ornstein_uhlenbeck_test
22-Feb-2019 20:30:39

ornstein_uhlenbeck_test:
  MATLAB version.
  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 version
  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 version
  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.

22-Feb-2019 20:30:42
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