# Why do I get this difference when simulating geometric Brownian motion?

I tried simulating GBM using both the SDE definition and the closed form solution. The paths I get through these methods are very different. Can someone help me figure my mistake? import numpy as np
import matplotlib.pyplot as mp
import statsmodels.api as sm

time_step = 1e-6
N = 30000

np.random.seed(987654321)
A = np.zeros((N,1))
A = 2
B = np.zeros((N,1))
B = 2
volvol = 40.0

s = np.sqrt(time_step)

y = np.exp(-volvol**2.0/2 * time_step + volvol * s * np.random.normal(0, 1, N))
y1 = np.random.normal(0, s, N)

for i in np.arange(1, N, 1):
A[i] = A[i-1] * y[i]

for i in np.arange(1, N, 1):
print(B)
dB = volvol * B[i - 1] * y1[i]
B[i] = B[i-1]+ dB

mp.plot(A, label = 'exp')
mp.plot(B, label = 'SDE')

mp.legend(loc='lower right', ncol=1, fancybox=True, shadow=True, prop={'size': 6})
mp.grid()
mp.show()[![enter image description here]]


Here are the things you need to correct in your code:

Although you are setting a seed, you are generating the random numbers twice, and therefore they are not identical. Try this:

rand = np.random.randn(N)
y = np.exp(-volvol**2.0/2 * time_step + volvol * s * rand)
y1 = rand


You also need to multiply the sigma by the square root of the timestep in the SDE version :

dB = volvol * B[i-1] * y1[i] * s


That should be enough to get the same results.

Apart from that, your volatility is 4000%. The sigma should be 0.4 and not 40.0.