😏★,°:.☆(￣▽￣)/$:.°★ 😏
这篇文章主要介绍量化交易库QuantLib配置与使用。
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文章目录
- :smirk:1. 项目介绍
- :blush:2. 环境配置
- :satisfied:3. 使用说明
😏1. 项目介绍
官网：https://www.quantlib.org/
项目Github地址：https://github.com/lballabio/QuantLib
QuantLib（Quantitative Finance Library）是一个开源的跨平台软件框架，专为量化金融领域设计和开发。它提供了丰富的金融工具和计算功能，用于衍生品定价、风险管理、投资组合管理等多个领域。以下是关于QuantLib的一些主要特点和用途：
1.开源跨平台：QuantLib是完全开源的，可以在不同操作系统上运行，包括Windows、Linux和Mac OS X。这使得它成为量化金融研究和开发的理想工具，能够在不同的环境中使用和定制。
2.丰富的金融工具：QuantLib支持多种金融工具和衍生品的定价和分析，包括利率衍生品（如利率互换、利率期权）、股票衍生品（如期权）、信用衍生品（如信用违约掉期）、外汇衍生品等。
3.数值方法和模型支持：QuantLib提供了广泛的数值方法和模型，用于衍生品定价和风险管理，如蒙特卡洛模拟、有限差分法、解析方法等。它支持的模型包括Black-Scholes模型、Heston模型、Libor Market Model等。
4.投资组合和风险管理：QuantLib能够处理复杂的投资组合和风险管理需求，包括风险测度、对冲分析、压力测试等，为金融机构和量化交易员提供重要的决策支持工具。
5.易于集成和扩展：QuantLib的设计允许用户根据特定需求进行定制和扩展，通过C++编程接口提供了灵活的扩展性，同时也支持Python等编程语言的接口，使得QuantLib能够与其他系统和库集成使用。
😊2. 环境配置
Ubuntu环境安装QuantLib库：
git clone https://github.com/lballabio/QuantLib # 或者下载release版本 1.34
mkdir build && cd build
cmake ..
make
sudo make install
程序g++编译：g++ -o main main.cpp -lQuantLib
😆3. 使用说明
下面是一个简单示例，计算零息债券的定价：
#include <ql/quantlib.hpp>
#include <iostream>
using namespace QuantLib;
int main() {
// 设置评估日期
Date today = Date::todaysDate();
Settings::instance().evaluationDate() = today;
// 定义债券参数
Real faceAmount = 1000.0; // 债券面值
Rate couponRate = 0.05; // 年利率
Date maturity = today + Period(1, Years); // 到期时间
// 创建收益率曲线
Rate marketRate = 0.03; // 市场利率
Handle<YieldTermStructure> discountCurve(boost::shared_ptr<YieldTermStructure>(
new FlatForward(today, marketRate, Actual360())));
// 创建零息债券
ZeroCouponBond bond(0, NullCalendar(), faceAmount, maturity, Following, 100.0, today);
// 创建定价引擎并设置参数
bond.setPricingEngine(boost::shared_ptr<PricingEngine>(
new DiscountingBondEngine(discountCurve)));
// 计算债券价格
Real bondPrice = bond.NPV();
std::cout << "Zero-coupon bond price: " << bondPrice << std::endl;
return 0;
}
此外，还有官方示例里的BasketLosses 计算一组金融资产损失示例（看起来还是很复杂的）：
/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*!
Copyright (C) 2009 Mark Joshi
This file is part of QuantLib, a free-software/open-source library
for financial quantitative analysts and developers - http://quantlib.org/
QuantLib is free software: you can redistribute it and/or modify it
under the terms of the QuantLib license. You should have received a
copy of the license along with this program; if not, please email
<quantlib-dev@lists.sf.net>. The license is also available online at
<http://quantlib.org/license.shtml>.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the license for more details.
*/
#include <ql/qldefines.hpp>
#if !defined(BOOST_ALL_NO_LIB) && defined(BOOST_MSVC)
# include <ql/auto_link.hpp>
#endif
#include <ql/models/marketmodels/marketmodel.hpp>
#include <ql/models/marketmodels/accountingengine.hpp>
#include <ql/models/marketmodels/pathwiseaccountingengine.hpp>
#include <ql/models/marketmodels/products/multiproductcomposite.hpp>
#include <ql/models/marketmodels/products/multistep/multistepswap.hpp>
#include <ql/models/marketmodels/products/multistep/callspecifiedmultiproduct.hpp>
#include <ql/models/marketmodels/products/multistep/exerciseadapter.hpp>
#include <ql/models/marketmodels/products/multistep/multistepnothing.hpp>
#include <ql/models/marketmodels/products/multistep/multistepinversefloater.hpp>
#include <ql/models/marketmodels/products/pathwise/pathwiseproductswap.hpp>
#include <ql/models/marketmodels/products/pathwise/pathwiseproductinversefloater.hpp>
#include <ql/models/marketmodels/products/pathwise/pathwiseproductcallspecified.hpp>
#include <ql/models/marketmodels/models/flatvol.hpp>
#include <ql/models/marketmodels/callability/swapratetrigger.hpp>
#include <ql/models/marketmodels/callability/swapbasissystem.hpp>
#include <ql/models/marketmodels/callability/swapforwardbasissystem.hpp>
#include <ql/models/marketmodels/callability/nothingexercisevalue.hpp>
#include <ql/models/marketmodels/callability/collectnodedata.hpp>
#include <ql/models/marketmodels/callability/lsstrategy.hpp>
#include <ql/models/marketmodels/callability/upperboundengine.hpp>
#include <ql/models/marketmodels/correlations/expcorrelations.hpp>
#include <ql/models/marketmodels/browniangenerators/mtbrowniangenerator.hpp>
#include <ql/models/marketmodels/browniangenerators/sobolbrowniangenerator.hpp>
#include <ql/models/marketmodels/evolvers/lognormalfwdratepc.hpp>
#include <ql/models/marketmodels/evolvers/lognormalfwdrateeuler.hpp>
#include <ql/models/marketmodels/pathwisegreeks/bumpinstrumentjacobian.hpp>
#include <ql/models/marketmodels/utilities.hpp>
#include <ql/methods/montecarlo/genericlsregression.hpp>
#include <ql/legacy/libormarketmodels/lmlinexpcorrmodel.hpp>
#include <ql/legacy/libormarketmodels/lmextlinexpvolmodel.hpp>
#include <ql/time/schedule.hpp>
#include <ql/time/calendars/nullcalendar.hpp>
#include <ql/time/daycounters/simpledaycounter.hpp>
#include <ql/pricingengines/blackformula.hpp>
#include <ql/pricingengines/blackcalculator.hpp>
#include <ql/utilities/dataformatters.hpp>
#include <ql/math/integrals/segmentintegral.hpp>
#include <ql/math/statistics/convergencestatistics.hpp>
#include <ql/termstructures/volatility/abcd.hpp>
#include <ql/termstructures/volatility/abcdcalibration.hpp>
#include <ql/math/optimization/simplex.hpp>
#include <ql/quotes/simplequote.hpp>
#include <sstream>
#include <iostream>
#include <ctime>
using namespace QuantLib;
std::vector<std::vector<Matrix>>
theVegaBumps(bool factorwiseBumping, const ext::shared_ptr<MarketModel>& marketModel, bool doCaps) {
Real multiplierCutOff = 50.0;
Real projectionTolerance = 1E-4;
Size numberRates= marketModel->numberOfRates();
std::vector<VolatilityBumpInstrumentJacobian::Cap> caps;
if (doCaps)
{
Rate capStrike = marketModel->initialRates()[0];
for (Size i=0; i< numberRates-1; i=i+1)
{
VolatilityBumpInstrumentJacobian::Cap nextCap;
nextCap.startIndex_ = i;
nextCap.endIndex_ = i+1;
nextCap.strike_ = capStrike;
caps.push_back(nextCap);
}
}
std::vector<VolatilityBumpInstrumentJacobian::Swaption> swaptions(numberRates);
for (Size i=0; i < numberRates; ++i)
{
swaptions[i].startIndex_ = i;
swaptions[i].endIndex_ = numberRates;
}
VegaBumpCollection possibleBumps(marketModel,
factorwiseBumping);
OrthogonalizedBumpFinder bumpFinder(possibleBumps,
swaptions,
caps,
multiplierCutOff, // if vector length grows by more than this discard
projectionTolerance); // if vector projection before scaling less than this discard
std::vector<std::vector<Matrix>> theBumps;
bumpFinder.GetVegaBumps(theBumps);
return theBumps;
}
int Bermudan()
{
Size numberRates =20;
Real accrual = 0.5;
Real firstTime = 0.5;
std::vector<Real> rateTimes(numberRates+1);
for (Size i=0; i < rateTimes.size(); ++i)
rateTimes[i] = firstTime + i*accrual;
std::vector<Real> paymentTimes(numberRates);
std::vector<Real> accruals(numberRates,accrual);
for (Size i=0; i < paymentTimes.size(); ++i)
paymentTimes[i] = firstTime + (i+1)*accrual;
Real fixedRate = 0.05;
std::vector<Real> strikes(numberRates,fixedRate);
Real receive = -1.0;
// 0. a payer swap
MultiStepSwap payerSwap(rateTimes, accruals, accruals, paymentTimes,
fixedRate, true);
// 1. the equivalent receiver swap
MultiStepSwap receiverSwap(rateTimes, accruals, accruals, paymentTimes,
fixedRate, false);
//exercise schedule, we can exercise on any rate time except the last one
std::vector<Rate> exerciseTimes(rateTimes);
exerciseTimes.pop_back();
// naive exercise strategy, exercise above a trigger level
std::vector<Rate> swapTriggers(exerciseTimes.size(), fixedRate);
SwapRateTrigger naifStrategy(rateTimes, swapTriggers, exerciseTimes);
// Longstaff-Schwartz exercise strategy
std::vector<std::vector<NodeData>> collectedData;
std::vector<std::vector<Real>> basisCoefficients;
// control that does nothing, need it because some control is expected
NothingExerciseValue control(rateTimes);
// SwapForwardBasisSystem basisSystem(rateTimes,exerciseTimes);
SwapBasisSystem basisSystem(rateTimes,exerciseTimes);
// rebate that does nothing, need it because some rebate is expected
// when you break a swap nothing happens.
NothingExerciseValue nullRebate(rateTimes);
CallSpecifiedMultiProduct dummyProduct =
CallSpecifiedMultiProduct(receiverSwap, naifStrategy,
ExerciseAdapter(nullRebate));
const EvolutionDescription& evolution = dummyProduct.evolution();
// parameters for models
Size seed = 12332; // for Sobol generator
Size trainingPaths = 65536;
Size paths = 16384;
Size vegaPaths = 16384*64;
std::cout << "training paths, " << trainingPaths << "\n";
std::cout << "paths, " << paths << "\n";
std::cout << "vega Paths, " << vegaPaths << "\n";
#ifdef _DEBUG
trainingPaths = 512;
paths = 1024;
vegaPaths = 1024;
#endif
// set up a calibration, this would typically be done by using a calibrator
Real rateLevel =0.05;
Real initialNumeraireValue = 0.95;
Real volLevel = 0.11;
Real beta = 0.2;
Real gamma = 1.0;
Size numberOfFactors = std::min<Size>(5,numberRates);
Spread displacementLevel =0.02;
// set up vectors
std::vector<Rate> initialRates(numberRates,rateLevel);
std::vector<Volatility> volatilities(numberRates, volLevel);
std::vector<Spread> displacements(numberRates, displacementLevel);
ExponentialForwardCorrelation correlations(
rateTimes,volLevel, beta,gamma);
FlatVol calibration(
volatilities,
ext::make_shared<ExponentialForwardCorrelation>(correlations),
evolution,
numberOfFactors,
initialRates,
displacements);
auto marketModel = ext::make_shared<FlatVol>(calibration);
// we use a factory since there is data that will only be known later
SobolBrownianGeneratorFactory generatorFactory(
SobolBrownianGenerator::Diagonal, seed);
std::vector<Size> numeraires( moneyMarketMeasure(evolution));
// the evolver will actually evolve the rates
LogNormalFwdRatePc evolver(marketModel,
generatorFactory,
numeraires // numeraires for each step
);
auto evolverPtr = ext::make_shared<LogNormalFwdRatePc>(evolver);
int t1= clock();
// gather data before computing exercise strategy
collectNodeData(evolver,
receiverSwap,
basisSystem,
nullRebate,
control,
trainingPaths,
collectedData);
int t2 = clock();
// calculate the exercise strategy's coefficients
genericLongstaffSchwartzRegression(collectedData,
basisCoefficients);
// turn the coefficients into an exercise strategy
LongstaffSchwartzExerciseStrategy exerciseStrategy(
basisSystem, basisCoefficients,
evolution, numeraires,
nullRebate, control);
// bermudan swaption to enter into the payer swap
CallSpecifiedMultiProduct bermudanProduct =
CallSpecifiedMultiProduct(
MultiStepNothing(evolution),
exerciseStrategy, payerSwap);
// callable receiver swap
CallSpecifiedMultiProduct callableProduct =
CallSpecifiedMultiProduct(
receiverSwap, exerciseStrategy,
ExerciseAdapter(nullRebate));
// lower bound: evolve all 4 products togheter
MultiProductComposite allProducts;
allProducts.add(payerSwap);
allProducts.add(receiverSwap);
allProducts.add(bermudanProduct);
allProducts.add(callableProduct);
allProducts.finalize();
AccountingEngine accounter(evolverPtr,
Clone<MarketModelMultiProduct>(allProducts),
initialNumeraireValue);
SequenceStatisticsInc stats;
accounter.multiplePathValues (stats,paths);
int t3 = clock();
std::vector<Real> means(stats.mean());
for (Real mean : means)
std::cout << mean << "\n";
std::cout << " time to build strategy, " << (t2-t1)/static_cast<Real>(CLOCKS_PER_SEC)<< ", seconds.\n";
std::cout << " time to price, " << (t3-t2)/static_cast<Real>(CLOCKS_PER_SEC)<< ", seconds.\n";
// vegas
// do it twice once with factorwise bumping, once without
Size pathsToDoVegas = vegaPaths;
for (Size i=0; i < 4; ++i)
{
bool allowFactorwiseBumping = i % 2 > 0 ;
bool doCaps = i / 2 > 0 ;
LogNormalFwdRateEuler evolverEuler(marketModel,
generatorFactory,
numeraires
) ;
MarketModelPathwiseSwap receiverPathwiseSwap( rateTimes,
accruals,
strikes,
receive);
Clone<MarketModelPathwiseMultiProduct> receiverPathwiseSwapPtr(receiverPathwiseSwap.clone());
// callable receiver swap
CallSpecifiedPathwiseMultiProduct callableProductPathwise(receiverPathwiseSwapPtr,
exerciseStrategy);
Clone<MarketModelPathwiseMultiProduct> callableProductPathwisePtr(callableProductPathwise.clone());
std::vector<std::vector<Matrix>> theBumps(theVegaBumps(allowFactorwiseBumping,
marketModel,
doCaps));
PathwiseVegasOuterAccountingEngine
accountingEngineVegas(ext::make_shared<LogNormalFwdRateEuler>(evolverEuler),
callableProductPathwisePtr,
marketModel,
theBumps,
initialNumeraireValue);
std::vector<Real> values,errors;
accountingEngineVegas.multiplePathValues(values,errors,pathsToDoVegas);
std::cout << "vega output \n";
std::cout << " factorwise bumping " << allowFactorwiseBumping << "\n";
std::cout << " doCaps " << doCaps << "\n";
Size r=0;
std::cout << " price estimate, " << values[r++] << "\n";
for (Size i=0; i < numberRates; ++i, ++r)
std::cout << " Delta, " << i << ", " << values[r] << ", " << errors[r] << "\n";
Real totalVega = 0.0;
for (; r < values.size(); ++r)
{
std::cout << " vega, " << r - 1 - numberRates<< ", " << values[r] << " ," << errors[r] << "\n";
totalVega += values[r];
}
std::cout << " total Vega, " << totalVega << "\n";
}
// upper bound
MTBrownianGeneratorFactory uFactory(seed+142);
auto upperEvolver = ext::make_shared<LogNormalFwdRatePc>(ext::make_shared<FlatVol>(calibration),
uFactory,
numeraires // numeraires for each step
);
std::vector<ext::shared_ptr<MarketModelEvolver>> innerEvolvers;
std::valarray<bool> isExerciseTime = isInSubset(evolution.evolutionTimes(), exerciseStrategy.exerciseTimes());
for (Size s=0; s < isExerciseTime.size(); ++s)
{
if (isExerciseTime[s])
{
MTBrownianGeneratorFactory iFactory(seed+s);
auto e = ext::make_shared<LogNormalFwdRatePc>(ext::make_shared<FlatVol>(calibration),
uFactory,
numeraires, // numeraires for each step
s);
innerEvolvers.push_back(e);
}
}
UpperBoundEngine uEngine(upperEvolver, // does outer paths
innerEvolvers, // for sub-simulations that do continuation values
receiverSwap,
nullRebate,
receiverSwap,
nullRebate,
exerciseStrategy,
initialNumeraireValue);
Statistics uStats;
Size innerPaths = 255;
Size outerPaths =256;
int t4 = clock();
uEngine.multiplePathValues(uStats,outerPaths,innerPaths);
Real upperBound = uStats.mean();
Real upperSE = uStats.errorEstimate();
int t5=clock();
std::cout << " Upper - lower is, " << upperBound << ", with standard error " << upperSE << "\n";
std::cout << " time to compute upper bound is, " << (t5-t4)/static_cast<Real>(CLOCKS_PER_SEC) << ", seconds.\n";
return 0;
}
int InverseFloater(Real rateLevel)
{
Size numberRates =20;
Real accrual = 0.5;
Real firstTime = 0.5;
Real strike =0.15;
Real fixedMultiplier = 2.0;
Real floatingSpread =0.0;
bool payer = true;
std::vector<Real> rateTimes(numberRates+1);
for (Size i=0; i < rateTimes.size(); ++i)
rateTimes[i] = firstTime + i*accrual;
std::vector<Real> paymentTimes(numberRates);
std::vector<Real> accruals(numberRates,accrual);
std::vector<Real> fixedStrikes(numberRates,strike);
std::vector<Real> floatingSpreads(numberRates,floatingSpread);
std::vector<Real> fixedMultipliers(numberRates,fixedMultiplier);
for (Size i=0; i < paymentTimes.size(); ++i)
paymentTimes[i] = firstTime + (i+1)*accrual;
MultiStepInverseFloater inverseFloater(
rateTimes,
accruals,
accruals,
fixedStrikes,
fixedMultipliers,
floatingSpreads,
paymentTimes,
payer);
//exercise schedule, we can exercise on any rate time except the last one
std::vector<Rate> exerciseTimes(rateTimes);
exerciseTimes.pop_back();
// naive exercise strategy, exercise above a trigger level
Real trigger =0.05;
std::vector<Rate> swapTriggers(exerciseTimes.size(), trigger);
SwapRateTrigger naifStrategy(rateTimes, swapTriggers, exerciseTimes);
// Longstaff-Schwartz exercise strategy
std::vector<std::vector<NodeData>> collectedData;
std::vector<std::vector<Real>> basisCoefficients;
// control that does nothing, need it because some control is expected
NothingExerciseValue control(rateTimes);
SwapForwardBasisSystem basisSystem(rateTimes,exerciseTimes);
// SwapBasisSystem basisSystem(rateTimes,exerciseTimes);
// rebate that does nothing, need it because some rebate is expected
// when you break a swap nothing happens.
NothingExerciseValue nullRebate(rateTimes);
CallSpecifiedMultiProduct dummyProduct =
CallSpecifiedMultiProduct(inverseFloater, naifStrategy,
ExerciseAdapter(nullRebate));
const EvolutionDescription& evolution = dummyProduct.evolution();
// parameters for models
Size seed = 12332; // for Sobol generator
Size trainingPaths = 65536;
Size paths = 65536;
Size vegaPaths =16384;
#ifdef _DEBUG
trainingPaths = 8192;
paths = 8192;
vegaPaths = 1024;
#endif
std::cout << " inverse floater \n";
std::cout << " fixed strikes : " << strike << "\n";
std::cout << " number rates : " << numberRates << "\n";
std::cout << "training paths, " << trainingPaths << "\n";
std::cout << "paths, " << paths << "\n";
std::cout << "vega Paths, " << vegaPaths << "\n";
// set up a calibration, this would typically be done by using a calibrator
//Real rateLevel =0.08;
std::cout << " rate level " << rateLevel << "\n";
Real initialNumeraireValue = 0.95;
Real volLevel = 0.11;
Real beta = 0.2;
Real gamma = 1.0;
Size numberOfFactors = std::min<Size>(5,numberRates);
Spread displacementLevel =0.02;
// set up vectors
std::vector<Rate> initialRates(numberRates,rateLevel);
std::vector<Volatility> volatilities(numberRates, volLevel);
std::vector<Spread> displacements(numberRates, displacementLevel);
ExponentialForwardCorrelation correlations(
rateTimes,volLevel, beta,gamma);
FlatVol calibration(
volatilities,
ext::make_shared<ExponentialForwardCorrelation>(correlations),
evolution,
numberOfFactors,
initialRates,
displacements);
auto marketModel = ext::make_shared<FlatVol>(calibration);
// we use a factory since there is data that will only be known later
SobolBrownianGeneratorFactory generatorFactory(
SobolBrownianGenerator::Diagonal, seed);
std::vector<Size> numeraires( moneyMarketMeasure(evolution));
// the evolver will actually evolve the rates
LogNormalFwdRatePc evolver(marketModel,
generatorFactory,
numeraires // numeraires for each step
);
auto evolverPtr = ext::make_shared<LogNormalFwdRatePc>(evolver);
int t1= clock();
// gather data before computing exercise strategy
collectNodeData(evolver,
inverseFloater,
basisSystem,
nullRebate,
control,
trainingPaths,
collectedData);
int t2 = clock();
// calculate the exercise strategy's coefficients
genericLongstaffSchwartzRegression(collectedData,
basisCoefficients);
// turn the coefficients into an exercise strategy
LongstaffSchwartzExerciseStrategy exerciseStrategy(
basisSystem, basisCoefficients,
evolution, numeraires,
nullRebate, control);
// callable receiver swap
CallSpecifiedMultiProduct callableProduct =
CallSpecifiedMultiProduct(
inverseFloater, exerciseStrategy,
ExerciseAdapter(nullRebate));
MultiProductComposite allProducts;
allProducts.add(inverseFloater);
allProducts.add(callableProduct);
allProducts.finalize();
AccountingEngine accounter(evolverPtr,
Clone<MarketModelMultiProduct>(allProducts),
initialNumeraireValue);
SequenceStatisticsInc stats;
accounter.multiplePathValues (stats,paths);
int t3 = clock();
std::vector<Real> means(stats.mean());
for (Real mean : means)
std::cout << mean << "\n";
std::cout << " time to build strategy, " << (t2-t1)/static_cast<Real>(CLOCKS_PER_SEC)<< ", seconds.\n";
std::cout << " time to price, " << (t3-t2)/static_cast<Real>(CLOCKS_PER_SEC)<< ", seconds.\n";
// vegas
// do it twice once with factorwise bumping, once without
Size pathsToDoVegas = vegaPaths;
for (Size i=0; i < 4; ++i)
{
bool allowFactorwiseBumping = i % 2 > 0 ;
bool doCaps = i / 2 > 0 ;
LogNormalFwdRateEuler evolverEuler(marketModel,
generatorFactory,
numeraires
) ;
MarketModelPathwiseInverseFloater pathwiseInverseFloater(
rateTimes,
accruals,
accruals,
fixedStrikes,
fixedMultipliers,
floatingSpreads,
paymentTimes,
payer);
Clone<MarketModelPathwiseMultiProduct> pathwiseInverseFloaterPtr(pathwiseInverseFloater.clone());
// callable inverse floater
CallSpecifiedPathwiseMultiProduct callableProductPathwise(pathwiseInverseFloaterPtr,
exerciseStrategy);
Clone<MarketModelPathwiseMultiProduct> callableProductPathwisePtr(callableProductPathwise.clone());
std::vector<std::vector<Matrix>> theBumps(theVegaBumps(allowFactorwiseBumping,
marketModel,
doCaps));
PathwiseVegasOuterAccountingEngine
accountingEngineVegas(ext::make_shared<LogNormalFwdRateEuler>(evolverEuler),
// pathwiseInverseFloaterPtr,
callableProductPathwisePtr,
marketModel,
theBumps,
initialNumeraireValue);
std::vector<Real> values,errors;
accountingEngineVegas.multiplePathValues(values,errors,pathsToDoVegas);
std::cout << "vega output \n";
std::cout << " factorwise bumping " << allowFactorwiseBumping << "\n";
std::cout << " doCaps " << doCaps << "\n";
Size r=0;
std::cout << " price estimate, " << values[r++] << "\n";
for (Size i=0; i < numberRates; ++i, ++r)
std::cout << " Delta, " << i << ", " << values[r] << ", " << errors[r] << "\n";
Real totalVega = 0.0;
for (; r < values.size(); ++r)
{
std::cout << " vega, " << r - 1 - numberRates<< ", " << values[r] << " ," << errors[r] << "\n";
totalVega += values[r];
}
std::cout << " total Vega, " << totalVega << "\n";
}
// upper bound
MTBrownianGeneratorFactory uFactory(seed+142);
auto upperEvolver = ext::make_shared<LogNormalFwdRatePc>(ext::make_shared<FlatVol>(calibration),
uFactory,
numeraires // numeraires for each step
);
std::vector<ext::shared_ptr<MarketModelEvolver>> innerEvolvers;
std::valarray<bool> isExerciseTime = isInSubset(evolution.evolutionTimes(), exerciseStrategy.exerciseTimes());
for (Size s=0; s < isExerciseTime.size(); ++s)
{
if (isExerciseTime[s])
{
MTBrownianGeneratorFactory iFactory(seed+s);
auto e = ext::make_shared<LogNormalFwdRatePc>(ext::make_shared<FlatVol>(calibration),
uFactory,
numeraires , // numeraires for each step
s);
innerEvolvers.push_back(e);
}
}
UpperBoundEngine uEngine(upperEvolver, // does outer paths
innerEvolvers, // for sub-simulations that do continuation values
inverseFloater,
nullRebate,
inverseFloater,
nullRebate,
exerciseStrategy,
initialNumeraireValue);
Statistics uStats;
Size innerPaths = 255;
Size outerPaths =256;
int t4 = clock();
uEngine.multiplePathValues(uStats,outerPaths,innerPaths);
Real upperBound = uStats.mean();
Real upperSE = uStats.errorEstimate();
int t5=clock();
std::cout << " Upper - lower is, " << upperBound << ", with standard error " << upperSE << "\n";
std::cout << " time to compute upper bound is, " << (t5-t4)/static_cast<Real>(CLOCKS_PER_SEC) << ", seconds.\n";
return 0;
}
int main()
{
try {
for (Size i=5; i < 10; ++i)
InverseFloater(i/100.0);
return 0;
} catch (std::exception& e) {
std::cerr << e.what() << std::endl;
return 1;
} catch (...) {
std::cerr << "unknown error" << std::endl;
return 1;
}
}
以上。
