Thu Oct 6 19:48:48 2022
ornstein_uhlenbeck_test():
Python version: 3.6.9
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 = 2.0
Using mean MU = 1.0
Using variance SIGMA = 0.15
Using initial value X0 = 2.0
Using final time TMAX = 3.0
Using number of timesteps N = 10000
ornstein_uhlenbeck_euler():
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.
Graphics 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 = 2.0
Using mean MU = 1.0
Using variance SIGMA = 0.15
Using initial value X0 = 2.0
Using final time TMAX = 3.0
Using number of large timesteps N = 10000
Using R = 16 small time steps per one large time step
ornstein_uhlenbeck_euler_maruyama():
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.
Graphics saved as "ornstein_uhlenbeck_maruyama.png"
ornstein_uhlenbeck_test():
Normal end of execution.
Thu Oct 6 19:48:49 2022