being primarily driven by the need for consistency across different institutions standardized adverse scenarios. Historical and hypothetical scenarios detail how relevant risk factors and macroeconomic conditions behave for a predefined time frame. In both cases, the relationships between risk factors are prescribed. However, for a scenario to be useful it must produce substantial shocks in market, credit and economic conditions. Mild scenarios will lull risk managers into a false sense of security with benign portfolio impact. Additionally, the probability of a particular scenario ought to be known to help support the buy-in from senior management and risk committees. Scenario data can be obtained from historical data sets or economic scenario generator engines; it can also be provided by a third party. For certain types of stress tests, such as SCAP and CCAR, the regulator will provide some of the data. In cases where all the risk factors are not present, models can be used to complete the required data for analysis. For reference, documented below (Table 2) is a collection of recent crises starting with the 1987 Black Monday and ending in with the recent 2007-2011 global financial crisis. Year Crisis 1987 Black Monday 1989-91 US Savings and Loan Crisis 1990 Japanese Asset Bubble, Swedish and Finnish Banking Crisis 1992-93 Black Wednesday 1994-95 Mexican Peso Crisis 1997-98 Asian Financial Crisis 1998 Russian Ruble Crisis 2001 Argentine Crisis 2001 Dot-Com Bubble Bursting 2007-11 Global Financial Crisis Table 2: Wikipedia list of recent financial crises. Once the scenario data is obtained, it can be fed into appropriate models to determine additional risk factors, forecasts, risk metrics calculations and balance sheet/income statement projections. 6

Firmwide Stress Testing Sources Models Scenarios Analysis Results Analysis The risk engine is the heart of the process and has the primary responsibility for revaluing the portfolio. The aforementioned components (data, models and scenarios) are used as inputs after being vetted by the appropriate groups. The risk engine will limit the types of analysis that can be performed along with asset pricing functionality. For this reason, it is important to select an engine that will support various analysis types, with third-party plug-in functionality for models, scenarios, pricing functions and different risk types (e.g., credit, market and operational risk). As stress testing capabilities and demands are expanded, this flexibility will reduce future costs by reducing the technology stack. The SAS stress testing solution offers the described capabilities, as well as cash-flow analysis, optimization, multilevel netting and custom aggregation. It offers scalable performance for handling large portfolios, external third-party functions and regulatory reporting requirements. It ensures that the same scenario is used across different risk types (market, credit and liquidity) while delivering firmwide metrics. The architecture (Figure 3) provides a flexible and extensible framework to grow with a firm s evolving needs. Portfolio VaR Models Risk Engine Scenario: Market and Econometric Risk Measures Potential Future Exposure Regulatory and Economic Capital Etc. Analysis Aggregation Figure 3: SAS risk solution architecture. 7

SAS White Paper Sources Models Scenarios Analysis Results Results Risk results are the outcome of the analysis. The results data is needed for downstream reporting systems, dashboards and spreadsheets. There is also tremendous value in preserving this results data. When combined with persisted inputs (e.g., risk factors, reference data) from source system, the results provide a snapshot of the institution and analysis outcomes. A complete system using the same data, models, scenarios and analysis ought to be able to reproduce the same results (depending on the analysis type). This repeatability is extremely important during an audit to ensure there are no material deficiencies. Results will vary in granularity and type, depending on the analysis configuration and goals of the stress test. They could include economic and regulatory capital, various VaR measures, financial projections, and concentrations. In the case of a comprehensive and holistic firmwide stress test, it will include granular results at the position level across all lines of business and different risk types: credit, market, liquidity and operational. A good framework will provide firmwide aggregated measures. In many cases, this task needs to be performed by the risk engine as some risk measures are nonadditive, such as VaR measures and diversification benefits. A flexible results repository will increase the value of results to downstream systems. A normalized set of database tables tying reference data and inputs results for each stress test execution will allow comparison of results across scenarios. The rationale for results persistence becomes more compelling when coupled with memory-intensive business intelligence (BI) applications and the OLAP cube structures they require. The SAS stress testing solution provides a reporting repository to house results, as well as BI capabilities. High-performance offerings from SAS provide fast calculations and aggregation, 2 as well as all in-memory drill-down to the position level. Advanced visualization capabilities allow on-the-fly slicing and dicing of data without timeconsuming OLAP cube generation. 2 Illustrative result of 65 seconds to aggregate 143,000 positions, 40 time horizons, and 28.6 billion risk statistics using a test grid of 200 processor cores with eight threads per core. 8

Firmwide Stress Testing Conclusion There is no one-size-fits-all approach to stress testing, but, nonetheless, we have focused on key components that are part of a holistic, rigorous and transparent stress testing framework. We have also highlighted a fraction of what the SAS stress testing solution offers in the critical areas of workflow, data, modeling, scenarios, analysis and results. Close coordination between several groups in an institution will be required. Engineering the process, gaining input and obtaining buy-in from stakeholders are critical first steps. The need for coordination will increase in both the implementation phase during data mapping exercises and model development and in production execution, where data from disparate sources is collected and verified all the way through managerial and regulatory reporting. While the burden of acquiring and operating such a framework appears to be great, greater still are the costs of noncompliance, insolvency and regulatory action. A stress testing framework is an investment in institutional longevity, preparing the firm to weather storms, improve risk-adjusted profitability and efficiently deploy its capital. Firms that rise to this challenge will reap the rewards. 9

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