I created the Matlab code below to implement the value of an European Put following the implementation inside this paper. I am trying to graph the value of M against the value of the European Put as M increases from 20 to 250 in time steps of 5.
In order to do this, I created a for loop to change the value of M,
for M = 20:5:250
I think that I need to create this for loop in order to change the value of M. Unit testing shows that I did something wrong. The for loop isn't working as intended. The graph produced by the code is referencing the original value of M (defined to be 200) instead of the changing values of M inside the for loop. I don't know why the code returns the original value of M instead of the values inside the for loop.
clear all;
close all;
% EURO9 Binomial method for a European put.
%
% Uses explicit solution based on binomial expansion.
% Vectorized, based on logs to avoid overflow,
% and avoids computing with zeros.
%%%%%%%%%% Problem and method parameters %%%%%%%%%%%%%
S = 9 ;E = 10 ;T = 3 ;r = 0.06 ;sigma = 0.3 ; M = 200;
dt = T/M ; A = 0.5*(exp(-r*dt)+exp((r+sigma^2)*dt)) ;
u= A + sqrt(A^2-1) ; d = 1/u ; p = (exp(r*dt)-d)/(u-d) ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% cut-off index
for M = 20:5:250
z = max(1,min(M+1,floor( log((E*u)/(S*d^(M+1)))/(log(u/d)) )));
% Option values at time T
W=E-S*d.^([M:-1:M-z+1]').*u.^([0:z-1]');
% log/cumsum version using cut-off index z
tmp1 = cumsum(log([1;[M:-1:M-z+2]'])) - cumsum(log([1;[1:z-1]']));
tmp2 = tmp1 + log(p)*([0:z-1]') + log(1-p)*([M:-1:M-z+1]');
value = exp(-r*T)*sum(exp(tmp2).*W);
disp('M is'), disp(M)
disp('Option value is'), disp(value)
hold on;
xlabel('M') % x-axis label
ylabel('European Put') % y-axis label
plot(M,value,'r*')
end
Unit testing shows that my code is wrong. Testing against M=20 returns a value less than one when the true value is 1.5076.
Did I write the for loop completely wrong? Why is it referencing the value of M=200 at every iteration instead of the increment specified in the for loop for M = 20:5:250?
As an example, running
clear all;
close all;
% EURO9 Binomial method for a European put.
%
% Uses explicit solution based on binomial expansion.
% Vectorized, based on logs to avoid overflow,
% and avoids computing with zeros.
%%%%%%%%%% Problem and method parameters %%%%%%%%%%%%%
S = 9 ;E = 10 ;T = 3 ;r = 0.06 ;sigma = 0.3 ; M = 20;
dt = T/M ; A = 0.5*(exp(-r*dt)+exp((r+sigma^2)*dt)) ;
u= A + sqrt(A^2-1) ; d = 1/u ; p = (exp(r*dt)-d)/(u-d) ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% cut-off index
z = max(1,min(M+1,floor( log((E*u)/(S*d^(M+1)))/(log(u/d)) )));
% Option values at time T
W=E-S*d.^([M:-1:M-z+1]').*u.^([0:z-1]');
% log/cumsum version using cut-off index z
tmp1 = cumsum(log([1;[M:-1:M-z+2]'])) - cumsum(log([1;[1:z-1]']));
tmp2 = tmp1 + log(p)*([0:z-1]') + log(1-p)*([M:-1:M-z+1]');
value = exp(-r*T)*sum(exp(tmp2).*W);
disp('M is'), disp(M)
disp('Option value is'), disp(value)
returns
Option value is
1.5076
and running
clear all;
close all;
% EURO9 Binomial method for a European put.
%
% Uses explicit solution based on binomial expansion.
% Vectorized, based on logs to avoid overflow,
% and avoids computing with zeros.
%%%%%%%%%% Problem and method parameters %%%%%%%%%%%%%
S = 9 ;E = 10 ;T = 3 ;r = 0.06 ;sigma = 0.3 ; M = 25;
dt = T/M ; A = 0.5*(exp(-r*dt)+exp((r+sigma^2)*dt)) ;
u= A + sqrt(A^2-1) ; d = 1/u ; p = (exp(r*dt)-d)/(u-d) ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% cut-off index
z = max(1,min(M+1,floor( log((E*u)/(S*d^(M+1)))/(log(u/d)) )));
% Option values at time T
W=E-S*d.^([M:-1:M-z+1]').*u.^([0:z-1]');
% log/cumsum version using cut-off index z
tmp1 = cumsum(log([1;[M:-1:M-z+2]'])) - cumsum(log([1;[1:z-1]']));
tmp2 = tmp1 + log(p)*([0:z-1]') + log(1-p)*([M:-1:M-z+1]');
value = exp(-r*T)*sum(exp(tmp2).*W);
disp('M is'), disp(M)
disp('Option value is'), disp(value)
returns
Option value is
1.4666
Although, I don't get these values in the graph of the for loop for M = 20:5:250. I must have made a mistake inside the for loop.