Leisen-Reimer in MATLAB

This tutorial presents MATLAB code that implements the Leisen-Reimer version of the binomial model as discussed in the Binomial model tutorial.

The code may be used to price vanilla European or American, Put or Call, options. Given appropriate input parameters a full lattice of prices for the underlying asset is calculated, and backwards induction is used to calculate an option price at each node in the lattice. Creating a full lattice is wasteful (of memory and computation time) when only the option price is required. However the code could easily be modified to show how the price evolves over time in which case the full lattices would be required.

Note that the primary purpose of the code is to show how to implement the Leisen-Reimer binomial model. The code contains no error checking and is not optimized for speed or memory use. As such it is not suitable for inclusion into a larger application without modifications.

A Pricing Example

Consider pricing a European Call option with the following parameters, X = $60, S0 = $50, r = 5%, σ = 0.2, Δt = 0.01, N = 100.

The Black-Scholes price for this option is $1.624.

A MATLAB function called binPriceLR is given below. The following shows an example of executing binPriceLR (and pricing the above option) in MATLAB,

>> oPrice = binPriceLR(60,50,0.05,0.2,0.01,100,'CALL',false)
oPrice =

If the number of time steps is doubled then

>> oPrice = binPriceLR(60,50,0.05,0.2,0.005,200,'CALL',false)
oPrice =

For this particular option the Leisen-Reimer approach implemented here performs quite badly in comparison to other available binomial techniques.

MATLAB Function: binPriceLR

function oPrice = binPriceLR(X,S0,r,sig,dt,steps,oType,earlyExercise)
% Function to calculate the price of a vanilla European or American
% Put or Call option using a Leisen-Reimer binomial tree.
% Inputs: X - strike
%       : S0 - stock price
%       : r - risk free interest rate
%       : sig - volatility
%       : dt - size of time steps
%       : steps - number of time steps to calculate
%       : oType - must be 'PUT' or 'CALL'.
%       : earlyExercise - true for American, false for European.
% Output: oPrice - the option price
% Notes: This code focuses on details of the implementation of the
%        Leisen-Reimer algorithm.
%        It does not contain any programatic essentials such as error
%        checking.
%        It does not allow for optional/default input arguments.
%        It is not optimized for memory efficiency or speed.

% Author: Phil Goddard (phil@goddardconsulting.ca)
% Date  : Q4, 2007

% Create a function handle for the required cummulative distribution
% approximation
fh = @(z,n)(0.5+sign(z)...

% Calculate the d1 and d2 parameters of the Black-Scholes model
T = dt*steps ; % time to maturity
d1 = (log(S0/X)+(r+sig*sig/2)*T)/sig/sqrt(T);
d2 = (log(S0/X)+(r-sig*sig/2)*T)/sig/sqrt(T);

% Calculate the Leisen-Reimer model parameters
% The parameter n must be odd
if mod(steps,2) > 0
    n = steps;
    n = steps+1;
pbar = fh(d1,n);
p = fh(d2,n);
u = exp(r*dt)*pbar/p;
d = (exp(r*dt)-p*u)/(1-p);

% Loop over each node and calculate the CRR underlying price tree
priceTree = nan(steps+1,steps+1);
priceTree(1,1) = S0;
for idx = 2:steps+1
    priceTree(1:idx-1,idx) = priceTree(1:idx-1,idx-1)*u;
    priceTree(idx,idx) = priceTree(idx-1,idx-1)*d;

% Calculate the value at expiry
valueTree = nan(size(priceTree));
switch oType
    case 'PUT'
        valueTree(:,end) = max(X-priceTree(:,end),0);
    case 'CALL'
        valueTree(:,end) = max(priceTree(:,end)-X,0);

% Loop backwards to get values at the earlier times
steps = size(priceTree,2)-1;
for idx = steps:-1:1
    valueTree(1:idx,idx) = ...
        exp(-r*dt)*(p*valueTree(1:idx,idx+1) ...
        + (1-p)*valueTree(2:idx+1,idx+1));
    if earlyExercise
        switch oType
            case 'PUT'
                valueTree(1:idx,idx) = ...
            case 'CALL'
                valueTree(1:idx,idx) = ...

% Output the option price
oPrice = valueTree(1);        

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