How do you incorporate stress testing and scenario analysis in market risk modeling?

One area helpful in ERM is to ensure an "apples to apples" comparison of KPIs as own goes deep under specific risk dimensions as well as across risk disciplines. For example, a KPI on the level of NPLs from a credit risk perspective may not be consistent with a KPI on chargeoffs that may cut accross interest rate, credit, and operational risk perspective. A sound ERM framework has a robust process of aggregating data accross all material risk disciplines to help ensure consistency in expressions for risk appetitie and risk tolerance.

Enterprise Risk Management (ERM) is a comprehensive approach that involves identifying, evaluating, and addressing all potential risks that could impact an organization's goals, operations, or reputation. The goal of ERM is to align the organization's risk tolerance and strategic direction, promote a culture of risk awareness, and maximize the balance between risk and reward. ERM encompasses a wide range of risk categories, including market, credit, operational, liquidity, strategic, and reputational risks.

Stress testing and scenario analysis allow market risk managers to anticipate and quantify the impact of rare but severe events on portfolios. By applying specific shocks (e.g., interest rate spikes or currency devaluation), stress testing reveals how individual risk factors affect asset values. Scenario analysis, combining multiple risk factors into realistic, adverse situations, shows broader vulnerabilities. Together, they help firms assess capital adequacy, liquidity buffers, and overall resilience, providing actionable insights for risk mitigation and better-prepared contingency plans.

The recent rapid rise in interest rates and effect on long duration high quality securites has reinforced the value of conducting forwarding looking exercises such as stress tests and linking such results to capital. Another important reminder is to take credible forward looking exercises seriously and not wait on contingent actions to mitigate the risks. For example, stressing high quality securities with a parallel incresing shift in interest rates may likely have shown an institution to take appropriate contingent actions on capital that may not have been alarming to key stakeholders.

Stress testing and scenario analysis aid market risk managers in assessing extreme event impacts on portfolios. Stress testing applies shocks to risk factors, observing value or income changes. Scenario analysis creates adverse scenarios to evaluate potential consequences. Both techniques identify vulnerabilities and test capital adequacy, informing risk plans.

To design effective stress tests and scenarios, we start by clearly defining our objective—whether it is to test liquidity, portfolio resilience, or regulatory compliance. We use validated, high-quality data and models tailored to our portfolio's specific risk factors. Scenario design should reflect the most relevant risks (e.g., geopolitical shifts or market crashes), ensuring transparency in assumptions. Finally, we communicate the results in a digestible way, focusing on key vulnerabilities and actionable recommendations, making sure stakeholders can respond quickly and effectively.

A top down and bottoms up process for identifying risks linked to scenarios helps ensure muliple perspectives are considered in the scenario design process. This includes SMEs across the 3 lines of defense as well as more senior level management that can provide a "birds eye" view on how specific vulnerabilies under stress could be interconnected.

Stress testing and scenario analysis are crucial for robust market risk modeling. These techniques provide a deeper understanding of potential losses beyond expected market movements. By simulating extreme events and evaluating the impact on risk metrics, we can identify vulnerabilities, refine risk mitigation strategies, and enhance decision-making. Stress testing involves applying hypothetical shocks to market variables, while scenario analysis explores plausible but less extreme scenarios. Both methods complement each other, offering a comprehensive view of market risk and ensuring that our risk management framework is resilient to a wide range of potential outcomes.