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]