Risk Management

MATLAB® helps financial organizations develop quality-assured, transparent, documented, and replicable risk and stress testing models in days, not years. MATLAB offers agility amidst rapidly changing regulatory and business environments.

With MATLAB, risk analysts and managers work with developers, integrators, stakeholders, and CROs to blend, scale, and customize research. You can incorporate “risk-aware” developer best practices when implementing model control and automation, reducing model and operational risk.

Using MATLAB, a single risk model stack can service multiple compliance regimes and front and middle office functions. On a smaller scale, you can customize, control, and automate market, credit, economic capital, and systemic risk models. You can operate alongside or validate existing vendor models, home-grown code, and spreadsheets.

Financial Risk Management: Improving Model Governance with MATLAB

A Practical Guide: Modeling Financial Risk with MATLAB

Accelerating Model Development Across Risk-Aware Institutions

Risk Modeling

MATLAB is used across industries for research and modeling. Banks, asset managers, supervisors, and insurers use MATLAB in:

  • Capital requirements regimes such as Basel III
  • Stress test infrastructures such as CCAR and DFAST
  • Accounting regulations such as IFRS 9 and CECL and trade reconciliation requirements such as MiFID
  • Market risk, credit risk, operational risk, and compliance and fraud monitoring

Building Models. Mathematicians, quants, data scientists, and others use MATLAB to perform risk calculations that are faster than spreadsheets. They create models more quickly than in C++, with greater transparency and customization than black box products, and with greater quality and consistency than open source applications.

Pre-built, tested functions facilitate:

  • Estimating and back-testing value at risk, conditional value-at-risk, and other key risk measures
  • Applying factor analysis, optimization, and machine learning methods to inform risk parity, “smart beta,” and other risk-informed factor-based strategies
  • Calculating risk metrics such as exposure, loss-given defaults, probability of default, loss distributions, and recovery
  • Aggregating risks and ensuring reliable key risk indicators [KRIs] through clean, join, and transformation functions; managing missing data; and calculating derived data
  • Generating real-world and statistical scenarios through copulas, curves, option pricing, and data import functions

"[MATLAB] is not as complex to code as C++. It is not as restrictive in terms of applications as Excel.... For communicating the idea — selling the idea — MATLAB has helped a lot.... I can create visuals very quickly."

Attilio Meucci

Risk-Aware Development

Application developers at financial institutions find that MATLAB makes it easier to incorporate new and updated models directly into professional risk applications. It is easier for IT teams to maintain and troubleshoot components. They can:

  • Ensure quality and accuracy by incorporating unit and performance tests automatically
  • Accommodate functional and non-functional design requirements
  • Modularize models with object oriented programming (e.g., cleaning data from multiple sources), or manage and replicate model methods across risk categories and asset classes
  • Manage code with source control systems such as Git and Subversion
  • Scale and add compute power through parallel computing on clusters, GPUs, and in the cloud
  • Accommodate preferred company development styles and philosophies (e.g., “Lean” or “Agile” methodologies)
  • Reduce technical debt

MATLAB helps risk teams easily incorporate their models and analytics into development projects by providing capabilities such as Object-Oriented Programming and the ability to package and share applications.

MATLAB and Simulink Consulting Services 

MathWorks Consulting Services works with you to produce robust, scalable, flexible, and reusable applications.

Enterprise Implementation

MATLAB enables risk professionals and integrators to share a risk analytic or model from a single risk analytics stack through extensible interfaces that include connections to C++, Java, .NET, Python, SQL, and web services. Risk teams share, control, and manage the use of their models as a standalone or spreadsheet application; as an automatically generated professional watermarked report embedded in the risk production stack; or as a component of a web application, database, visualization application, or third-party risk system.