@Override
public void update() {
// forward_ might have changed
QL.require(
forward_ > 0.0,
"at the money forward rate must be " + "positive: " + forward_ + " not allowed");
// we should also check that y contains positive values only
// we must update weights if it is vegaWeighted
if (vegaWeighted_) {
// itsCoeffs.weights_.clear();
double weightsSum = 0.0;
for (int i = 0; i < vx.size(); i++) {
final double x = vx.get(i);
final double y = vy.get(i);
final double stdDev = Math.sqrt(y * y * itsCoeffs.t_);
itsCoeffs.weights_.set(i, BlackFormula.blackFormulaStdDevDerivative(x, forward_, stdDev));
weightsSum += itsCoeffs.weights_.get(i);
}
// weight normalization
for (int i = 0; i < itsCoeffs.weights_.size(); i++) {
itsCoeffs.weights_.set(i, itsCoeffs.weights_.get(i) / weightsSum);
}
}
// there is nothing to optimize
if (itsCoeffs.alphaIsFixed_
&& itsCoeffs.betaIsFixed_
&& itsCoeffs.nuIsFixed_
&& itsCoeffs.rhoIsFixed_) {
itsCoeffs.error_ = interpolationError();
itsCoeffs.maxError_ = interpolationMaxError();
itsCoeffs.SABREndCriteria_ = EndCriteria.Type.None;
return;
} else {
final SABRError costFunction = new SABRError(this);
transformation_ = new SabrParametersTransformation();
final Array guess = new Array(4);
guess.set(0, itsCoeffs.alpha_);
guess.set(1, itsCoeffs.beta_);
guess.set(2, itsCoeffs.nu_);
guess.set(3, itsCoeffs.rho_);
final boolean[] parameterAreFixed = new boolean[4];
parameterAreFixed[0] = itsCoeffs.alphaIsFixed_;
parameterAreFixed[1] = itsCoeffs.betaIsFixed_;
parameterAreFixed[2] = itsCoeffs.nuIsFixed_;
parameterAreFixed[3] = itsCoeffs.rhoIsFixed_;
final Array inversedTransformatedGuess = new Array(transformation_.inverse(guess));
final ProjectedCostFunction constrainedSABRError =
new ProjectedCostFunction(costFunction, inversedTransformatedGuess, parameterAreFixed);
final Array projectedGuess =
new Array(constrainedSABRError.project(inversedTransformatedGuess));
final NoConstraint constraint = new NoConstraint();
final Problem problem = new Problem(constrainedSABRError, constraint, projectedGuess);
itsCoeffs.SABREndCriteria_ = optMethod_.minimize(problem, endCriteria_);
final Array projectedResult = new Array(problem.currentValue());
final Array transfResult = new Array(constrainedSABRError.include(projectedResult));
final Array result = transformation_.direct(transfResult);
itsCoeffs.alpha_ = result.get(0);
itsCoeffs.beta_ = result.get(1);
itsCoeffs.nu_ = result.get(2);
itsCoeffs.rho_ = result.get(3);
}
itsCoeffs.error_ = interpolationError();
itsCoeffs.maxError_ = interpolationMaxError();
}
