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Moody’s Analytics, a leader in risk measurement and management, today announced the release of an enhanced version of RiskCalc™ Plus, its private-firm probability of default model for credit risk management. New features include stress testing models for the US, ratio-driven global banking models for assessing financial institutions and a qualitative overlay module that combines the quantitative probability of default measure from RiskCalc with qualitative risk drivers into a single measure.
“Many risk practitioners are struggling to build, validate, and integrate credit analytics into their stress-testing platforms,” said Thomas Day, Senior Director at Moody’s Analytics. “RiskCalc Plus streamlines the process by allowing firms to focus less on process and more on risk analysis. As an established product, based on comprehensive data, granular analytical capabilities and flexible platform delivery choices, it is an ideal tool in any stress-testing process, as a primary, challenger, or benchmark model.”
Regulators are prescribing different modeling approaches and requirements, and are requesting transparency around stress testing modeling. RiskCalc Plus now offers two stress testing modeling approaches for evaluating US private-firm commercial and industrial loan portfolios.
The first, a ratio-based approach, incorporates fundamental analysis that links financial statement-based ratios to macroeconomic variables developed by Moody’s Analytics’ leading team of economists. The model forecasts pro-forma financials and provides transparency into financial ratio risk drivers under various stressed environments. Detailed economic scenarios for the US in the model include those of the Federal Reserve’s Comprehensive and Capital Analysis and Review (CCAR).
The second approach for modeling the portfolios of US private firms uses probability of default (PD) and loss given default (LGD) to provide an aggregated and pooled method for stress testing. The model aggregates exposures by credit quality and industry, leveraging an organization’s internal ratings. It forecasts expected loss (EL) and charge-offs based on macroeconomic scenarios, debt type, industry, and stressed PD and LGD levels. This model also leverages organization-specific scenarios to meet the firm’s specific modeling and software needs.