Risk Management

• Financial Services Overview
• Investment Management
• Risk Management
• Insurance and Actuarial Science
• Econometrics and Economics
• Algorithmic Trading
• Pricing and Valuation

Risk managers and analysts rely on MATLAB to:

• Estimate measures such as value at risk
• Calculate credit risk and counterparty risk metrics such as loss-given defaults, probabilities of default, and loss distributions
• Determine operational risk
• build comprehensive financial risk management tools

The resulting parameters from these activities are used to rebalance portfolios, allocate economic capital, enable valuations (for example, credit valuation adjustment), and facilitate hedging and trading.

Using MATLAB, organizations build an integrated workflow from research to testing and deployment that helps protect balance sheets and satisfy the diverse requirements of regulators, governments, customers, and shareholders.

Develop Robust Risk Applications

Teams develop and test applications in MATLAB that meet new requirements for base-case conditions and extensive what-if scenarios. MATLAB provides a flexible environment that combines numerical accuracy and algorithm traceability with extensive reference documentation.

Visualize Results with Intuitive Graphics

Using MATLAB visualization capabilities and interactive plots, risk-management departments communicate a quantitative and intuitive description of risk model behavior that is accessible to mathematicians and nonmathematicians alike.

Rapidly Deploy Financial Applications Without Recoding

Using MATLAB and related toolboxes, developers deploy standalone applications or software components that integrate with C and C++, .NET, Excel, and Java based applications. Deploying standalone executables and Web components helps reduce the risk of user error and tampering, common in spreadsheet applications. By leveraging multicore computers and grids developers can further accelerate application performance.