Risk Management Toolbox


Risk Management Toolbox

Develop risk models and perform risk simulation

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.

“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.”

Dr. Marco Folpmers, Capgemini

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