Finance and Risk Management

In just a few lines of MATLAB® code, you can prototype and validate computational finance models, accelerate those models using parallel processing, and put them directly into production.

Leading institutions use MATLAB to determine interest rates, perform stress tests, manage multi-billion dollar portfolios, and trade complex instruments in less than a second.

MATLAB is fast: Run risk and portfolio analytics prototypes up to 120x faster than in R, 100xfaster than in Excel/VBA, and up to 64x faster than Python.

MATLAB automatically generates documentation for model review and regulatory approval.

Analysts use prebuilt apps and tools to visualize intermediate results and debug models.

IT groups can deploy IP protected models directly to desktop and web applications such as Excel, Tableau, Java, C++, and Python.

MATLAB includes an interface for importing historical and real-time market data from free and paid sources including Bloomberg, Refinitiv, FactSet, FRED, and Twitter.

MATLAB handles big and streaming data from traditional and alternative data sources.    

Investment Management

Build and evolve dashboards for portfolio managers, with intraday risk reporting, valuation, and trade execution capabilities. 

Use prebuilt tools for performing portfolio optimization using mean-variance, mean absolute deviation (MAD), conditional value-at-risk (CVaR), and Black-Litterman methods.

Measure investment performance using risk-adjusted alphas, tracking errors, maximum drawdowns, and the Sharpe ratio.

Risk Management

Automate, augment, and provide executable reporting throughout the risk model lifecycle. Take models through model validation, model review, implementation, and regulatory approval in just three months.

Build risk management systems or stress testing infrastructure for CCAR, DFAST, Basel III, and Solvency II.

Use models and functions to quantify risk exposure (e.g., market, credit, and operational risks), validate the models using VaR and expected shortfall backtesting, and supplement the traditional methods with machine learning algorithms and text analytics.

Financial Forecasting and Modeling

Use point-and click apps to fit time-series data with econometric models (e.g., ARMA, ARIMA, GARCH, EGARCH, GJR) or machine learning algorithms.

Interface to DSGE models to forecast key economic variables.

Use functions for interest rate modeling and forecasting based on parameters estimated from the Nelson-Siegel or Svensson models.

Insurance and Actuarial Science

Analyze large data sets, create custom actuarial models, and easily accelerate the simulations using parallelization.

Build custom risk models using MATLAB as a platform for Solvency II.

Price various insurance products such as variable annuities, guaranteed minimum benefit options, term assurance, and endowment policies.

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