public override void calculate(IPricingEngineResults r)
{
OneAssetOption.Results results = r as OneAssetOption.Results;
setGridLimits();
initializeInitialCondition();
initializeOperator();
initializeBoundaryConditions();
initializeStepCondition();
List <IOperator> operatorSet = new List <IOperator>();
List <Vector> arraySet = new List <Vector>();
BoundaryConditionSet bcSet = new BoundaryConditionSet();
StepConditionSet <Vector> conditionSet = new StepConditionSet <Vector>();
prices_ = (SampledCurve)intrinsicValues_.Clone();
controlPrices_ = (SampledCurve)intrinsicValues_.Clone();
controlOperator_ = (TridiagonalOperator)finiteDifferenceOperator_.Clone();
controlBCs_[0] = BCs_[0];
controlBCs_[1] = BCs_[1];
operatorSet.Add(finiteDifferenceOperator_);
operatorSet.Add(controlOperator_);
arraySet.Add(prices_.values());
arraySet.Add(controlPrices_.values());
bcSet.Add(BCs_);
bcSet.Add(controlBCs_);
conditionSet.Add(stepCondition_);
conditionSet.Add(new NullCondition <Vector>());
var model = new FiniteDifferenceModel <ParallelEvolver <CrankNicolson <TridiagonalOperator> > >(operatorSet, bcSet);
object temp = arraySet;
model.rollback(ref temp, getResidualTime(), 0.0, timeSteps_, conditionSet);
arraySet = (List <Vector>)temp;
prices_.setValues(arraySet[0]);
controlPrices_.setValues(arraySet[1]);
StrikedTypePayoff striked_payoff = payoff_ as StrikedTypePayoff;
Utils.QL_REQUIRE(striked_payoff != null, () => "non-striked payoff given");
double variance = process_.blackVolatility().link.blackVariance(exerciseDate_, striked_payoff.strike());
double dividendDiscount = process_.dividendYield().link.discount(exerciseDate_);
double riskFreeDiscount = process_.riskFreeRate().link.discount(exerciseDate_);
double spot = process_.stateVariable().link.value();
double forwardPrice = spot * dividendDiscount / riskFreeDiscount;
BlackCalculator black = new BlackCalculator(striked_payoff, forwardPrice, Math.Sqrt(variance), riskFreeDiscount);
results.value = prices_.valueAtCenter()
- controlPrices_.valueAtCenter()
+ black.value();
results.delta = prices_.firstDerivativeAtCenter()
- controlPrices_.firstDerivativeAtCenter()
+ black.delta(spot);
results.gamma = prices_.secondDerivativeAtCenter()
- controlPrices_.secondDerivativeAtCenter()
+ black.gamma(spot);
results.additionalResults["priceCurve"] = prices_;
}
protected virtual void initializeModel()
{
model_ = new FiniteDifferenceModel <CrankNicolson <TridiagonalOperator> >(finiteDifferenceOperator_, BCs_);
}
public void rollback(ref object a,
double from, double to,
int steps, int dampingSteps)
{
double deltaT = from - to;
int allSteps = steps + dampingSteps;
double dampingTo = from - (deltaT * dampingSteps) / allSteps;
if (dampingSteps > 0 &&
schemeDesc_.type != FdmSchemeDesc.FdmSchemeType.ImplicitEulerType)
{
ImplicitEulerScheme implicitEvolver = new ImplicitEulerScheme(map_, bcSet_);
FiniteDifferenceModel <ImplicitEulerScheme> dampingModel
= new FiniteDifferenceModel <ImplicitEulerScheme>(implicitEvolver, condition_.stoppingTimes());
dampingModel.rollback(ref a, from, dampingTo,
dampingSteps, condition_);
}
switch (schemeDesc_.type)
{
case FdmSchemeDesc.FdmSchemeType.HundsdorferType:
{
HundsdorferScheme hsEvolver = new HundsdorferScheme(schemeDesc_.theta, schemeDesc_.mu,
map_, bcSet_);
FiniteDifferenceModel <HundsdorferScheme>
hsModel = new FiniteDifferenceModel <HundsdorferScheme>(hsEvolver, condition_.stoppingTimes());
hsModel.rollback(ref a, dampingTo, to, steps, condition_);
}
break;
case FdmSchemeDesc.FdmSchemeType.DouglasType:
{
DouglasScheme dsEvolver = new DouglasScheme(schemeDesc_.theta, map_, bcSet_);
FiniteDifferenceModel <DouglasScheme>
dsModel = new FiniteDifferenceModel <DouglasScheme>(dsEvolver, condition_.stoppingTimes());
dsModel.rollback(ref a, dampingTo, to, steps, condition_);
}
break;
case FdmSchemeDesc.FdmSchemeType.CrankNicolsonType:
{
CrankNicolsonScheme cnEvolver = new CrankNicolsonScheme(schemeDesc_.theta, map_, bcSet_);
FiniteDifferenceModel <CrankNicolsonScheme>
cnModel = new FiniteDifferenceModel <CrankNicolsonScheme>(cnEvolver, condition_.stoppingTimes());
cnModel.rollback(ref a, dampingTo, to, steps, condition_);
}
break;
case FdmSchemeDesc.FdmSchemeType.CraigSneydType:
{
CraigSneydScheme csEvolver = new CraigSneydScheme(schemeDesc_.theta, schemeDesc_.mu,
map_, bcSet_);
FiniteDifferenceModel <CraigSneydScheme>
csModel = new FiniteDifferenceModel <CraigSneydScheme>(csEvolver, condition_.stoppingTimes());
csModel.rollback(ref a, dampingTo, to, steps, condition_);
}
break;
case FdmSchemeDesc.FdmSchemeType.ModifiedCraigSneydType:
{
ModifiedCraigSneydScheme csEvolver = new ModifiedCraigSneydScheme(schemeDesc_.theta,
schemeDesc_.mu,
map_, bcSet_);
FiniteDifferenceModel <ModifiedCraigSneydScheme>
mcsModel = new FiniteDifferenceModel <ModifiedCraigSneydScheme>(csEvolver, condition_.stoppingTimes());
mcsModel.rollback(ref a, dampingTo, to, steps, condition_);
}
break;
case FdmSchemeDesc.FdmSchemeType.ImplicitEulerType:
{
ImplicitEulerScheme implicitEvolver = new ImplicitEulerScheme(map_, bcSet_);
FiniteDifferenceModel <ImplicitEulerScheme>
implicitModel = new FiniteDifferenceModel <ImplicitEulerScheme>(implicitEvolver, condition_.stoppingTimes());
implicitModel.rollback(ref a, from, to, allSteps, condition_);
}
break;
case FdmSchemeDesc.FdmSchemeType.ExplicitEulerType:
{
ExplicitEulerScheme explicitEvolver = new ExplicitEulerScheme(map_, bcSet_);
FiniteDifferenceModel <ExplicitEulerScheme>
explicitModel = new FiniteDifferenceModel <ExplicitEulerScheme>(explicitEvolver, condition_.stoppingTimes());
explicitModel.rollback(ref a, dampingTo, to, steps, condition_);
}
break;
case FdmSchemeDesc.FdmSchemeType.MethodOfLinesType:
{
MethodOfLinesScheme methodOfLines = new MethodOfLinesScheme(schemeDesc_.theta, schemeDesc_.mu, map_, bcSet_);
FiniteDifferenceModel <MethodOfLinesScheme>
molModel = new FiniteDifferenceModel <MethodOfLinesScheme>(methodOfLines, condition_.stoppingTimes());
molModel.rollback(ref a, dampingTo, to, steps, condition_);
}
break;
case FdmSchemeDesc.FdmSchemeType.TrBDF2Type:
{
FdmSchemeDesc trDesc = new FdmSchemeDesc().CraigSneyd();
CraigSneydScheme hsEvolver = new CraigSneydScheme(trDesc.theta, trDesc.mu, map_, bcSet_);
TrBDF2Scheme <CraigSneydScheme> trBDF2 = new TrBDF2Scheme <CraigSneydScheme>(
schemeDesc_.theta, map_, hsEvolver, bcSet_, schemeDesc_.mu);
FiniteDifferenceModel <TrBDF2Scheme <CraigSneydScheme> >
trBDF2Model = new FiniteDifferenceModel <TrBDF2Scheme <CraigSneydScheme> >(trBDF2, condition_.stoppingTimes());
trBDF2Model.rollback(ref a, dampingTo, to, steps, condition_);
}
break;
default:
Utils.QL_FAIL("Unknown scheme type");
break;
}
}
