QuantLib_MonteCarloModel (3) - Linux Manuals

QuantLib_MonteCarloModel: General-purpose Monte Carlo model for path samples.

NAME

QuantLib::MonteCarloModel - General-purpose Monte Carlo model for path samples.

SYNOPSIS

#include <ql/methods/montecarlo/montecarlomodel.hpp>

Public Types

typedef MC< RNG > mc_traits

typedef RNG rng_traits

typedef MC< RNG >::path_generator_type path_generator_type

typedef MC< RNG >::path_pricer_type path_pricer_type

typedef path_generator_type::sample_type sample_type

typedef path_pricer_type::result_type result_type

typedef S stats_type

Public Member Functions

MonteCarloModel (const boost::shared_ptr< path_generator_type > &pathGenerator, const boost::shared_ptr< path_pricer_type > &pathPricer, const stats_type &sampleAccumulator, bool antitheticVariate, const boost::shared_ptr< path_pricer_type > &cvPathPricer=boost::shared_ptr< path_pricer_type >(), result_type cvOptionValue=result_type(), const boost::shared_ptr< path_generator_type > &cvPathGenerator=boost::shared_ptr< path_generator_type >())

void addSamples (Size samples)

const stats_type & sampleAccumulator (void) const

Detailed Description

template<template< class > class MC, class RNG, class S = Statistics> class QuantLib::MonteCarloModel< MC, RNG, S >

The template arguments of this class correspond to available policies for the particular model to be instantiated---i.e., whether it is single- or multi-asset, or whether it should use pseudo-random or low-discrepancy numbers for path generation. Such decisions are grouped in trait classes so as to be orthogonal---see mctraits.hpp for examples.

The constructor accepts two safe references, i.e. two smart pointers, one to a path generator and the other to a path pricer. In case of control variate technique the user should provide the additional control option, namely the option path pricer and the option value.

Examples:

DiscreteHedging.cpp.

Author

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