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Matlab Finance Code

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Matrix Decomposition

Matrix decomposition using, e.g. the Cholesky decomposition requires the correlation matrix to be positive definite. That is, the eigenvalues must all be positive. In finance, this is rarely the case, and one often observes negative eigenvalues, or zero eigenvalues. These two functions do essentially the same thing. One adjusts only the <= 0 eigenvalues, while the other adjusts those eigenvalues, but then also increases the other non-negative eigenvalues to compensate for the higher ‘weight’ given to the smaller eigenvalues.

Particle Swarm Optimization (PSO)

Using MATLAB to Develop Portfolio Optimization Models

A .zip file contains a series of scripts that were used in the MathWorks webinar “Using MATLAB to Develop Portfolio Optimization Models.” The scripts generate 3D efficient frontiers for a universe of 44 stocks with time as the third axis. Additional scripts perform various ex-ante and ex-post analyses. Results are generated with and without market adjustments in the data. A readme.txt. file in the .zip folder describes each script and how to use it.

Using MATLAB to Optimize Portfolios with Financial Toolbox

A .zip file contains a series of scripts that were used in the MathWorks webinar “Using MATLAB to Optimize Portfolios with Financial Toolbox.” The scripts demonstrate features of the Portfolio object and follows with case studies that demonstrate how to customize the tools for different tasks, including Sharpe/information ratio optimization and 130/30 portfolios. A readme.txt. file in the .zip folder describes how to use the scripts.

Analyzing Investment Strategies with CVaR Portfolio Optimization

Monte Carlo Simulation for Portfolio Assets

CVaR Portfolio Optimization

Genetic and Evolutionary Algorithm Toolbox for use with MATLAB

Mean-variance model with Var Cvar Constraint

Optimizing breakpoints for Tables

MATLAB code to support the “Generating Optimal Tables using MATLAB” webinar.

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