28-Jul-2021 20:35:40 ornstein_uhlenbeck_test(): MATLAB/Octave version 9.9.0.1467703 (R2020b) Test ornstein_uhlenbeck. ornstein_uhlenbeck_euler_test(): Estimate a solution to the Ornstein-Uhlenbeck equation using the Euler method for stochastic differential equations. Using decay rate THETA = 2 Using mean MU = 1 Using variance SIGMA = 0.15 Using initial value X0 = 2 Using final time TMAX = 3 Using number of timesteps N = 10000 Using value of random SEED = 123456789 ornstein_uhlenbeck_euler: MATLAB/Octave version 9.9.0.1467703 (R2020b) Use an Euler method to approximate the solution of the Ornstein-Uhlenbeck stochastic differential equation: d x(t) = theta * ( mu - x(t) ) dt + sigma dW with initial condition x(0) = x0. Plot saved as "ornstein_uhlenbeck_euler.png" ornstein_uhlenbeck_euler_maruyama_test(): Estimate a solution to the Ornstein-Uhlenbeck equation using the Euler-Maruyama method for stochastic differential equations. Using decay rate THETA = 2 Using mean MU = 1 Using variance SIGMA = 0.15 Using initial value X0 = 2 Using final time TMAX = 3 Using number of large timesteps N = 10000 Using R = 16 small time steps per one large time step Using value of random SEED = 123456789 ornstein_uhlenbeck_euler_maruyama: MATLAB/Octave version 9.9.0.1467703 (R2020b) Use an Euler-Maruyama method to approximate the solution of the Ornstein-Uhlenbeck stochastic differential equation: d x(t) = theta * ( mu - x(t) ) dt + sigma dW with initial condition x(0) = x0. Plot saved as "ornstein_uhlenbeck_euler_maruyama.png" ornstein_uhlenbeck_test(): Normal end of execution. 28-Jul-2021 20:35:45