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.
Explore, Analyze, and Visualize Time-Series Data
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:
- Access data from multiple sources including files, spreadsheets, databases, data providers, and the Web
- Store data in financial time-series objects to simplify data management, data transformation, missing-data handling, and date-math calculations
- Perform technical analysis with moving averages, oscillators, stochastics, and indices
- Create custom analysis routines and visualizations or animations to capture, present, and share your analysis processes and discoveries
- Customize your analysis environment with advanced functionality from signal processing, statistics, or econometrics
Develop, Backtest, and Simulate Dynamic Models
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.
Perform Financial and Econometric Forecasting
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.