Assignment 9

Lorenzo Biasi and Michael Aichmüller

Exercise 1.



In [1]:

    
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

def figure_label():
    plt.xlabel('time')
    plt.ylabel('x')



In [2]:

    
def brownian_motion(T, N):
    z = np.hstack(([0], np.random.randn(N - 1)))
    return np.cumsum(z * np.sqrt(T / N))

T, N, n = 1, 10001, 100
t = np.linspace(0, T, N)
motions = np.zeros((n, N))
for i in range(n):
    motions[i, :] = brownian_motion(T, N)

plt.title('mean and variance')
plt.plot(t, np.mean(motions, axis=0))
plt.plot(t, np.var(motions, axis=0))
figure_label()

Exercise 2.



In [3]:

    
def geometric_brownian_motion(T, N, sigma, r, x):
    t = np.linspace(0, T, N)
    W = brownian_motion(T, N)
    return x * np.exp((r + sigma ** 2 / 2) * t + sigma * W)

sigma, r, x = 2, 4, 2

for i in range(n):
    motions[i, :] = geometric_brownian_motion(T, N, sigma, r, x)
    
plt.title('Mean')
plt.plot(t, np.mean(motions, axis=0)), figure_label()
plt.figure()
plt.title('Variance')
plt.plot(t, np.var(motions, axis=0)), figure_label()









    Out[3]:





([<matplotlib.lines.Line2D at 0x7f1f102f72e8>], None)

Exercise 3.



In [4]:

    
def geometric_brownian_motion(T, N, t_0, x, y):
    t = np.linspace(0, T, N)
    W = brownian_motion(T, N)
    return x + W - (t - t_0) / (T - t_0) * (W[-1] - y + x)


t_0, x, y = 0, -1, 2
t = np.linspace(0, T, N)
motions = np.zeros((n, N))
for i in range(n):
    motions[i, :] = geometric_brownian_motion(T, N, t_0, x, y)

    
plt.title('Mean')
plt.plot(t, np.mean(motions, axis=0)), figure_label()
plt.figure()
plt.title('Variance')
plt.plot(t, np.var(motions, axis=0)), figure_label()









    Out[4]:





([<matplotlib.lines.Line2D at 0x7f1f128a0588>], None)