MathWorks tools let you use Monte Carlo methods to model and simulate complex financial systems and analyze how uncertainty influences them much faster than using spreadsheets or traditional programming languages such as C++ or Visual Basic.
You can develop models that capture detailed information about unlikely or worst-case scenarios or obtain approximate solutions to problem that are otherwise intractable or time-consuming to analyze with traditional analytical techniques. Supported capabilities include a wide range of random and quasi-random number generators, parallel computing enabled random number generators, Markov Chain Monte Carlo simulation, and simulation of stochastic differential equations. Financial engineers and actuarial scientists use these capabilities for:
The breadth and depth of functionality available in MATLAB and related products simplifies the development of Monte Carlo–driven risk metrics used for quantifying credit risk, market risk, liquidity risk, investment risk, or operational risk. You can evaluate risk using standard methods such as value at risk (VaR) and extreme value theory (EVT), or develop customized risk measurement and management metrics for individual and firm-wide activities.
Monte Carlo simulations are often complex and computationally intensive. MathWorks parallel computing tools reduce simulation times to a fraction of what they would be on a single computational processor. By using additional hardware, you can distribute your Monte Carlo simulations across multiple processors with little modification to your algorithms or code.