This is the code for the book
"Financial Modelling, Theory, Implementation and Practice (with Matlab Source)" by Kienitz and Wetterau.
This book shows how to cope with the usage and the implementation of models for derivatives pricing, asset allocation and hedging.
We cover Non-Gaussian (StochVol, Levy, StochVolLevy, LV) using state-of-the art Transformation methods, Monte Carlo and we give calibration algos.

The corresponding book can be found here:
http://www.amazon.co.uk/Financial-Modelling-Implementation-Practice-Finance/dp/0470744898/ref=sr_1_1?ie=UTF8&qid=1351061102&sr=8-1

%Example: Note requires the financial toolbox function blsprice
S=50; K=50; sigma=0.4; r=0.1; T=1; steps=200;
a=[]; b=[];
counter=0;
for k=5:5:steps
counter=counter+1;
a(counter)=TreeMy(S,K,sigma,r,T,k);
b(counter)=blsprice(S,K,r,T,sigma);
end
k=5:5:steps;
plot(k,a,'b');
hold on
plot (k,b,':r');
hold off
title('Option price as a function of the number of steps');
ylabel('Option price');
xlabel('Number of steps, n');

Consider a European knock out call option with a barrier Sb. This is an option that seizes
to exist when the price of the underlying asset hits a predetermined barrier during the entire
life of the contract. The function KOCallTreeMy returns the value of a knock-out European
call option using a binomial lattice:

Example:
KOCallTreeMy(50,55,0.2,0.1,1,200,60)
ans =
0.0883

Just happened to come across this link, thought it will be useful example code for pricing barrier options and using variance reduction techniques
I can't find his name, only his ID is there

This is a tutorial code which demonstrates comparison of the below 3 methods to find delta of a plain vanilla call option using monte carlo simulations:
1. Finite difference using forward difference scheme
2. Finite difference using crank nicholson scheme
3. Pathwise derivative method suggestd by formula in Glasserman's paper

Following is an output of the reslut:


Points to note from the results:
- avg_finitediff_delta_Fwd is biased higher, which matches with the explanation
- crank nicholson gives better values than forward difference scheme, since it is closer to analytical value
- variance of delta calculated using crank nicholson increases when FD step size (h) is decreased

PS : Following file is needed to compare analytical result:
http://www.quantcode.com/modules/docmanager/view_file.php?curent_file=407&curent_dir=19