Financial engineers, analysts, and economists worldwide use MathWorks time-series analysis capabilities to forecast market volatility, analyze correlation in data series, test hypotheses about market dynamics, and build models for further analysis or simulation of future outcomes.
MATLAB and related computational finance products let you access, visualize, and analyze historic and live time-series data to identify patterns or uncover complex relationships. From within a single environment you can:
You can build and estimate parameters for your models by using traditional techniques, such as solving systems of ordinary differential equations, performing multivariate regression, or using optimization techniques for fitting models to time-series data. Alternatively, you can apply specialized modeling techniques such as ARMAX/GARCH, VAR/VARMA, and linear or nonlinear stochastic differential equations.
MATLAB lets you mix and match different techniques and approaches to develop models that incorporate market dynamics. You can combine continuous and discrete time approaches with random/discrete events. These types of models are used to simulate trading systems that account for exchange trading curbs or describe macroeconomic systems containing random events.
You can forecast market trends to make budgeting, planning, investing, and policy decisions. Financial Toolbox™ provides the foundation for working with financial time-series data; regression and parameter estimation with or without missing data; and simulating different scenarios to estimate risk. Econometrics Toolbox™ extends Financial Toolbox with advanced capabilities that account for nonuniform variance across time or perform Monte Carlo simulation of stochastic systems.