Matlab Finance Code

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Energy Trading & Risk Management with MATLAB Webinar Case Study

MATLAB code for the generation asset risk analysis case study

Improving MATLAB® performance when solving financial optimization problems

Optimization algorithms are commonly used in the financial industry with examples including Markowitz portfolio optimization, Asset-Liability management, credit-risk management, volatility surface estimation etc. Many optimization problems involve nonlinear objective functions and constraints. These problems can be computationally expensive, especially with numerically estimated gradients. We have seen many cases where optimizations were sped up by incorporating pre-computed analytical derivatives.

GARCH,EGARCH,NAGARCH,GJR models and implicit VIX

Estimate GARCH/EGARCH/NAGARCH/GJR parameters from a time series of prices , rates and VIX  value.

FBD – “Find the Best Distribution” tool

With this GUI you can find the best distribution that fits your data.

Portfolio Optimization

Risk management and analysis using automatically downloaded data from the internet. Analysis can be made for a combination of stocks, bonds, derivatives and any other financial instruments.

Modeling Variable Annuities with MATLAB

This demo shows how to price variable annuity product (Guaranteed Minimum Withdrawal Benefit)

Modeling Variable Annuities with MATLAB

Pricing Guaranteed Minimum Withdrawal Benefit

Cointegration and Pairs Trading with Econometrics Toolbox

Files used in the April 14, 2011 webinar titled Cointegration and Pairs Trading with Econometrics Toolbox.

It is recomended that you watch the recording of the webinar: http://www.mathworks.com/wbnr55450

Approaches to implementing Monte Carlo methods in MATLAB

Monte Carlo methods have long been used in computational finance to solve problems where analytical solutions are not feasible or are difficult to formulate. However, these methods are computationally intensive making it challenging to implement and adopt. In the last decade, advances in hardware, increasing processor speeds and decreasing costs have made it easier to adopt Monte Carlo methods to solve numerically intensive problems. With growing access to data and demand for quicker results, researchers are constantly looking for better ways to implement algorithms using Monte Carlo methods.

In the Wilmott Magazine September 2011 article(http://www.wilmott.com/magazine.cfm), we will share some of our observations and demonstrate various ways MATLAB could be used to implement Monte Carlo methods. We take a case study of pricing Asian options and show various approaches to implementing them in MATLAB.

A draft version of the article is included in this submission.


User Interface for fitting and evaluating a generic GARCH model using the Econometrics Toolbox.

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