Financial Toolbox™ provides functions for the mathematical modeling and statistical analysis of financial data. You can analyze, backtest, and optimize investment portfolios taking into account turnover, transaction costs, semi-continuous constraints, and minimum or maximum number of assets. The toolbox enables you to estimate risk, model credit scorecards, analyze yield curves, price fixed-income instruments and European options, and measure investment performance.
Stochastic differential equation (SDE) tools let you model and simulate a variety of stochastic processes. Time series analysis functions let you perform transformations or regressions with missing data and convert between different trading calendars and day-count conventions.
Matrices, matrix functions, and matrix algebra are the most efficient ways to analyze sets of numbers.
Matrix algebra you learned in school but may have forgotten.
Inputs and outputs for Financial Toolbox functions.
Introduction to Computational Finance with MATLAB: A Risk Management Example (44 min 00 sec)
Getting Started with Portfolio Optimization (4 min 12 sec)
Using Tables for Financial Data (5 min 33 sec)
Parallel Computing with MATLAB in Computational Finance (51 min 53 sec)
MATLAB for R Users in Computational Finance (55 min 12 sec)
Optimization in MATLAB for Financial Applications (63 min 00 sec)
Machine Learning for Algorithmic Trading (32 min 55 sec)
Equity Trading with MATLAB and FactSet (69 min 09 sec)
Modeling Equity-Indexed Annuities with MATLAB (52 min 26 sec)
Pricing and Analysis of an Insurance Contract (34 min 09 sec)