- QuantLib
- KlugeExtOUProcess
 
#include <ql/experimental/processes/klugeextouprocess.hpp>

| Public Member Functions | |
| KlugeExtOUProcess (Real rho, const boost::shared_ptr< ExtOUWithJumpsProcess > &kluge, const boost::shared_ptr< ExtendedOrnsteinUhlenbeckProcess > &extOU) | |
| Size | size () const | 
| returns the number of dimensions of the stochastic process | |
| Size | factors () const | 
| returns the number of independent factors of the process | |
| Disposable< Array > | initialValues () const | 
| returns the initial values of the state variables | |
| Disposable< Array > | drift (Time t, const Array &x) const | 
| returns the drift part of the equation, i.e.,   | |
| Disposable< Matrix > | diffusion (Time t, const Array &x) const | 
| returns the diffusion part of the equation, i.e.   | |
| Disposable< Array > | evolve (Time t0, const Array &x0, Time dt, const Array &dw) const | 
| boost::shared_ptr < ExtOUWithJumpsProcess > | getKlugeProcess () const | 
| boost::shared_ptr < ExtendedOrnsteinUhlenbeckProcess > | getExtOUProcess () const | 
| Real | rho () const | 
This class describes a correlated Kluge - extended Ornstein-Uhlenbeck process governed by
![\[ \begin{array}{rcl} P_t &=& \exp(p_t + X_t + Y_t) \\ dX_t &=& -\alpha X_tdt + \sigma_x dW_t^x \\ dY_t &=& -\beta Y_{t-}dt + J_tdN_t \\ \omega(J) &=& \eta e^{-\eta J} \\ G_t &=& \exp(g_t + U_t) \\ dU_t &=& -\kappa U_tdt + \sigma_udW_t^u \\ \rho &=& \mathrm{corr} (dW_t^x, dW_t^u) \end{array} \]](form_133.png) 
References: B. Hambly, S. Howison, T. Kluge, Modelling spikes and pricing swing options in electricity markets, http://people.maths.ox.ac.uk/hambly/PDF/Papers/elec.pdf
returns the asset value after a time interval  according to the given discretization. By default, it returns
 according to the given discretization. By default, it returns 
![\[ E(\mathrm{x}_0,t_0,\Delta t) + S(\mathrm{x}_0,t_0,\Delta t) \cdot \Delta \mathrm{w} \]](form_357.png) 
 where  is the expectation and
 is the expectation and  the standard deviation.
 the standard deviation. 
Reimplemented from StochasticProcess.