Piecewise linear Ornstein-Uhlenbeck process class. More...
#include <ql/experimental/shortrate/generalizedornsteinuhlenbeckprocess.hpp>
Public Member Functions | |
GeneralizedOrnsteinUhlenbeckProcess (const boost::function< Real(Time)> &speed, const boost::function< Real(Time)> &vol, Real x0=0.0, Real level=0.0) | |
Real | speed (Time t) const |
Real | volatility (Time t) const |
Real | level () const |
StochasticProcess1D interface | |
Real | x0 () const |
returns the initial value of the state variable | |
Real | drift (Time t, Real x) const |
returns the drift part of the equation, i.e. \( \mu(t, x_t) \) | |
Real | diffusion (Time t, Real x) const |
returns the diffusion part of the equation, i.e. \( \sigma(t, x_t) \) | |
Real | expectation (Time t0, Real x0, Time dt) const |
Real | stdDeviation (Time t0, Real x0, Time dt) const |
Real | variance (Time t0, Real x0, Time dt) const |
![]() | |
virtual Real | evolve (Time t0, Real x0, Time dt, Real dw) const |
virtual Real | apply (Real x0, Real dx) const |
![]() | |
virtual Size | factors () const |
returns the number of independent factors of the process | |
virtual Time | time (const Date &) const |
void | update () |
![]() | |
Observer (const Observer &) | |
Observer & | operator= (const Observer &) |
std::pair< std::set< boost::shared_ptr< Observable > >::iterator, bool > | registerWith (const boost::shared_ptr< Observable > &) |
void | registerWithObservables (const boost::shared_ptr< Observer > &) |
Size | unregisterWith (const boost::shared_ptr< Observable > &) |
void | unregisterWithAll () |
![]() | |
Observable (const Observable &) | |
Observable & | operator= (const Observable &) |
void | notifyObservers () |
Additional Inherited Members | |
![]() | |
StochasticProcess1D (const boost::shared_ptr< discretization > &) | |
![]() | |
StochasticProcess (const boost::shared_ptr< discretization > &) | |
![]() | |
boost::shared_ptr< discretization > | discretization_ |
![]() | |
boost::shared_ptr< discretization > | discretization_ |
Piecewise linear Ornstein-Uhlenbeck process class.
This class describes the Ornstein-Uhlenbeck process governed by
\[ dx = a (level - x_t) dt + \sigma dW_t \]
where the coefficients a and sigma are piecewise linear.
returns the expectation \( E(x_{t_0 + \Delta t} | x_{t_0} = x_0) \) of the process after a time interval \( \Delta t \) according to the given discretization. This method can be overridden in derived classes which want to hard-code a particular discretization.
Reimplemented from StochasticProcess1D.
returns the standard deviation \( S(x_{t_0 + \Delta t} | x_{t_0} = x_0) \) of the process after a time interval \( \Delta t \) according to the given discretization. This method can be overridden in derived classes which want to hard-code a particular discretization.
Reimplemented from StochasticProcess1D.
returns the variance \( V(x_{t_0 + \Delta t} | x_{t_0} = x_0) \) of the process after a time interval \( \Delta t \) according to the given discretization. This method can be overridden in derived classes which want to hard-code a particular discretization.
Reimplemented from StochasticProcess1D.