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).
Perform stress testing and sensitivity analysis on financial portfolios.
Lifetime Expected Credit Loss Modeling
Estimate lifetime expected credit losses in compliance with risk regulations such as CECL and IFRS 9.
Calculating Regulatory Capital
Calculate capital requirements and value-at-risk with the asymptotic single risk factor (ASRF) model.
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.
Credit Risk Simulation
Perform copula simulations based on probability of default or credit rating migration to analyze the risk of credit portfolios.
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.
Risk Management Toolbox VaR backtesting models include traffic light, binomial, Kupiec's, Christoffersen's, and Haas' tests.
Expected Shortfall Backtesting
Backtesting models for expected shortfall (ES) include conditional test, unconditional test, and quantile test.
Backtest expected shortfall (ES) models using minimally biased Acerbi-Szekely tests
Expected shortfall (ES) model VaR level extended to 99.9%
Lifetime Credit Analysis
Probability of default models and examples
Chain ladder, expected claims, and Bornhuetter-Fergurson techniques for analyzing insurance claims reserves
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.