// to constrained <- from unconstrained
public AbcdCalibration(List <double> t,
List <double> blackVols,
double aGuess = -0.06,
double bGuess = 0.17,
double cGuess = 0.54,
double dGuess = 0.17,
bool aIsFixed = false,
bool bIsFixed = false,
bool cIsFixed = false,
bool dIsFixed = false,
bool vegaWeighted = false,
EndCriteria endCriteria = null,
OptimizationMethod method = null)
{
aIsFixed_ = aIsFixed;
bIsFixed_ = bIsFixed;
cIsFixed_ = cIsFixed;
dIsFixed_ = dIsFixed;
a_ = aGuess;
b_ = bGuess;
c_ = cGuess;
d_ = dGuess;
abcdEndCriteria_ = QLCore.EndCriteria.Type.None;
endCriteria_ = endCriteria;
optMethod_ = method;
weights_ = new InitializedList <double>(blackVols.Count, 1.0 / blackVols.Count);
vegaWeighted_ = vegaWeighted;
times_ = t;
blackVols_ = blackVols;
AbcdMathFunction.validate(aGuess, bGuess, cGuess, dGuess);
Utils.QL_REQUIRE(blackVols.Count == t.Count, () =>
"mismatch between number of times (" + t.Count + ") and blackVols (" + blackVols.Count + ")");
// if no optimization method or endCriteria is provided, we provide one
if (optMethod_ == null)
{
double epsfcn = 1.0e-8;
double xtol = 1.0e-8;
double gtol = 1.0e-8;
bool useCostFunctionsJacobian = false;
optMethod_ = new LevenbergMarquardt(epsfcn, xtol, gtol, useCostFunctionsJacobian);
}
if (endCriteria_ == null)
{
int maxIterations = 10000;
int maxStationaryStateIterations = 1000;
double rootEpsilon = 1.0e-8;
double functionEpsilon = 0.3e-4; // Why 0.3e-4 ?
double gradientNormEpsilon = 0.3e-4; // Why 0.3e-4 ?
endCriteria_ = new EndCriteria(maxIterations, maxStationaryStateIterations, rootEpsilon, functionEpsilon,
gradientNormEpsilon);
}
}
public void calculate()
{
validCurve_ = false;
int nInsts = ts_.instruments_.Count, i;
// ensure rate helpers are sorted
ts_.instruments_.Sort((x, y) => x.latestDate().CompareTo(y.latestDate()));
// check that there is no instruments with the same maturity
for (i = 1; i < nInsts; ++i)
{
Date m1 = ts_.instruments_[i - 1].latestDate(),
m2 = ts_.instruments_[i].latestDate();
Utils.QL_REQUIRE(m1 != m2, () => "two instruments have the same maturity (" + m1 + ")");
}
// check that there is no instruments with invalid quote
Utils.QL_REQUIRE((i = ts_.instruments_.FindIndex(x => !x.quoteIsValid())) == -1, () =>
"instrument " + i + " (maturity: " + ts_.instruments_[i].latestDate() + ") has an invalid quote");
// setup instruments and register with them
ts_.instruments_.ForEach((x, j) => ts_.setTermStructure(j));
// set initial guess only if the current curve cannot be used as guess
if (validCurve_)
{
Utils.QL_REQUIRE(ts_.data_.Count == nInsts + 1, () =>
"dimension mismatch: expected " + nInsts + 1 + ", actual " + ts_.data_.Count);
}
else
{
ts_.data_ = new InitializedList <double>(nInsts + 1);
ts_.data_[0] = ts_.initialValue();
}
// calculate dates and times
ts_.dates_ = new InitializedList <Date>(nInsts + 1);
ts_.times_ = new InitializedList <double>(nInsts + 1);
ts_.dates_[0] = ts_.initialDate();
ts_.times_[0] = ts_.timeFromReference(ts_.dates_[0]);
for (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 = new LevenbergMarquardt(ts_.accuracy_, ts_.accuracy_, ts_.accuracy_);
EndCriteria endCriteria = new EndCriteria(100, 10, 0.00, ts_.accuracy_, 0.00);
PositiveConstraint posConstraint = new PositiveConstraint();
NoConstraint noConstraint = new NoConstraint();
Constraint solverConstraint = forcePositive_ ? (Constraint)posConstraint : (Constraint)noConstraint;
// now start the bootstrapping.
int iInst = localisation_ - 1;
int dataAdjust = (ts_.interpolator_ as ConvexMonotone).dataSizeAdjustment;
do
{
int initialDataPt = iInst + 1 - localisation_ + dataAdjust;
Vector startArray = new Vector(localisation_ + 1 - dataAdjust);
for (int 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_ as ConvexMonotone).localInterpolate(ts_.times_, iInst + 2, ts_.data_,
localisation_, ts_.interpolation_ as ConvexMonotoneInterpolation, nInsts + 1);
if (iInst >= localisation_)
{
startArray[localisation_ - dataAdjust] = ts_.guess(iInst, ts_, false, 0);
}
else
{
startArray[localisation_ - dataAdjust] = ts_.data_[0];
}
var currentCost = new PenaltyFunction <T, U>(ts_, initialDataPt, ts_.instruments_,
iInst - localisation_ + 1, iInst + 1);
Problem toSolve = new Problem(currentCost, solverConstraint, startArray);
EndCriteria.Type endType = solver.minimize(toSolve, endCriteria);
// check the end criteria
Utils.QL_REQUIRE(endType == EndCriteria.Type.StationaryFunctionAccuracy ||
endType == EndCriteria.Type.StationaryFunctionValue, () =>
"Unable to strip yieldcurve to required accuracy ");
++iInst;
}while (iInst < nInsts);
validCurve_ = true;
}
