Risk Management Toolbox

Develop risk models and perform risk simulation


Risk Management Toolbox™ provides functions for mathematical modeling and simulation of credit and market risk. You can model probabilities of default, create credit scorecards, perform credit portfolio analysis, and backtest models to assess potential for financial loss. The toolbox lets you assess corporate and consumer credit risk as well as market risk. It includes an app for automatic and manual binning of variables for credit scorecards. It also includes simulation tools to analyze credit portfolio risk and backtesting tools to evaluate value-at-risk (VaR) and expected shortfall (ES).

Get Started:

Risk Modeling and Risk Regulation

Create risk models to comply with regulatory requirements for Basel III, Solvency II, CECL, and IFRS 9.

Lifetime Expected Credit Loss Modeling

Estimate lifetime expected credit losses in compliance with risk regulations such as CECL and IFRS 9.

Lifetime probability of default for a stress test.

Calculating Regulatory Capital

Calculate capital requirements and value-at-risk with the asymptotic single risk factor (ASRF) model.

Regulatory capital by asset class.

Credit Risk Modeling

Model and analyze the risk exposure of credit portfolios.

Credit Scorecards Modeling

Use the Binning Explorer app to develop credit scorecards by applying auto-binning algorithms or interactively adjusting edges, merging bins, and splitting bins. You can also fit a logistic model, obtain points and score, and calculate the probability of default.

Binning Explorer app for credit scorecard modeling.

Credit Risk Simulation

Perform copula simulations based on probability of default or credit rating migration to analyze the risk of credit portfolios.

Portfolio losses based on copula simulations.

Risk Parameters Estimation

Estimate probability of default (PD) using various methods, including structural models, reduced-from models, historical credit rating migration, and other statistical approaches. Additionally, you can use Risk Management Toolbox to calculate concentration risk indices.

Lorenz curve for representing the distribution of risk exposure.

Backtesting Models for Assessing Market Risk

Assess the accuracy of your value-at-risk (VaR) and expected shortfall models.

Value-at-Risk Backtesting

Risk Management Toolbox VaR backtesting models include traffic light, binomial, Kupiec's, Christoffersen's, and Haas' tests.

Results from multiple VaR backtesting models.

Expected Shortfall Backtesting

Backtesting models for expected shortfall (ES) include conditional test, unconditional test, and quantile test.

Historical VaR and ES plot.

Latest Features

Market Risk

Backtest expected shortfall (ES) models using minimally biased Acerbi-Szekely tests

Market Risk

Expected shortfall (ES) model VaR level extended to 99.9%

Lifetime Credit Analysis

Probability of default models and examples

Insurance Analysis

Chain ladder, expected claims, and Bornhuetter-Fergurson techniques for analyzing insurance claims reserves

See release notes for details on any of these features and corresponding functions.

Computational Finance Suite

The MATLAB Computational Finance Suite is a set of 12 essential products that enables you to develop quantitative applications for risk management, investment management, econometrics, pricing and valuation, insurance, and algorithmic trading.

Additional Risk Management Toolbox Resources