Risk Management Toolbox
Develop risk models and perform risk simulation
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Risk Management Toolbox supports mathematical modeling and simulation of credit, market, insurance, and climate risk. You can model lifetime probabilities of default (PD), exposure at default (EAD), and loss given default (LGD) and calculate expected credit losses (ECL). You can assess corporate and consumer credit risk, create credit scorecards, estimate PD, and perform credit portfolio analysis.
The toolbox lets you screen important scorecard variables and automatically or manually bin variables using the Binning Explorer app. You can assess market risk with value-at-risk (VaR) and expected shortfall (ES) models. The toolbox provides a comprehensive suite of model validation metrics for credit models and VaR and ES backtests. It also includes mortality and unpaid claims models to quantify and analyze insurance risk. You can visualize and analyze climate scenario data to assess physical or transition climate risk.
Climate Risk Modeling
Analyze and assess climate-related risk for financial assets.
Validating Risk Models
Validate risk models with discrimination and calibration metrics.
Consumer Credit Risk Modeling
Create and analyze credit scorecards, perform predictor screening, explore fairness metrics, conduct stress tests, and model probabilities of default (PD).
Corporate Credit Risk Modeling
Analyze corporate default probabilities, simulate credit portfolio value changes due to credit rating migrations and defaults, identify concentration risks, and calculate regulatory capital requirements.
Backtesting Models for Market Risk
Assess the accuracy of value-at-risk (VaR) and expected shortfall (ES) models.
Lifetime Models for Probability of Default (PD)
Estimate probability of default based upon lifetime analysis with macroeconomic scenarios using MATLAB. PD models include logistic, probit, and Cox.
Loss Given Default (LGD) Models
Estimate loss reserves using regression and tobit models.
Exposure at Default (EAD) Models
Predict the amount of loss exposure for a creditor when a debtor defaults on a loan using regression and tobit models.
Insurance Risk Modeling
Calculate the risk of loss arising from mortality and unpaid claims. Estimate ultimate claims using the chain ladder bootstrap method.
Product Resources:
“Some statistical tools can handle credit scoring models based on multivariate statistics or logistic regression, but are not well-suited to the advanced economic capital models needed for Basel II. With its computational power, matrix infrastructure, and ability to perform Monte Carlo simulations, MATLAB gives us a competitive advantage in performing complex risk analyses.”
Risk Management Toolbox FAQs
Risk Management Toolbox is a MATLAB product that supports mathematical modeling and simulation of credit, market, insurance, and climate risk.
The toolbox supports modeling of credit risk (corporate and consumer), market risk, insurance risk, and climate risk.
You can model lifetime probabilities of default (PD), exposure at default (EAD), and loss given default (LGD), calculate expected credit losses (ECL), create credit scorecards, and perform credit portfolio analysis.
The Binning Explorer app lets you automatically or manually bin variables for credit scorecard creation.
The toolbox enables you to assess market risk with value-at-risk (VaR) and expected shortfall (ES) models, along with comprehensive backtesting tools.
Yes, it supports insurance risk analysis for unpaid claims, including techniques such as development triangle, chain ladder, expected claims, Bornhuetter-Ferguson, and Cape Cod.
You can visualize and analyze climate scenario data to assess physical or transition climate risk for financial assets.
The toolbox provides a comprehensive suite of model validation metrics for credit models and VaR and ES backtests, including drift, discrimination, and calibration metrics.
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