void FittedBondDiscountCurve::FittingMethod::calculate() {
FittingCost& costFunction = *costFunction_;
Constraint constraint = NoConstraint();
// start with the guess solution, if it exists
Array x(size(), 0.0);
if (!curve_->guessSolution_.empty()) {
x = curve_->guessSolution_;
}
Simplex simplex(curve_->simplexLambda_);
Problem problem(costFunction, constraint, x);
Natural maxStationaryStateIterations = 100;
Real rootEpsilon = curve_->accuracy_;
Real functionEpsilon = curve_->accuracy_;
Real gradientNormEpsilon = curve_->accuracy_;
EndCriteria endCriteria(curve_->maxEvaluations_,
maxStationaryStateIterations,
rootEpsilon,
functionEpsilon,
gradientNormEpsilon);
simplex.minimize(problem,endCriteria);
solution_ = problem.currentValue();
numberOfIterations_ = problem.functionEvaluation();
costValue_ = problem.functionValue();
// save the results as the guess solution, in case of recalculation
curve_->guessSolution_ = solution_;
}
HullWhiteTimeDependentParameters calibration_hull_white(
const Date& evalDate,
const CapVolData& volData
)
{
boost::shared_ptr<IborIndex> index = volData.index;
Frequency fixedFreq = volData.fixedFreq;
DayCounter fixedDC = volData.fixedDC;
Real FixedA = volData.fixedA;
std::vector<Real> initialSigma = volData.initialSigma;
Settings::instance().evaluationDate() = Date( evalDate.serialNumber() );
Date today = Settings::instance().evaluationDate();
std::vector<Date> dates;
for (Size i=0; i<volData.tenors.size(); ++i){
dates.push_back(today+volData.tenors[i]*Years);
}
dates.back() = today+50*Years;
std::set<Date> temp(dates.begin(), dates.end());
dates = std::vector<Date>(temp.begin(), temp.end());
Handle<YieldTermStructure> rts_hw(index->forwardingTermStructure().currentLink());
boost::shared_ptr<Generalized_HullWhite> model(new Generalized_HullWhite(rts_hw, dates, initialSigma, FixedA));
boost::shared_ptr<PricingEngine> engine_hw(new AnalyticCapFloorEngine(model));
//boost::shared_ptr<PricingEngine> engine_hw(new TreeCapFloorEngine(model, 80));
std::vector<boost::shared_ptr<CalibrationHelper> > caps;
for (Size i=0; i<volData.tenors.size(); ++i) {
boost::shared_ptr<CalibrationHelper> helper(
new CapHelper(Period(volData.tenors[i], Years),
Handle<Quote>(boost::shared_ptr<Quote>(new SimpleQuote(volData.vols[i]))),
index, fixedFreq,
fixedDC,
false,
rts_hw, CalibrationHelper::PriceError));
helper->setPricingEngine(engine_hw);
caps.push_back(helper);
}
LevenbergMarquardt optimizationMethod(1.0e-8,1.0e-8,1.0e-8);
EndCriteria endCriteria(5000, 1000, 1e-8, 1e-8, 1e-8);
Constraint c = BoundaryConstraint(0.01, 3.0);
model->calibrate(caps, optimizationMethod, endCriteria, c);
EndCriteria::Type ecType = model->endCriteria();
Array xMinCalculated = model->params();
//Real a = xMinCalculated[0];
//Real sigmar = xMinCalculated[1];
std::vector<Real> sigma;
for (Size i=0; i<xMinCalculated.size(); ++i) {
sigma.push_back(xMinCalculated[i]);
}
return HullWhiteTimeDependentParameters(FixedA, dates, sigma, model, caps);
}
void FluctuatingChargePropagator::initialize() {
if (info_->usesFluctuatingCharges()) {
if (info_->getNFluctuatingCharges() > 0) {
hasFlucQ_ = true;
fqConstraints_ = new FluctuatingChargeConstraints(info_);
fqConstraints_->setConstrainRegions(fqParams_->getConstrainRegions());
}
}
if (!hasFlucQ_) {
initialized_ = true;
return;
}
// SimInfo::MoleculeIterator i;
// Molecule::FluctuatingChargeIterator j;
// Molecule* mol;
// Atom* atom;
//
// For single-minima flucq, this ensures a net neutral system, but
// for multiple minima, this is no longer the right thing to do:
//
// for (mol = info_->beginMolecule(i); mol != NULL;
// mol = info_->nextMolecule(i)) {
// for (atom = mol->beginFluctuatingCharge(j); atom != NULL;
// atom = mol->nextFluctuatingCharge(j)) {
// atom->setFlucQPos(0.0);
// atom->setFlucQVel(0.0);
// }
// }
FluctuatingChargeObjectiveFunction flucQobjf(info_, forceMan_,
fqConstraints_);
DynamicVector<RealType> initCoords = flucQobjf.setInitialCoords();
Problem problem(flucQobjf, *(new NoConstraint()), *(new NoStatus()),
initCoords);
EndCriteria endCriteria(1000, 100, 1e-5, 1e-5, 1e-5);
OptimizationMethod* minim = OptimizationFactory::getInstance()->createOptimization("SD", info_);
DumpStatusFunction dsf(info_); // we want a dump file written
// every iteration
minim->minimize(problem, endCriteria);
cerr << "back from minim\n";
initialized_ = true;
}
void FittedBondDiscountCurve::FittingMethod::calculate() {
FittingCost& costFunction = *costFunction_;
Constraint constraint = NoConstraint();
// start with the guess solution, if it exists
Array x(size(), 0.0);
if (!curve_->guessSolution_.empty()) {
x = curve_->guessSolution_;
}
if(curve_->maxEvaluations_ == 0)
{
//Don't calculate, simply use given parameters to provide a fitted curve.
//This turns the fittedbonddiscountcurve into an evaluator of the parametric
//curve, for example allowing to use the parameters for a credit spread curve
//calculated with bonds in one currency to be coupled to a discount curve in
//another currency.
return;
}
//workaround for backwards compatibility
ext::shared_ptr<OptimizationMethod> optimization = optimizationMethod_;
if(!optimization){
optimization = ext::make_shared<Simplex>(curve_->simplexLambda_);
}
Problem problem(costFunction, constraint, x);
Real rootEpsilon = curve_->accuracy_;
Real functionEpsilon = curve_->accuracy_;
Real gradientNormEpsilon = curve_->accuracy_;
EndCriteria endCriteria(curve_->maxEvaluations_,
curve_->maxStationaryStateIterations_,
rootEpsilon,
functionEpsilon,
gradientNormEpsilon);
optimization->minimize(problem,endCriteria);
solution_ = problem.currentValue();
numberOfIterations_ = problem.functionEvaluation();
costValue_ = problem.functionValue();
// save the results as the guess solution, in case of recalculation
curve_->guessSolution_ = solution_;
}
// [[Rcpp::export]]
Rcpp::List calibrateHullWhiteUsingSwapsEngine(SEXP termStrcDateVec,
SEXP termStrcZeroVec,
SEXP swapDataDF,
SEXP iborDateVec,
SEXP iborZeroVec,
std::string iborType,
QuantLib::Date evalDate) {
QuantLib::Settings::instance().evaluationDate() = evalDate;
//set up the HullWhite model
QuantLib::Handle<QuantLib::YieldTermStructure>
term(rebuildCurveFromZeroRates(termStrcDateVec, termStrcZeroVec));
boost::shared_ptr<QuantLib::HullWhite> model(new QuantLib::HullWhite(term));
//set up ibor index
QuantLib::Handle<QuantLib::YieldTermStructure>
indexStrc(rebuildCurveFromZeroRates(iborDateVec, iborZeroVec));
boost::shared_ptr<QuantLib::IborIndex> index = buildIborIndex(iborType, indexStrc);
//process capDataDF
boost::shared_ptr<QuantLib::PricingEngine>
engine(new QuantLib::JamshidianSwaptionEngine(model));
std::vector<boost::shared_ptr <QuantLib::CalibrationHelper> > swaps;
Rcpp::DataFrame swapDF(swapDataDF);
Rcpp::NumericVector i0v = swapDF[0];
Rcpp::CharacterVector s1v = swapDF[1];
Rcpp::NumericVector i2v = swapDF[2];
Rcpp::CharacterVector s3v = swapDF[3];
Rcpp::NumericVector d4v = swapDF[4];
Rcpp::NumericVector i5v = swapDF[5];
Rcpp::CharacterVector s6v = swapDF[6];
Rcpp::NumericVector i7v = swapDF[7];
Rcpp::NumericVector i8v = swapDF[8];
//std::vector<std::vector<ColDatum> > table = swapDF.getTableData();
//int nrow = table.size();
int nrow = i0v.size();
for (int row=0; row<nrow;row++) {
QuantLib::Period maturity = periodByTimeUnit(i0v[row],
Rcpp::as<std::string>(s1v[row]));
QuantLib::Period length = periodByTimeUnit(i0v[row],
Rcpp::as<std::string>(s3v[row]));
boost::shared_ptr<QuantLib::Quote> vol(new QuantLib::SimpleQuote(d4v[row]));
QuantLib::Period fixedLegTenor = periodByTimeUnit(i5v[row],
Rcpp::as<std::string>(s6v[row]));
QuantLib::DayCounter fixedLegDayCounter = getDayCounter(i7v[row]);
QuantLib::DayCounter floatingLegDayCounter = getDayCounter(i8v[row]);
boost::shared_ptr<QuantLib::CalibrationHelper>
helper(new QuantLib::SwaptionHelper(maturity, length,
QuantLib::Handle<QuantLib::Quote>(vol),
index,
fixedLegTenor,
fixedLegDayCounter,
floatingLegDayCounter,
term));
helper->setPricingEngine(engine);
swaps.push_back(helper);
}
//calibrate the data
QuantLib::LevenbergMarquardt optimizationMethod(1.0e-8,1.0e-8,1.0e-8);
QuantLib::EndCriteria endCriteria(10000, 100, 1e-6, 1e-8, 1e-8);
model->calibrate(swaps, optimizationMethod, endCriteria);
//EndCriteria::Type ecType = model->endCriteria();
//return the result
QuantLib::Array xMinCalculated = model->params();
return Rcpp::List::create(Rcpp::Named("alpha") = xMinCalculated[0],
Rcpp::Named("sigma") = xMinCalculated[1]);
}
// [[Rcpp::export]]
Rcpp::List calibrateHullWhiteUsingCapsEngine(SEXP termStrcDateVec,
SEXP termStrcZeroVec,
SEXP capDataDF,
SEXP iborDateVec,
SEXP iborZeroVec,
std::string iborType,
QuantLib::Date evalDate) {
QuantLib::Settings::instance().evaluationDate() = evalDate;
QuantLib::Handle<QuantLib::YieldTermStructure>
term(rebuildCurveFromZeroRates(termStrcDateVec, termStrcZeroVec));
//set up ibor index
QuantLib::Handle<QuantLib::YieldTermStructure>
indexStrc(rebuildCurveFromZeroRates(iborDateVec, iborZeroVec));
boost::shared_ptr<QuantLib::IborIndex> index = buildIborIndex(iborType, indexStrc);
//process capDataDF
std::vector<boost::shared_ptr <QuantLib::CalibrationHelper> > caps;
Rcpp::DataFrame capDF(capDataDF);
Rcpp::NumericVector i0v = capDF[0];
Rcpp::CharacterVector s1v = capDF[1];
Rcpp::NumericVector d2v = capDF[2];
Rcpp::NumericVector i3v = capDF[3];
Rcpp::NumericVector i4v = capDF[4];
Rcpp::NumericVector i5v = capDF[5];
//std::vector<std::vector<ColDatum> > table = capDF.getTableData();
//int nrow = table.size();
int nrow = i0v.size();
for (int row=0; row<nrow;row++) {
QuantLib::Period p = periodByTimeUnit(i0v[row], Rcpp::as<std::string>(s1v[row]));
boost::shared_ptr<QuantLib::Quote> vol(new QuantLib::SimpleQuote(d2v[row]));
QuantLib::DayCounter dc = getDayCounter(i4v[row]);
boost::shared_ptr<QuantLib::CalibrationHelper>
helper(new QuantLib::CapHelper(p, QuantLib::Handle<QuantLib::Quote>(vol), index,
getFrequency(i3v[row]),
dc,
(i5v[row]==1) ? true : false,
term));
boost::shared_ptr<QuantLib::BlackCapFloorEngine>
engine(new QuantLib::BlackCapFloorEngine(term, d2v[row]));
helper->setPricingEngine(engine);
caps.push_back(helper);
}
//set up the HullWhite model
boost::shared_ptr<QuantLib::HullWhite> model(new QuantLib::HullWhite(term));
//calibrate the data
QuantLib::LevenbergMarquardt optimizationMethod(1.0e-8,1.0e-8,1.0e-8);
QuantLib::EndCriteria endCriteria(10000, 100, 1e-6, 1e-8, 1e-8);
model->calibrate(caps, optimizationMethod, endCriteria);
//EndCriteria::Type ecType = model->endCriteria();
//return the result
QuantLib::Array xMinCalculated = model->params();
return Rcpp::List::create(Rcpp::Named("alpha") = xMinCalculated[0],
Rcpp::Named("sigma") = xMinCalculated[1]);
}
HullWhiteTimeDependentParameters calibration_hull_white(
const Date& evalDate,
const SwaptionVolData& volData
)
{
boost::shared_ptr<IborIndex> index = volData.index;
Frequency fixedFreq = volData.fixedFreq;
DayCounter fixedDC = volData.fixedDC;
DayCounter floatingDC = volData.floatingDC;
Real FixedA = volData.fixedA;
std::vector<Real> initialSigma = volData.initialSigma;
Settings::instance().evaluationDate() = Date( evalDate.serialNumber() );
Date today = Settings::instance().evaluationDate();
std::vector<Date> tmpdates;
for (Size i=0; i<volData.maturities.size(); ++i){
tmpdates.push_back(today+volData.maturities[i]);
}
tmpdates.back() = today+50*Years;
std::set<Date> dateSet( tmpdates.begin(), tmpdates.end() );
std::vector<Date> dates( dateSet.begin(), dateSet.end() );
Handle<YieldTermStructure> rts_hw(index->forwardingTermStructure().currentLink());
//boost::shared_ptr<GeneralizedHullWhite> model(new GeneralizedHullWhite(rts_hw, dates, dates, std::vector<Real>(1, FixedA), initialSigma));
boost::shared_ptr<Generalized_HullWhite> model(new Generalized_HullWhite(rts_hw, dates, initialSigma, FixedA));
//boost::shared_ptr<PricingEngine> engine_hw(new AnalyticCapFloorEngine(model));
//boost::shared_ptr<PricingEngine> engine_hw(new TreeSwaptionEngine(model, 100));
boost::shared_ptr<PricingEngine> engine_hw(new JamshidianSwaptionEngine(model));
std::vector<boost::shared_ptr<CalibrationHelper> > swaptions;
for (Size i=0; i<volData.maturities.size(); ++i) {
boost::shared_ptr<CalibrationHelper> helper(
new SwaptionHelper(volData.maturities[i],
volData.lengths[i],
Handle<Quote>(boost::shared_ptr<Quote>(new SimpleQuote(volData.vols[i]))),
index, Period(fixedFreq),
fixedDC, floatingDC,
rts_hw, CalibrationHelper::PriceError));
helper->setPricingEngine(engine_hw);
swaptions.push_back(helper);
}
LevenbergMarquardt optimizationMethod(1.0e-8,1.0e-8,1.0e-8);
EndCriteria endCriteria(50000, 1000, 1e-8, 1e-8, 1e-8);
model->calibrate(swaptions, optimizationMethod, endCriteria);
EndCriteria::Type ecType = model->endCriteria();
Array xMinCalculated = model->params();
//Real a = xMinCalculated[0];
//Real sigmar = xMinCalculated[1];
std::vector<Real> sigma;
for (Size i=0; i<xMinCalculated.size(); ++i) {
sigma.push_back(xMinCalculated[i]);
}
return HullWhiteTimeDependentParameters(FixedA, dates, sigma, model, swaptions);
}
SwaptionVolCube1::Cube
SwaptionVolCube1::sabrCalibration(const Cube& marketVolCube) const {
const std::vector<Time>& optionTimes = marketVolCube.optionTimes();
const std::vector<Time>& swapLengths = marketVolCube.swapLengths();
const std::vector<Date>& optionDates = marketVolCube.optionDates();
const std::vector<Period>& swapTenors = marketVolCube.swapTenors();
Matrix alphas(optionTimes.size(), swapLengths.size(),0.);
Matrix betas(alphas);
Matrix nus(alphas);
Matrix rhos(alphas);
Matrix forwards(alphas);
Matrix errors(alphas);
Matrix maxErrors(alphas);
Matrix endCriteria(alphas);
const std::vector<Matrix>& tmpMarketVolCube = marketVolCube.points();
std::vector<Real> strikes(strikeSpreads_.size());
std::vector<Real> volatilities(strikeSpreads_.size());
for (Size j=0; j<optionTimes.size(); j++) {
for (Size k=0; k<swapLengths.size(); k++) {
Rate atmForward = atmStrike(optionDates[j], swapTenors[k]);
strikes.clear();
volatilities.clear();
for (Size i=0; i<nStrikes_; i++){
Real strike = atmForward+strikeSpreads_[i];
if(strike>=MINSTRIKE) {
strikes.push_back(strike);
volatilities.push_back(tmpMarketVolCube[i][j][k]);
}
}
const std::vector<Real>& guess = parametersGuess_.operator()(
optionTimes[j], swapLengths[k]);
const boost::shared_ptr<SABRInterpolation> sabrInterpolation =
boost::shared_ptr<SABRInterpolation>(new
SABRInterpolation(strikes.begin(), strikes.end(),
volatilities.begin(),
optionTimes[j], atmForward,
guess[0], guess[1],
guess[2], guess[3],
isParameterFixed_[0],
isParameterFixed_[1],
isParameterFixed_[2],
isParameterFixed_[3],
vegaWeightedSmileFit_,
endCriteria_,
optMethod_,
errorAccept_,
useMaxError_,
maxGuesses_));
sabrInterpolation->update();
Real rmsError = sabrInterpolation->rmsError();
Real maxError = sabrInterpolation->maxError();
alphas [j][k] = sabrInterpolation->alpha();
betas [j][k] = sabrInterpolation->beta();
nus [j][k] = sabrInterpolation->nu();
rhos [j][k] = sabrInterpolation->rho();
forwards [j][k] = atmForward;
errors [j][k] = rmsError;
maxErrors [j][k] = maxError;
endCriteria[j][k] = sabrInterpolation->endCriteria();
QL_ENSURE(endCriteria[j][k]!=EndCriteria::MaxIterations,
"global swaptions calibration failed: "
"MaxIterations reached: " << "\n" <<
"option maturity = " << optionDates[j] << ", \n" <<
"swap tenor = " << swapTenors[k] << ", \n" <<
"error = " << io::rate(errors[j][k]) << ", \n" <<
"max error = " << io::rate(maxErrors[j][k]) << ", \n" <<
" alpha = " << alphas[j][k] << "n" <<
" beta = " << betas[j][k] << "\n" <<
" nu = " << nus[j][k] << "\n" <<
" rho = " << rhos[j][k] << "\n"
);
QL_ENSURE(useMaxError_ ? maxError : rmsError < maxErrorTolerance_,
"global swaptions calibration failed: "
"option tenor " << optionDates[j] <<
", swap tenor " << swapTenors[k] <<
(useMaxError_ ? ": max error " : ": error") <<
(useMaxError_ ? maxError : rmsError) <<
" alpha = " << alphas[j][k] << "n" <<
" beta = " << betas[j][k] << "\n" <<
" nu = " << nus[j][k] << "\n" <<
" rho = " << rhos[j][k] << "\n" <<
(useMaxError_ ? ": error" : ": max error ") <<
(useMaxError_ ? rmsError :maxError)
);
}
}
Cube sabrParametersCube(optionDates, swapTenors,
optionTimes, swapLengths, 8);
sabrParametersCube.setLayer(0, alphas);
sabrParametersCube.setLayer(1, betas);
sabrParametersCube.setLayer(2, nus);
sabrParametersCube.setLayer(3, rhos);
sabrParametersCube.setLayer(4, forwards);
sabrParametersCube.setLayer(5, errors);
sabrParametersCube.setLayer(6, maxErrors);
sabrParametersCube.setLayer(7, endCriteria);
return sabrParametersCube;
}
void LocalBootstrap<Curve>::calculate() const {
validCurve_ = false;
Size nInsts = ts_->instruments_.size();
// ensure rate helpers are sorted
std::sort(ts_->instruments_.begin(), ts_->instruments_.end(),
detail::BootstrapHelperSorter());
// check that there is no instruments with the same maturity
for (Size i=1; i<nInsts; ++i) {
Date m1 = ts_->instruments_[i-1]->latestDate(),
m2 = ts_->instruments_[i]->latestDate();
QL_REQUIRE(m1 != m2,
"two instruments have the same maturity ("<< m1 <<")");
}
// check that there is no instruments with invalid quote
for (Size i=0; i<nInsts; ++i)
QL_REQUIRE(ts_->instruments_[i]->quote()->isValid(),
io::ordinal(i+1) << " instrument (maturity: " <<
ts_->instruments_[i]->latestDate() <<
") has an invalid quote");
// setup instruments
for (Size i=0; i<nInsts; ++i) {
// don't try this at home!
// This call creates instruments, and removes "const".
// There is a significant interaction with observability.
ts_->instruments_[i]->setTermStructure(const_cast<Curve*>(ts_));
}
// set initial guess only if the current curve cannot be used as guess
if (validCurve_)
QL_ENSURE(ts_->data_.size() == nInsts+1,
"dimension mismatch: expected " << nInsts+1 <<
", actual " << ts_->data_.size());
else {
ts_->data_ = std::vector<Rate>(nInsts+1);
ts_->data_[0] = Traits::initialValue(ts_);
}
// calculate dates and times
ts_->dates_ = std::vector<Date>(nInsts+1);
ts_->times_ = std::vector<Time>(nInsts+1);
ts_->dates_[0] = Traits::initialDate(ts_);
ts_->times_[0] = ts_->timeFromReference(ts_->dates_[0]);
for (Size i=0; i<nInsts; ++i) {
ts_->dates_[i+1] = ts_->instruments_[i]->latestDate();
ts_->times_[i+1] = ts_->timeFromReference(ts_->dates_[i+1]);
if (!validCurve_)
ts_->data_[i+1] = ts_->data_[i];
}
LevenbergMarquardt solver(ts_->accuracy_,
ts_->accuracy_,
ts_->accuracy_);
EndCriteria endCriteria(100, 10, 0.00, ts_->accuracy_, 0.00);
PositiveConstraint posConstraint;
NoConstraint noConstraint;
Constraint& solverConstraint = forcePositive_ ?
static_cast<Constraint&>(posConstraint) :
static_cast<Constraint&>(noConstraint);
// now start the bootstrapping.
Size iInst = localisation_-1;
Size dataAdjust = Curve::interpolator_type::dataSizeAdjustment;
do {
Size initialDataPt = iInst+1-localisation_+dataAdjust;
Array startArray(localisation_+1-dataAdjust);
for (Size j = 0; j < startArray.size()-1; ++j)
startArray[j] = ts_->data_[initialDataPt+j];
// here we are extending the interpolation a point at a
// time... but the local interpolator can make an
// approximation for the final localisation period.
// e.g. if the localisation is 2, then the first section
// of the curve will be solved using the first 2
// instruments... with the local interpolator making
// suitable boundary conditions.
ts_->interpolation_ =
ts_->interpolator_.localInterpolate(
ts_->times_.begin(),
ts_->times_.begin()+(iInst + 2),
ts_->data_.begin(),
localisation_,
ts_->interpolation_,
nInsts+1);
if (iInst >= localisation_) {
startArray[localisation_-dataAdjust] =
Traits::guess(iInst, ts_, false, 0); // ?
} else {
startArray[localisation_-dataAdjust] = ts_->data_[0];
}
PenaltyFunction<Curve> currentCost(
ts_,
initialDataPt,
ts_->instruments_.begin() + (iInst - localisation_+1),
ts_->instruments_.begin() + (iInst+1));
Problem toSolve(currentCost, solverConstraint, startArray);
EndCriteria::Type endType = solver.minimize(toSolve, endCriteria);
// check the end criteria
QL_REQUIRE(endType == EndCriteria::StationaryFunctionAccuracy ||
endType == EndCriteria::StationaryFunctionValue,
"Unable to strip yieldcurve to required accuracy " );
++iInst;
} while ( iInst < nInsts );
validCurve_ = true;
}
