Risk Management Toolbox


Risk Management Toolbox

Develop risk models and perform risk simulation

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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

Identify the variables in your data sets that have the best predictive power using tools for predictor screening. Once you’ve identified important variables, 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. Once developed, deploy a lightweight version of the model using compact credit scorecard.

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. Simulation throughput can be increased through parallel computing using Parallel Computing Toolbox.

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. Use the lifetime probability of default (PD) models to estimate the loss reserves based on a lifetime analysis conditioned on macroeconomic scenarios. 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, quantile test and minimally biased test by Acerbi and Szekely, as well as conditional and unconditional tests by Du and Escanciano

Historical VaR and ES plot.

Insurance Risk

Calculate the risk of loss arising from mortality and unpaid claims.

Claims Estimation

Use the development triangle along with other estimation techniques such as chain ladder, expected claims, and Bornhuetter-Fergurson to estimate unpaid claims.

Development of the reported claims

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.