Financial professionals from more than 2300 organizations worldwide use MATLAB® to develop and implement financial models, analyze substantial volumes of data, and operate under tightening regulation. With MATLAB they measure and manage risk, construct portfolios, trade at low and high frequencies, value complex instruments, and construct asset-liability models.
Building commodity risk management platforms: analyzing multiple risk factors
A2A is one of the largest utility companies in Italy. The company’s risk management unit facilitates and monitors daily trading activities and supports longer-term strategy-setting. A2A’s risk management platform, built using MATLAB and related toolboxes, enables analysts to gather historical and current market data, apply sophisticated nonlinear models, rapidly calculate more than 500 risk factors, perform Monte Carlo simulations, and quantify value at risk (VaR). With MATLAB Compiler™ and MATLAB Builder™ NE, A2A deploys dashboard-driven analytics that help analysts and traders visualize results, manage risk, and record contract information.
Increasing model scale: monitoring transaction performance on an exchange
To reduce the market impact of large trades, institutional equity traders turn to alternative trading systems, such as Liquidnet, in which trades are executed anonymously. To measure execution performance, Liquidnet must compare the execution price of the traded equity with price trends preceding and following the transaction. Using MATLAB, Liquidnet built an automated system capable of analyzing all orders executed every day on the Liquidnet platform. The system compares transaction data with market trends and measures the performance of trades within a short-term time scale.
Nykredit Asset Management
Deploying reliable performance analytics: calculating and visualizing risk statistics
With a strong emphasis on balanced risk management, the Analytic Support Unit within Nykredit Asset Management provides quantitative support for the division’s fund managers and analysts. The Analytic Support Unit provides reports and tools that drive asset allocation, corporate bond profiling, performance metrics, and risk analyses. The unit uses MATLAB and related toolboxes to rapidly prototype algorithms, access information in databases and legacy C++ applications, visualize results, and implement operational dashboards that portfolio managers can use without formal training or knowledge of the underlying algorithms.
Complying with insurance regulations: determining solvency, assessing insurance risk, and identifying embedded value
Europe’s Solvency II and Own Risk Solvency and Assessment (ORSA) frameworks require insurers to model and price insurance contracts to the most granular level of detail. This requirement is particularly challenging in life insurance, where policies include many possible capital market relations and optionalities. Model IT used the MATLAB based mSII Toolbox to develop a contract-level simulation methodology to simplify the problem and minimize model risk. This methodology enabled a Finnish life insurer to develop a Pillar 1 Market Consistent Embedded Value (MCEV) and Solvency Capital Requirement (SCR) calculation environment and run authority reporting tests according to Eiopa’s Quantitative Impact Study QIS5 guidelines.
Designing modern forecasting and policy analysis systems: collaborating with economist peers
Many governments and their central banks base monetary policy on inflation targeting. Effective inflation targeting requires forecasting and policy analysis systems that model inflation, output, and other macroeconomic variables. Jaromir Benes at the International Monetary Fund (IMF) employs MATLAB and the MATLAB based, open-source IRIS Toolbox to perform dynamic stochastic general equilibrium (DSGE) modeling, multivariate time-series and macroeconomic time-series management, and PDF and LaTeX-based reporting. Economists can use these same tools to investigate components of macroeconomic and systemic risk analysis; for example, they can assess the properties of stochastic economic models, build policy scenarios, and condition model simulations upon judgmental adjustment.
Building robust corporate development tools: providing analytics to thousands of users
Banc Sabadell’s Quantitative Tools team develops and maintains a library of interest rate, commodities, foreign exchange, inflation hedging, and investment analytics. The team manages more than 80,000 lines of code in the library through Subversion source control. With MATLAB Compiler and MATLAB Builder JA, the team rapidly deploys analytics, tools, and market information such as trading ideas and market parameters to more than 2000 users, through web interfaces, databases, and Microsoft® Excel®. In one project, the analyst defines a payoff structure through a web form, which is then priced using Monte Carlo methods. Execution speed for performance-critical applications is optimized using parallel and grid computing.