Pricing_Library / Models / Heston / callPrice_FFT_Heston.m
callPrice_FFT_Heston.m
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function [P_i] = callPrice_FFT_Heston(S0, T, r, K_i, PARAMS)

% Price of a Plain Vanilla Call exploiting the Carr-Madan algorithm
% Model: Heston

%INPUT:
% K_i    : strikes (could be a vector)
%
%OUTPUT:
% P_i    : prices (could be a vector)
%

%% Parameters

% discretization parameters
Npow = 16;
N    = 2^Npow; % grid point
A    = 2000;   % upper bound


%% Computations

% 0. FFT grid ───────────────────────────────────────────────────────────────────────

% integral grid (for dv) to compute integral numerically as a summation
eta  = A/N;
v    = eta*(0:N-1);
v(1) = 1e-22; % correction term
% Note: it cannot be equal to zero, otherwise we get NaN

% logstrike grid, then compute summation via FFT
lambda = 2 * pi/(N * eta); 
k      = - lambda * N/2 + lambda * (0:N-1);
K      = S0 * exp(k); % strike 


% 1. Find the Fourier transform of Z(k) using the Carr-Madan closed formula ─────────

% Fourier transform of z_k
tr_fou = fourier_transform(r, PARAMS, T, v);


% 2. Compute the prices computing the integral using trapezoidal rule ───────────────

% trapezoidal rule
w = [0.5  ones(1, N-2)  0.5];
h = exp( 1i * (0:N-1) * pi ) .* tr_fou .* w * eta;
P = S0 * real( fft(h) / pi + max( 1 - exp( k - r * T ), 0 ) ); % prices


% 3. Compute the call price ─────────────────────────────────────────────────────────

% focus on the grid of prices by deleting too small and too big strikes
index = find( ( K > 0.1 * S0 & K < 3 * S0 ) );
K = K(index);
P = P(index);

% find the option price interpolating on the pricing grid
P_i = interp1(K, P, K_i, 'spline');


end


% >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>


function fii = fourier_transform(r, PARAMS, T, v)

fii = ( exp( 1i * r * v * T) ) .* ( ( characteristic_func(PARAMS, T, v - 1i ) - 1 ) ./ ( 1i * v .* ( 1 + 1i * v ) ) );


end


% >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>


function f = characteristic_func(PARAMS, T, u)

% model parameters 
epsilon = PARAMS(1); % vol-of-vol
kappa   = PARAMS(2); % mean reversion speed
rho     = PARAMS(3); % correlation
theta   = PARAMS(4); % mean 
V0      = PARAMS(5);
    
alfa     = - 0.5 * ( u .* u + u * 1i );
beta     = kappa - rho * epsilon * u * 1i;
epsilon2 = epsilon * epsilon;
gamma    = 0.5 * epsilon2;

D = sqrt( beta .* beta - 4.0 * alfa .* gamma );

bD  = beta - D;
eDt = exp( - D * T );

G   = bD ./ ( beta + D );
B   = ( bD ./ epsilon2 ) .* ( ( 1.0 - eDt )./( 1.0 - G .* eDt ) );
psi = ( G .* eDt - 1.0 )./( G - 1.0 );
A   = ( ( kappa * theta )/( epsilon2 ) ) * ( bD * T - 2.0 * log(psi) );

y = A + B * V0;

f = exp( y );


end