Monte Carlo Algorithm

Monte_Carlo.py

#
# Monte Carlo valuation of European call options with NumPy (log version)
# Monte_Carlo.py
#
import math
from numpy import *
from time import time
# star import for shorter code
random.seed(20000)
t0 = time()
# Parameters
S0 = 100.; K = 105.; T = 1.0; r = 0.05; sigma = 0.2
M = 50; dt = T / M; I = 250000
# Simulating I paths with M time steps
S = S0 * exp(cumsum((r - 0.5 * sigma ** 2) * dt
+ sigma * math.sqrt(dt)
* random.standard_normal((M + 1, I)), axis=0))
# sum instead of cumsum would also do
# if only the final values are of interest
S[0] = S0
# Calculating the Monte Carlo estimator
C0 = math.exp(-r * T) * sum(maximum(S[-1] - K, 0)) / I
# Results output
tnp2 = time() - t0

print('The European Option Value is: ', C0)  # The European Option Value is:  8.165807966259603
print('The Execution Time is: ',tnp2)  # The Execution Time is:  0.9024488925933838