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Approaches to implementing Monte Carlo methods in MATLAB

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Approaches to implementing Monte Carlo methods in MATLAB


sri (view profile)


28 Sep 2011 (Updated )

Code for the article in the September 2011 article

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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(, 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.

Required Products Econometrics Toolbox
Financial Derivatives Toolbox
Financial Toolbox
Parallel Computing Toolbox
Statistics and Machine Learning Toolbox
MATLAB release MATLAB 7.12 (R2011a)
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27 Aug 2012 Ryan G

Ryan G (view profile)

Monte carlo methods can be complicated for the uninitiated. MathWorks has compiled a number of resources to help make them easier to implement and speed-up at the following link:

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14 Oct 2011 1.1

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01 Sep 2016

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