This article outlines the steps to perform reverse stress testing, which explores tail risks and reveals hidden vulnerabilities and scenarios not reflected through traditional stress testing analysis.
Regulators have advocated for the use of reverse stress testing to supplement stress testing by exploring tail risks and revealing hidden vulnerabilities and scenarios that are not reflected through traditional stress testing analysis. This article outlines the steps required to perform such analysis to meet regulatory expectations.
Reverse stress testing analysis offers a unique opportunity for financial institutions to better understand their business and focus management’s attention on the areas where weakness could turn out to be potentially harmful to the entire organisation. A reverse stress test explicitly identifies and assesses only the tail risk scenarios most likely to render business models unviable, that can cause the institution to default. This is a core difference when compared with traditional stress testing methodologies, where stress scenarios are chosen based on expert knowledge or historical evidence a priori.
Although an accurate modelling methodology able to characterise an institution’s business model and portfolio compositions is critical to identify and analyse hidden vulnerabilities within a reverse stress testing framework, the regulatory bodies have not provided methodological guidelines. However, the following principles should apply when developing one:
- Granularity: Able to drill down to individual factors that may affect the business lines or products.
- Consistency: Consistent with overall stress testing methodology and regulatory guidelines.
- Integration: Integrated within the enterprise risk management function and architecture.
- Flexibility: Fully customisable to the business model of the institution.
- Scalability: Accommodate future requirements in terms of asset coverage, portfolios, geographies, or regulatory guidelines.
From a workflow and data management perspective, as a best practice, institutions should develop centralised, enterprise-wide stress testing and reverse stress infrastructures that strive to integrate data, analytics, and reporting. All information critical to calculating, managing, reporting, and monitoring the stress and reverse stress testing results should be easily and cost effectively available.
From a regulatory compliance perspective, institutions’ enterprise risk management platform should be able to generate pre-configured stress testing and reverse stress testing reports by different regulatory jurisdictions. The institutions should also maintain the analysis history for trend analysis, auditing, and benchmarking across several dimensions and for each legal entity of the institution.
From an operational perspective, the institutions’ enterprise risk management platform should allow banks to drill down into each scenario to see the detailed underlying factors’ composition during the reverse stress testing calculation process.
From a reporting perspective, the platform should perform side-by-side comparison analysis between the stress testing and reverse stress testing results across jurisdictions, strategies, or portfolios.
To be effective, the reverse stress testing exercise should finalise an enterprise-wide contingency plan framework to address vulnerabilities before the changes hit and ensure the survival of the institution under those events.
Institutions should address the reverse stress testing analysis using a bottom-up modelling approach. The advantage of this approach is that it avoids solving inversion problems arising from maximisation-based models and at the same time accounts for all the risk dependencies during the simulation through the factors’ correlation structure and migration dynamics. On the other hand, top-down approaches are usually not suitable for reverse stress testing analysis since the factors’ realisations are aggregated and cannot be decomposed at an individual level.
Once the modelling flow and enterprise risk management architecture has been set at the institution, the reverse stress test analysis should start by specifying a target loss level, business line or sub-portfolio subject to the analysis. The analysis should then identify the macroeconomic shocks, scenarios, and tail risk factors driving those losses.
Subsequently, the connections with a portfolio’s performance, strategic events (merger, acquisition, new portfolio composition, etc.), and business model weaknesses (insolvency, bankruptcy, etc.) should be analysed as well. Therefore, the analysis would identify hidden vulnerabilities that may have not been detected during the stress testing analysis.
There are six main recommended stages when performing a bottom-up reverse stress testing analysis at the enterprise-wide level.
The first stage involves defining the appropriate loss level (e.g., confidence level) for the metric of interest for the financial institution (e.g., capital ratio, solvency ratio, etc.). The horizon for the analysis should be consistent with the requirements to fulfill the capital requirements under the corresponding regulatory jurisdiction and guidelines (e.g., one year under Basel III).
The second stage is to identify the factor draws and their combinations that had the most impact on the portfolio tail region through a quantitative discovery process. The factors and the loss associated with the portfolio in the tail region as well as which instruments, counterparties, countries, and industries react most to these states are also known at this step. The correlation structure affecting the institution’s balance sheet composition should be taken into account in the analysis as well.
For example, Figure 4 shows the factors that will make the institution unviable for a sample institution’s portfolio for a given target tail risk probability of 10 basis points (or equivalent, a target confidence level of 99.9%). In this specific case, the SME portfolio is the most reactive factor that causes the institution to default.
Once the most reactive factors have been identified from Stage 2, a sensitivity analysis is performed to measure the impact of these factors on an institution’s business model. This analysis is designed to uncover the severity of the scenarios needed for the financial institution to fail, or losses to exceed the given level of capital in Stage 1.
Factors from Stage 3 are ranked and mapped to macroeconomic variables and scenarios analysed during the simulation. In detail, for each simulated trial and each analysed sector a unique vector (φ) determines the relevant macroeconomic variables (MV) and their weights (w) at counterparty level:
Macroeconomic variables from Stage 4 are mapped to macroeconomic variables from the stress testing analysis, thus identifying hidden vulnerabilities and overlapping effects.
Finally, to be effective, the analysis should identify how resilient a bank’s business model is for different solvency and capitalisation rates. An enterprise-wide risk management diagnostic matrix should present the information, sensitivity analysis, and facilitate analysing the results for regulatory reporting and decision-making initiatives.
Having an enterprise-wide stress testing framework that acknowledges both traditional stress testing analysis and reverse stress testing is a game changer for financial institutions. Reverse stress testing addresses tail risk analysis by starting from a known stress test outcome and then asking what events could lead to such an outcome for the financial institution, revealing hidden vulnerabilities in the portfolio and in the firm’s stress testing framework that may not be detected during the stress testing analysis. Therefore, a robust and consistent portfolio bottom-up modelling approach is key to avoiding under or over-estimation of risk for assuring flexible risk management policies and increasing the return for the shareholders.
We have introduced a modelling framework that allows financial institutions to understand and identify the enterprise-wide risks under adverse conditions that may have serious implications for their solvency. The framework can be used to provide guidance and perform analysis in order to reveal hidden vulnerabilities and tail risks for several key metrics.
Douglas W. Dwyer leads Corporate Credit Research in Predictive Analytics. This group produces credit risk metrics of small businesses, medium sized enterprises, large corporations, financial institutions, and sovereigns worldwide. The group’s models are used by banks, asset managers, insurance companies, accounting firms and corporations to measure name specific credit risk for a wide variety of purposes. We measure credit risk using information drawn from financial statements, regulatory filings, security prices, derivative contracts, behavioral and payment information. For each asset class, the methodology is developed based on the available information for each obligor. <br><br> Current projects include developing a climate adjusted probability of default and incorporating ESG factors into credit analytics. We also are developing an approach to produces comparable PDs across asset classes that opportunistically uses whatever information is available. <br><br> Prior to working at Moody’s Analytics, Dr. Dwyer was a Principal at William M. Mercer, Inc., in their Human Capital Strategy practice. Dr. Dwyer earned a Ph.D. in Economics at Columbia University and a B.A. in Economics from Oberlin College.
Dr. Juan M. Licari is a managing director at Moody's Analytics. Juan and team-members are responsible for the research and analytics that enable our quantitative solutions. The team helps our customers solve complex business problems; adding value through data and analytics.
Focuses on stress testing in Europe – how banks can build an effective stress testing program, achieve critical business objectives, and ensure regulatory compliance.
Previous ArticleStress Testing Best Practices: A Seven Steps Model
Next ArticleA Macroeconomic View of Stress Testing
In this article, we examine the role of new and emerging technologies in the rapidly evolving financial technology space.
This article provides an overview of the new standard and analyzes the major challenges financial institutions will face in ensuring IFRS 9 compliance.
Banks should prepare for a new business ecosystem driven by the financial technology (FinTech) revolution. Learn how the industry can adapt to disruptions.
International Financial Reporting Standard 9 (IFRS 9) will soon replace International Accounting Standard 39 (IAS 39). The change will materially influence banks’ financial statements, with impairment calculations affected most.
This article discusses the importance of effective resolution plans, given their impact throughout a business.
Implementing an Effective Stress Testing Program for Risk Management Governance and Regulatory Compliance
This article discusses the regulatory view on governance for stress testing in the US, UK, and euro zone, as well as aspects of governance best practice and implementing an effective stress testing program.
On October 26th, the European Central Bank (ECB) published the results of the Comprehensive Assessment (CA – AQR and Stress Test). This article discusses the results, next steps such as the timeline and capital plan to meet the capital shortfall, other potential areas of enhancement at banks, and future expectations.
Preparing for the 2014 EBA Stress Test - Best Practices for Regulatory Stress Testing & Capital Modeling
This Moody's Analytics and PRMIA webinar-on-demand provides an overview of EU stress testing regulatory requirements and the Moody's Analytics capabilities and solutions that will help you meet them.
Learn more about liquidity stress testing.
This article discusses the importance of managing and measuring liquidity risk, regulatory guidelines and implications, and how an effective enterprise-wide stress testing program requires and integrates liquidity risk.