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sigma**2) * (t[i] - t[i-1]) Z = np.random.normal(0., 1., dim) diffusion = np.matmul(A, Z) * (np.sqrt(t[i] - t[i-1])) S[:, i] = S[:, i-1]*np.exp(drift + diffusion) return S, t An Alternative To GBM — Bootstrap Sampling Source :