Resilient Finance: SAS Stress Testing Calculation Process
Stress testing is a proactive methodology employed to assess how financial institutions can endure under challenging economic conditions. It involves assessing potential losses, capital adequacy, and liquidity requirements to prepare for unexpected market disruptions. This practice is essential for ensuring stability, meeting regulatory standards, and guiding strategic decision-making in an increasingly volatile financial landscape.
SAS Stress Testing
SAS Stress Testing solution uses SAS Risk Cirrus platform as a foundational environment. Cirrus platform is a cloud native risk management solution ecosystem that is built on and fully integrated with the SAS Viya analytics platform. It features intuitive solution editing capabilities in the cloud with minimal coding requirements.
Cirrus platform is the evolution of the SAS Stratum platform on SAS 9.4. Cirrus takes advantage of the open distributed environment using a containerized architecture, making the technology of the Cirrus platform very different from that of Stratum. Since it is built on SAS Viya, it leverages SAS Viya features and services to manage the environment, offers quick deployment, and utilizes continuous integration and delivery of processes for deployment of updates.
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Calculation Process in SAS Stress Testing
Data Preparation
SAS Stress Testing facilitates the entire regulatory cycle, encompassing all necessary stages from data preparation to final reporting.
During the Data Preparation phase, the system connects to the specified database and retrieves source data using predefined configurations. This data is stored in the SAS Risk Data Service (built on a PostgreSQL database) and is further registered within the SAS Stress Testing environment as Data Definitions and Analysis Data.
Subsequently, pre-defined data quality checks are executed to ensure accuracy and consistency. The results of these checks are saved in the SAS Risk Data Service, with detailed and summary-level outputs recorded in the Data Quality Report Mart. These outputs form the foundation of the Data Quality Report, which is accessible via the Dashboard, offering a clear view of the data's integrity and readiness for analysis.
This streamlined approach ensures that institutions meet regulatory demands while maintaining the accuracy and traceability of data throughout the stress testing lifecycle.
Business Projection Analysis
The Business Projection Analysis process begins by sourcing data from key tables, including the Business Evolution Plan (BEP), static parameters, and horizon parameters. It retrieves relevant scenarios from the Risk Scenario Repository and applies the Business Evolution Plan Overlay Model to simulate outcomes. The analysis generates results stored as analysis data, alongside a detailed Business Evolution Plan Report Mart, which populates the Business Evolution Plan Report.
This process forecasts critical financial and operational metrics, such as revenue, expenses, and cash flow, under various stress scenarios. By doing so, it identifies vulnerabilities in meeting financial obligations or maintaining liquidity. These insights enable institutions to implement proactive measures, such as optimizing capital reserves or restructuring liabilities, to mitigate risks and improve resilience.
The BEP Overlay Model enhances projections by incorporating dynamic elements, such as strategic initiatives, product launches, or market repositioning, into hypothetical adverse scenarios like economic downturns or significant rate changes. Unlike static models, the BEP approach includes management actions and strategic adjustments, providing a more nuanced understanding of potential impacts. This forward-looking perspective helps institutions assess how structural changes—such as operational strategies or capital allocation adjustments—could influence performance during severe economic stress.
Portfolio Projection Analysis
In the Portfolio Projection Analysis task, the process begins by referencing the BEP and executing allocation models for the defined portfolio segments. Next, the relevant scenarios are retrieved from the Risk Scenario Repository. The solution then goes on to generate the front book (or import it from some other solution).
Portfolio projection analysis plays a pivotal role in stress testing, focusing on the evolution of both existing exposures (back book) and anticipated new exposures (front book) over the stress testing horizon. The front book reflects future business activities, including new transactions and customer engagements, which introduce variability in risk profiles. The analysis models how these new activities influence financial metrics, such as asset quality, interest rate sensitivity, and customer behavior.
By integrating front book generation into portfolio projection analysis, financial institutions can comprehensively evaluate risks, ensuring robust capital planning and effective risk mitigation strategies.
Credit Risk Analysis
In the Credit Risk Analysis task, the system retrieves credit portfolio analysis data, applies the specified scenarios from the Risk Scenario Repository, and runs the selected stress testing models. The results are stored as analysis data, and a Credit Risk Report Mart detail table is created to support the generation of the Management Report on the Dashboard.
Credit risk analysis is a vital component of financial stress testing because it assesses the potential losses an institution could face from borrower defaults or a decline in credit quality under adverse economic scenarios. As one of the largest risks banks and financial institutions face, credit risk directly affects their solvency and capital adequacy. Including credit risk analysis in stress testing ensures that institutions are prepared to handle these risks in times of economic distress.
The analysis quantifies how a portfolio of loans, bonds, and other credit exposures would perform during periods of financial stress, such as economic recessions, rising unemployment, or industry downturns. It evaluates the likelihood of defaults, downgrades, and credit deterioration, allowing banks to estimate potential losses and the capital required to absorb them.
Without robust credit risk evaluation, financial institutions risk underestimating their vulnerabilities, potentially leading to insufficient capital buffers during crises and jeopardizing their stability.
Reconciliation
The Reconciliation task starts by extracting data from key sources, including the BEP, static parameter table, horizon parameter table, and selected scenarios from the Risk Scenario Repository. It then applies the BEP Provision Model and stores the results as analysis data. Additionally, it creates or updates the Business Evolution Plan report mart details table, which is used to populate the Business Evolution Plan Report on the Dashboard.
In stress testing, accurate reconciliation of financial statements, such as the balance sheet, income statement, and cash flow statement, is essential. This process ensures that these financial documents reflect the firm’s true financial position before subjecting them to hypothetical adverse scenarios.
Reconciliation is crucial because stress tests depend on precise baseline data to simulate how an institution might react to various stressors, including market declines, rising interest rates, or increased loan defaults. Proper reconciliation ensures that the data used in these simulations is consistent and reliable, forming the foundation for accurate stress testing outcomes.
Attribution Analysis
In the Attribution Analysis task, the process follows a predefined template to analyze how various risk factors contribute to the overall financial outcomes during stress testing. The results, categorized by these risk factors, are appended to the Credit Risk Report Mart detail table, which is then used to generate the Management Report on the Dashboard.
Attribution analysis enhances stress testing by breaking down the impact of individual risk factors, assets, or strategies on the overall financial results. This analysis is crucial for understanding the underlying causes of potential vulnerabilities and guiding informed risk management decisions.
For example, during a stress test simulating an economic recession, attribution analysis can help isolate the effects of different factors—such as changes in interest rates, deteriorating credit quality, or increased market volatility—on the firm’s capital position or profitability.
Top-Down and Bottom-Up Analysis
The Stress Testing calculation process can also be categorized into Top-Down, Bottom-Up, and Granular analyses.
The Top-Down analysis starts with business projection analysis, where growth assumptions are applied at a high level—such as the portfolio or overall financial statement level. These assumptions are then refined and broken down into more granular components, eventually leading to the creation of the front book.
This front book can be derived directly within the stress testing system or imported from other solutions, such as SAS ALM (Asset Liability Management). This integration between different tools, such as SAS Stress Testing and SAS ALM, enables a seamless flow of data, allowing institutions to track the generation of the front book effectively—whether it’s done internally or imported externally from other SAS risk solutions.
In the Granular analysis, growth assumptions are applied at a much more detailed level, such as the instrument or transaction level. This deeper analysis involves running a credit risk analysis at the individual instrument level, which is similar to conducting a portfolio analysis but with greater specificity. For example, if we are evaluating Expected Credit Loss (ECL), then the analysis takes place at the instrument level, where each loan or bond is assessed based on its individual characteristics and risk.
Finally, the Bottom-Up approach involves taking the detailed, instrument-level results and consolidating them with the original growth assumptions. This comparison provides insights into how the granular factors influence the overall financial metrics, such as the Expected Credit Loss (ECL), and helps determine whether the initial growth assumptions remain valid under stress scenarios.
Additional Information
For more information on SAS Stress Testing visit the software information page here. For more information on curated learnings paths on SAS Solutions and SAS Viya, visit the SAS Training page. You can also browse the catalog of SAS courses here.
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