29 October   2023  11:18:39.799 AM      
 
ornstein_uhlenbeck_test():
  FORTRAN77 version.
  Test ornstein_uhlenbeck().

ornstein_uhlenbeck_euiler_test():
  Estimate a solution to the Ornstein-Uhlenbeck equation with
  the Euler method for stochastic differential equations.

  Using decay rate THETA =    2.00000    
  Using mean MU =    1.00000    
  Using variance SIGMA =   0.150000    
  Using initial value X0 =    2.00000    
  Using final time TMAX =    3.00000    
  Using number of timesteps N =    10000
  Using value of random SEED =    123456789

ornstein_uhlenbeck_euler():
  FORTRAN77 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.
  Created data file "ornstein_uhlenbeck_euler_data.txt".
  Created command file "ornstein_uhlenbeck_euler_commands.txt".

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 =    2.00000    
  Using mean MU =    1.00000    
  Using variance SIGMA =   0.150000    
  Using initial value X0 =    2.00000    
  Using final time TMAX =    3.00000    
  Using number of large timesteps N =  10000
  Using small time steps per one large time step R =     16
  Using value of random SEED =    123456789

ornstein_uhlenbeck_euler_maruyama():
  FORTRAN77 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.
  Created data file "ornstein_uhlenbeck_euler_maruyama_data.txt".
  Created command file "ornstein_uhlenbeck_euler_maruyama_commands.txt".

ornstein_uhlenbeck_test():
  Normal end of execution.

29 October   2023  11:18:39.833 AM