Used extensively in energy industry

Author : Andrew Patton
Quoted text on website:

This zip file contains a collection of Matlab functions that I wrote for my research on copulas for financial time series (Patton 2006a, Patton 2006b, Patton 2004, Granger et al. 2006, Patton 2007). Some simple example code is given in "copula_example_code.m". A table of contents is given in "contents.xls". Briefly, the toolbox contains CDFs, PDFs, log-likelihoods and random number generators for many common bivariate copulas, including the Clayton, Gumbel, Normal, Student's t, Frank, Plackett and symmetrised Joe-Clayton (SJC) copulas. Basic code for time-varying Normal, Gumbel and SJC copulas is included. Some helper functions are also included. If you find any bugs in this code please let me know: andrew.patton@economics.ox.ac.uk.

This file calculates lookback call option price using analytical formula given by Cozy and Viswanathan

(Please check the FAQ http://www.quantcode.com/modules/smartfaq/faq.php?faqid=72 for details of formula used)
This file demonstrates application of using importance sampling in Monte Carlo simulation for calculating call option price of a plain Vanilla European option. This is a variance reduction technique which is beneficial in scenario when there is very less probability of the occurrence of an event of interest. In our example scenario, we have a call option which is out of the money, so that most of the simulation outcomes done without importance sampling will result in zero payoff. Importance sampling comes to the rescue, by changing the drift so that most of the paths result in a positive payoff, but finally the resulting payoff is adjusted for the change of measure.

To run the code, simply download the 2 files , and run mcblack.m file in Octave or Matlab

Following is an example output after running the code:



As seen above, variance of importance sampling (variance_importance_sampling) is much less than the normal sampling (variance_normal_sampling).

From website:

It contains MATLAB codes that we created over many years. A subset of these codes have been used for the Springer book Financial Modeling Under Non-Gaussian Distributions, ISBN: 1-84628-419-8, written jointly with Ser-Huang Poon.

You may use these codes as you wish. Please, do not hold us responsible for any mistakes in the codes. If you find mistakes, please, send us a message either to Eric.Jondeau AT unil.ch or to Michael.Rockinger AT unil.ch. Replace AT with @.

We articulate the codes along several dimensions. Utility codes are short programs that made our life easier at one stage or another. Then, there are codes related to asset allocation, to option pricing (with an emphasis on extracting information out of options) and to the analysis of time series (estimation of all sorts of GARCH models).

Note that some codes may need some other codes, that can be found in this site, in order to run properly.

Further links that may be useful are to LeSage's econometrics toolbox. Again, please, do not hold us responsible for any problems that may arise out of using their toolboxes.