from Model.QgyModel import *
qgy = QgyModel()
qgy.n_per_year = 10
N = qgy.n_per_year * (qgy.n - 1)
sigma1 = np.repeat(1, N)
sigma2 = np.repeat(2, N)
rho = -0.1
dt = 1 / qgy.n_per_year
num_sim = 10000
res1 = []
res2 = []
for i in range(num_sim):
[x_n, x_y1] = qgy.generate_two_correlated_gauss(sigma1, sigma2, rho, N, dt)
res1.append(x_n[-1])
res2.append(x_y1[-1])
corr = np.corrcoef(res1, res2)
print("corr = ", corr)
from Model.QgyModel import *
test = [10, 20, 40, 80, 160, 320]
num_sim = 10000
for num_per_year in test:
dt = 1 / num_per_year
qgy = QgyModel()
qgy.n_per_year = num_per_year
N = num_per_year * (qgy.n - 1)
sigma = np.repeat(1, N)
res = []
for i in range(num_sim):
one_path = qgy.generate_one_gauss(sigma, N, dt)
res.append(one_path[-1])
#plt.plot(one_path, 'g-')
#plt.show()
var = np.var(res)
mean = np.mean(res)
print("num_per_year = ", num_per_year, "mean = ", mean, ", var = ", var)
from Model.QgyModel import *
from scipy import stats
import seaborn as sns
qgy = QgyModel()
qgy.n_per_year = 500
N = qgy.n_per_year * (qgy.n - 1)
sigma2 = []
sigma2_prime = []
dt = 1 / qgy.n_per_year
for i in range(1, len(qgy.R_Tk_y)):
for j in range(qgy.n_per_year):
t = qgy.Tk[i - 1] + dt * (j + 1)
sigma2.append(qgy.inf_vol(t))
sigma2_prime.append(qgy.inf_vol_prime(t))
sigma_n = np.repeat(1, N)
sigma_n_prime = np.repeat(0, N)
t = np.linspace(1, qgy.Tk[-1], qgy.n_per_year * (qgy.n - 1))
dist = []
N = 100
for i in range(0, N):
[x_n,
x_y1] = qgy.generate_two_correlated_gauss(sigma_n, sigma2, qgy.rho_n_y1,
(qgy.n - 1) * qgy.n_per_year,
1 / qgy.n_per_year,
sigma_n_prime, sigma2_prime)
x_y2 = qgy.generate_one_gauss(sigma2, (qgy.n - 1) * qgy.n_per_year,
1 / qgy.n_per_year, sigma2_prime)
x_Tk_y1 = x_y1[::qgy.n_per_year]