Recent abrupt changes in U.S. economic policies have sent shockwaves across both equity and credit markets, affecting both private and public sectors. The corporate debt and U.S. leveraged loan markets have been rattled by heightened volatility and declining investor confidence.

Persistent inflationary pressures have further fueled the market downturn, even raising concerns about a looming recession. Fortunately, though, risk managers have the weapons to combat these factors.

Adoption of timely early warning signals, for example, is one essential tool for implementing effective contingency plans. But successfully navigating these challenges also requires a consistent and reliable approach to assessing risk and return tradeoffs.

The most effective way to prepare for unprecedented scenarios is to construct comprehensive outcome distributions for an organization’s balance sheet or investment portfolio. These distributions can transition between different time horizons, integrating the assessment of both regular volatility and tail risk. What’s more, they can consistently estimate dynamic correlations across business lines and asset classes.

Proactive Steps for Public and Private Markets

During periods of extreme market stress, key performance indicators often become highly correlated, impacting securities portfolios, loan downgrades, collateral positions and depositor behavior. Indeed, as we’ve seen in past crises, a perfect storm for financial institutions often arises when structured product deterioration coincides with margin calls on hedges, liquidity shortages and rising funding costs. So, what specific steps can financial institutions and their risk managers take to better assess and mitigate this dangerous tail risk?

Reactive measures are insufficient, akin to purchasing fire insurance after a house is already ablaze. Organizations must instead establish their own early warning signals to navigate away from danger proactively.

Risk managers can detect early warning signals by generating a comprehensive range of scenarios. Through analyzing the outcomes at the distribution horizon, they can track back the scenarios leading to the adverse outcomes and identify the action point at which they can mitigate risks.

The strength of full-range scenario analysis lies in its ability to model macroeconomic and market variables consistently across both stable conditions and hypothetical shock scenarios. Driven by real probabilities, these scenarios also enable risk-neutral discount curves, allowing risk and investment managers to evaluate public, private and structured securities with appropriate techniques.

While publicly-traded instruments benefit from extensive information and analysis tools, private credit and equity remain challenging to evaluate accurately. At the same time, evaluating private assets is critical, considering that private debt emerged as the second-largest fundraising strategy in private capital markets over the past decade.

This is easier said than done. The absence of centralized reporting and public ratings has made it particularly difficult to identify emerging risks in private credit. Investors, moreover, often rely on limited borrower disclosures and fund-level reports, increasing the likelihood of mispricing and valuation uncertainty. Implementing robust scenario analysis and monitoring systems is therefore essential for proactively managing these challenges.

One approach to enhance scenario analysis for private credit is generating synthetic data using peer group analysis. By leveraging data from comparable companies within similar industries, market segments and credit profiles, organizations can simulate borrower financials, operational performance and market behaviors.

ML-based regressions and dynamic Monte Carlo simulations with disruptive shocks can further refine these synthetic datasets to overcome historical bias, while simultaneously reflecting macroeconomic conditions and sector-specific risks.

Leveraging CAMELS

To ensure consistency in assessing and integrating KPIs across the entire balance sheet, covering all asset classes and liabilities, organizations can adopt the combinations of capital, liquidity and other ratios similar to CAMELS – an international rating system that bank supervisors use to assess capital adequacy, asset quality, management, earnings, liquidity and sensitivity to market risk. While regulators employ CAMELS to evaluate the financial health of banks, it also provides a structured and flexible framework for monitoring integrated performance.

Unlike traditional ratings, CAMELS ratings offer customizable insights that can be tailored to specific market conditions and the specific risks facing an institution. All the ratios and their underlying components should be linked to the full range of generated scenarios.

Projecting institutions’ balance sheet segments, calculating respective ratios and combining them into composite CAMELS ratings for multiple market scenarios provides a proactive and forward-looking perspective. For example, capital adequacy can be assessed through leverage ratios and loss-absorbing capacity, while asset quality can be measured using delinquency rates, borrower financial strength and a market-comparable peer group.

The management component of CAMELS can be estimated by evaluating the impact of acquisitions, management changes and operational risk events, while the earnings analysis considers yield stability and net interest margins. Funding liquidity, meanwhile, is measured as a ratio of short- to long-term liabilities, while sensitivity analysis assesses reactions to markets movements.

In addition to getting the full distribution of potential outcomes, risk managers, investors and research analysts can also obtain an early warnings picture through projecting CAMELS on all generated scenarios. What’s more, a firm can customize exactly how its CAMELS rating is composed, what weights are given to each KPI and how the rating notches are defined.

The key is consistency. If you’re a risk manager comparing yourself to your peer group, you’ll get a good idea about how your firm will weather the storm by applying the same ratios and by composing the final result with the same weighted ratios.

Figure 1 shows an example of healthy (Bank A) and shaky banks (Bank B), using historical data up December 2022. The tables show the probabilities of these example banks to either maintain their CAMELS rating or to transition up or down, starting in December 2022 and extrapolating for the next 12 quarters.

Figure 1: Custom CAMELS Rating Transition Probabilities

Bank A

Bank B

Even a quick glance at Figure 1 shows the difference in the direction and width of the rating transition probabilities cones for the example banks. Despite being quite similar with respect to geography, size and the sectors they cover, Bank B would have defaulted in the turmoil of 2023 regional bank crisis, while Bank A would have survived and prospered.

The main advantage in constructing such rating transition tables is their full transparency, as the probabilities are derived from the full range of generated scenarios. Risk managers can use this data to get answers to, for example, the following questions: (1) Why is there a large probability of a downgrade one year from now? (2) What KPI in CAMELS is driving this? (3) Is this an issue with the numerator (e.g., increasing delinquencies and charge-offs in the numerator of the capital ratio) or denominator (e.g., risk weighted assets in the denominator of the capital ratio growing too big, too fast)?

Full-range scenario analysis can also help a firm determine which component of the ratio is driving an adverse outcome. Is it, for example, residential or commercial real estate lending, a certain investment strategy, or some liability product? It can, moreover, provide clarity on what macro-market environment (e.g., inflation or a recession) is causing it.

Research analysts and investment managers can use this approach to make better, more accurate forecasts of potential risks across capital, liquidity and earnings.

Parting Thoughts

Adopting the CAMELS framework and weaving it into full-range scenario analysis offers many benefits. For example, it facilitates a comprehensive and consistent view of risk, aids proactive decision-making and enhances portfolio/balance sheet resilience.

What’s more, this flexible, scenario-driven approach ensures that institutions are not only prepared to respond to immediate challenges and market turbulence but also equipped to capitalize on emerging opportunities and to build resilience against future unprecedented scenarios.

Source: GARP “Striving for Stability Amid a Perfect Storm: How to Align Risk, Lending and Investing”