def test_mc_analy_comparison():
qgy_md = qgy.QgyModel()
num_sim = 10000
n_per_year = 50
EtXT_analy = compute_expectation_analytic(qgy_md)
EtXT_mc = compute_expectation_mc(qgy_md, num_sim, n_per_year)
plt.plot(qgy_md.Tk, EtXT_analy, 'r--')
plt.plot(qgy_md.Tk, EtXT_mc, 'b-o')
plt.show()
def test_convergence():
qgy_md = qgy.QgyModel()
num_sim = 100
n_per_year = 100
EtXT_analy = compute_expectation_analytic(qgy_md)
for i in range(1, 10):
current_num = num_sim * i * 2
EtXT_mc = compute_expectation_mc(qgy_md, current_num, n_per_year)
RMSE = np.sqrt(np.sum((EtXT_analy - EtXT_mc)**2))
print("sim_num = ", current_num, "err = ", RMSE, "N * err = ",
current_num * RMSE, "sqrt(N) * err = ",
np.sqrt(current_num) * RMSE)
from Model.QgyModel import * qgy = QgyModel() ytT = qgy.price_yoy_infln_fwd() I0_Tk_corr = qgy.fit_yoy_convexity_correction(qgy.I0_Tk) plt.subplot(1, 2, 1) plt.plot(qgy.Tk[1:], -0.00012662 * np.log(qgy.Tk[1:]), '-') plt.plot(qgy.Tk[1:], ytT[1:] - (qgy.I0_Tk[1:] / qgy.I0_Tk[:-1] - 1), 'or') plt.subplot(1, 2, 2) plt.plot(qgy.Tk[1:], ytT[1:] - (I0_Tk_corr[1:] / I0_Tk_corr[:-1] - 1), 'or') print(qgy.I0_Tk) print(I0_Tk_corr) plt.show()
import Model.QgyModel as qgy
import matplotlib.pyplot as plt
import numpy as np
qgy = qgy.QgyModel()
qgy.n_per_year = 50
num_path = 10000
P_Tk = qgy.P_0T(qgy.Tk)
Sigma_Tk_y = 0.045
v_Tk_y = 0.8
rho_Tk_y = -0.5
rho_t_ny1 = -0.1
#R_Tk = np.array([0, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]) * 0.01
R_Tk = np.array([0, 1, 2, 4, 8, 16, 32, 64, 128, 256]) * 0.01
count = 0
for R in R_Tk:
mc_res = np.zeros(len(qgy.Tk))
qgy.fill_spherical_parameters(Sigma_Tk_y, v_Tk_y, rho_Tk_y, rho_t_ny1, R)
for k in range(num_path):
qgy.generate_terms_structure()
disc = qgy.D_t
yoy = qgy.Y_Tk
disc_price = yoy * disc
mc_res += disc_price
#plt.plot(qgy.Tk, disc_price/P_Tk - 1, 'g-')
avg_price = mc_res/num_path
count += 1
plt.plot(qgy.Tk, avg_price/P_Tk - 1, color=[0, count/len(R_Tk), 0])