QuantLib
A free/open-source library for quantitative finance
Reference manual - version 1.12
Public Member Functions | List of all members
BinomialDoubleBarrierEngine< T, D > Class Template Reference

Pricing engine for double barrier options using binomial trees. More...

#include <ql/experimental/barrieroption/binomialdoublebarrierengine.hpp>

+ Inheritance diagram for BinomialDoubleBarrierEngine< T, D >:

Public Member Functions

 BinomialDoubleBarrierEngine (const boost::shared_ptr< GeneralizedBlackScholesProcess > &process, Size timeSteps)
 
void calculate () const
 
- Public Member Functions inherited from GenericEngine< DoubleBarrierOption::arguments, DoubleBarrierOption::results >
PricingEngine::arguments * getArguments () const
 
const PricingEngine::results * getResults () const
 
void reset ()
 
void update ()
 
- Public Member Functions inherited from Observable
 Observable (const Observable &)
 
Observableoperator= (const Observable &)
 
void notifyObservers ()
 
- Public Member Functions inherited from Observer
 Observer (const Observer &)
 
Observeroperator= (const Observer &)
 
std::pair< iterator, bool > registerWith (const boost::shared_ptr< Observable > &)
 
void registerWithObservables (const boost::shared_ptr< Observer > &)
 
Size unregisterWith (const boost::shared_ptr< Observable > &)
 
void unregisterWithAll ()
 
virtual void deepUpdate ()
 

Additional Inherited Members

- Public Types inherited from Observer
typedef std::set< boost::shared_ptr< Observable > > set_type
 
typedef set_type::iterator iterator
 
- Protected Member Functions inherited from DoubleBarrierOption::engine
bool triggered (Real underlying) const
 
- Protected Attributes inherited from GenericEngine< DoubleBarrierOption::arguments, DoubleBarrierOption::results >
DoubleBarrierOption::arguments arguments_
 
DoubleBarrierOption::results results_
 

Detailed Description

template<class T, class D = DiscretizedDoubleBarrierOption>
class QuantLib::BinomialDoubleBarrierEngine< T, D >

Pricing engine for double barrier options using binomial trees.

Note
This engine requires a the discretized option classes. By default uses a standard binomial implementation, but it can also work with DiscretizedDermanKaniDoubleBarrierOption to implement a Derman-Kani optimization.
Tests:
the correctness of the returned values is tested by checking it against analytic results.