public static GeneralizedBlackScholesProcess clone(GeneralizedBlackScholesProcess process, SimpleQuote volQuote) {
Handle<Quote> stateVariable = process.stateVariable();
Handle<YieldTermStructure> dividendYield = process.dividendYield();
Handle<YieldTermStructure> riskFreeRate = process.riskFreeRate();
Handle<BlackVolTermStructure> blackVol = process.blackVolatility();
var volatility = new Handle<BlackVolTermStructure>(new BlackConstantVol(blackVol.link.referenceDate(),
blackVol.link.calendar(), new Handle<Quote>(volQuote),
blackVol.link.dayCounter()));
return new GeneralizedBlackScholesProcess(stateVariable, dividendYield, riskFreeRate, volatility);
}
public static GeneralizedBlackScholesProcess clone(GeneralizedBlackScholesProcess process, SimpleQuote volQuote)
{
Handle <Quote> stateVariable = process.stateVariable();
Handle <YieldTermStructure> dividendYield = process.dividendYield();
Handle <YieldTermStructure> riskFreeRate = process.riskFreeRate();
Handle <BlackVolTermStructure> blackVol = process.blackVolatility();
var volatility = new Handle <BlackVolTermStructure>(new BlackConstantVol(blackVol.link.referenceDate(),
blackVol.link.calendar(), new Handle <Quote>(volQuote),
blackVol.link.dayCounter()));
return(new GeneralizedBlackScholesProcess(stateVariable, dividendYield, riskFreeRate, volatility));
}
// McSimulation implementation
protected override TimeGrid timeGrid()
{
Date referenceDate = process_.riskFreeRate().link.referenceDate();
DayCounter voldc = process_.blackVolatility().link.dayCounter();
List <double> fixingTimes = new InitializedList <double>(arguments_.fixingDates.Count);
for (int i = 0; i < arguments_.fixingDates.Count; i++)
{
if (arguments_.fixingDates[i] >= referenceDate)
{
double t = voldc.yearFraction(referenceDate,
arguments_.fixingDates[i]);
fixingTimes.Add(t);
}
}
// handle here maxStepsPerYear
return(new TimeGrid(fixingTimes.Last(), fixingTimes.Count));
}
public FdmBlackScholesOp(FdmMesher mesher,
GeneralizedBlackScholesProcess bsProcess,
double strike,
bool localVol = false,
double?illegalLocalVolOverwrite = null,
int direction = 0)
{
mesher_ = mesher;
rTS_ = bsProcess.riskFreeRate().currentLink();
qTS_ = bsProcess.dividendYield().currentLink();
volTS_ = bsProcess.blackVolatility().currentLink();
localVol_ = (localVol) ? bsProcess.localVolatility().currentLink()
: null;
x_ = (localVol) ? new Vector(Vector.Exp(mesher.locations(direction))) : null;
dxMap_ = new FirstDerivativeOp(direction, mesher);
dxxMap_ = new SecondDerivativeOp(direction, mesher);
mapT_ = new TripleBandLinearOp(direction, mesher);
strike_ = strike;
illegalLocalVolOverwrite_ = illegalLocalVolOverwrite;
direction_ = direction;
}
protected void setGridLimits(double center, double t)
{
Utils.QL_REQUIRE(center > 0.0, () => "negative or null underlying given");
Utils.QL_REQUIRE(t > 0.0, () => "negative or zero residual time");
center_ = center;
int newGridPoints = safeGridPoints(gridPoints_, t);
if (newGridPoints > intrinsicValues_.size())
{
intrinsicValues_ = new SampledCurve(newGridPoints);
}
double volSqrtTime = Math.Sqrt(process_.blackVolatility().link.blackVariance(t, center_));
// the prefactor fine tunes performance at small volatilities
double prefactor = 1.0 + 0.02 / volSqrtTime;
double minMaxFactor = Math.Exp(4.0 * prefactor * volSqrtTime);
sMin_ = center_ / minMaxFactor; // underlying grid min value
sMax_ = center_ * minMaxFactor; // underlying grid max value
}
public override void calculate()
{
Utils.QL_REQUIRE(arguments_.exercise.type() == Exercise.Type.European, () => "not an European Option");
StrikedTypePayoff payoff = arguments_.payoff as StrikedTypePayoff;
Utils.QL_REQUIRE(payoff != null, () => "not an European Option");
double variance = process_.blackVolatility().link.blackVariance(arguments_.exercise.lastDate(), payoff.strike());
double dividendDiscount = process_.dividendYield().link.discount(arguments_.exercise.lastDate());
double riskFreeDiscount = process_.riskFreeRate().link.discount(arguments_.exercise.lastDate());
double drift = Math.Log(dividendDiscount / riskFreeDiscount) - 0.5 * variance;
Integrand f = new Integrand(arguments_.payoff, process_.stateVariable().link.value(), drift, variance);
SegmentIntegral integrator = new SegmentIntegral(5000);
double infinity = 10.0 * Math.Sqrt(variance);
results_.value = process_.riskFreeRate().link.discount(arguments_.exercise.lastDate()) /
Math.Sqrt(2.0 * Math.PI * variance) *
integrator.value(f.value, drift - infinity, drift + infinity);
}
public override void calculate()
{
DayCounter rfdc = process_.riskFreeRate().link.dayCounter();
DayCounter divdc = process_.dividendYield().link.dayCounter();
DayCounter voldc = process_.blackVolatility().link.dayCounter();
Calendar volcal = process_.blackVolatility().link.calendar();
double s0 = process_.stateVariable().link.value();
if (!(s0 > 0.0))
{
throw new ApplicationException("negative or null underlying given");
}
double v = process_.blackVolatility().link.blackVol(arguments_.exercise.lastDate(), s0);
Date maturityDate = arguments_.exercise.lastDate();
double r = process_.riskFreeRate().link.zeroRate(maturityDate, rfdc, Compounding.Continuous, Frequency.NoFrequency).rate();
double q = process_.dividendYield().link.zeroRate(maturityDate, divdc, Compounding.Continuous, Frequency.NoFrequency).rate();
Date referenceDate = process_.riskFreeRate().link.referenceDate();
// binomial trees with constant coefficient
var flatRiskFree = new Handle <YieldTermStructure>(new FlatForward(referenceDate, r, rfdc));
var flatDividends = new Handle <YieldTermStructure>(new FlatForward(referenceDate, q, divdc));
var flatVol = new Handle <BlackVolTermStructure>(new BlackConstantVol(referenceDate, volcal, v, voldc));
PlainVanillaPayoff payoff = arguments_.payoff as PlainVanillaPayoff;
if (payoff == null)
{
throw new ApplicationException("non-plain payoff given");
}
double maturity = rfdc.yearFraction(referenceDate, maturityDate);
StochasticProcess1D bs =
new GeneralizedBlackScholesProcess(process_.stateVariable(), flatDividends, flatRiskFree, flatVol);
TimeGrid grid = new TimeGrid(maturity, timeSteps_);
T tree = new T().factory(bs, maturity, timeSteps_, payoff.strike());
BlackScholesLattice <T> lattice = new BlackScholesLattice <T>(tree, r, maturity, timeSteps_);
DiscretizedVanillaOption option = new DiscretizedVanillaOption(arguments_, process_, grid);
option.initialize(lattice, maturity);
// Partial derivatives calculated from various points in the
// binomial tree (Odegaard)
// Rollback to third-last step, and get underlying price (s2) &
// option values (p2) at this point
option.rollback(grid[2]);
Vector va2 = new Vector(option.values());
if (!(va2.size() == 3))
{
throw new ApplicationException("Expect 3 nodes in grid at second step");
}
double p2h = va2[2]; // high-price
double s2 = lattice.underlying(2, 2); // high price
// Rollback to second-last step, and get option value (p1) at
// this point
option.rollback(grid[1]);
Vector va = new Vector(option.values());
if (!(va.size() == 2))
{
throw new ApplicationException("Expect 2 nodes in grid at first step");
}
double p1 = va[1];
// Finally, rollback to t=0
option.rollback(0.0);
double p0 = option.presentValue();
double s1 = lattice.underlying(1, 1);
// Calculate partial derivatives
double delta0 = (p1 - p0) / (s1 - s0); // dp/ds
double delta1 = (p2h - p1) / (s2 - s1); // dp/ds
// Store results
results_.value = p0;
results_.delta = delta0;
results_.gamma = 2.0 * (delta1 - delta0) / (s2 - s0); //d(delta)/ds
results_.theta = Utils.blackScholesTheta(process_,
results_.value.GetValueOrDefault(),
results_.delta.GetValueOrDefault(),
results_.gamma.GetValueOrDefault());
}
private double volatility()
{
return(process_.blackVolatility().link.blackVol(residualTime(), strike()));
}
public override void calculate()
{
/* this engine cannot really check for the averageType==Geometric
* since it can be used as control variate for the Arithmetic version
* QL_REQUIRE(arguments_.averageType == Average::Geometric,"not a geometric average option")
*/
Utils.QL_REQUIRE(arguments_.exercise.type() == Exercise.Type.European, () => "not an European Option");
double runningLog;
int pastFixings;
if (arguments_.averageType == Average.Type.Geometric)
{
Utils.QL_REQUIRE(arguments_.runningAccumulator > 0.0, () =>
"positive running product required: " + arguments_.runningAccumulator + " not allowed");
runningLog = Math.Log(arguments_.runningAccumulator.GetValueOrDefault());
pastFixings = arguments_.pastFixings.GetValueOrDefault();
}
else
{ // it is being used as control variate
runningLog = 1.0;
pastFixings = 0;
}
PlainVanillaPayoff payoff = arguments_.payoff as PlainVanillaPayoff;
Utils.QL_REQUIRE(payoff != null, () => "non-plain payoff given");
Date referenceDate = process_.riskFreeRate().link.referenceDate();
DayCounter rfdc = process_.riskFreeRate().link.dayCounter();
DayCounter divdc = process_.dividendYield().link.dayCounter();
DayCounter voldc = process_.blackVolatility().link.dayCounter();
List <double> fixingTimes = new List <double>();
int i;
for (i = 0; i < arguments_.fixingDates.Count; i++)
{
if (arguments_.fixingDates[i] >= referenceDate)
{
double t = voldc.yearFraction(referenceDate, arguments_.fixingDates[i]);
fixingTimes.Add(t);
}
}
int remainingFixings = fixingTimes.Count;
int numberOfFixings = pastFixings + remainingFixings;
double N = numberOfFixings;
double pastWeight = pastFixings / N;
double futureWeight = 1.0 - pastWeight;
double timeSum = 0;
fixingTimes.ForEach((ii, vv) => timeSum += fixingTimes[ii]);
double vola = process_.blackVolatility().link.blackVol(arguments_.exercise.lastDate(), payoff.strike());
double temp = 0.0;
for (i = pastFixings + 1; i < numberOfFixings; i++)
{
temp += fixingTimes[i - pastFixings - 1] * (N - i);
}
double variance = vola * vola / N / N * (timeSum + 2.0 * temp);
double dsigG_dsig = Math.Sqrt((timeSum + 2.0 * temp)) / N;
double sigG = vola * dsigG_dsig;
double dmuG_dsig = -(vola * timeSum) / N;
Date exDate = arguments_.exercise.lastDate();
double dividendRate = process_.dividendYield().link.
zeroRate(exDate, divdc, Compounding.Continuous, Frequency.NoFrequency).rate();
double riskFreeRate = process_.riskFreeRate().link.
zeroRate(exDate, rfdc, Compounding.Continuous, Frequency.NoFrequency).rate();
double nu = riskFreeRate - dividendRate - 0.5 * vola * vola;
double s = process_.stateVariable().link.value();
Utils.QL_REQUIRE(s > 0.0, () => "positive underlying value required");
int M = (pastFixings == 0 ? 1 : pastFixings);
double muG = pastWeight * runningLog / M + futureWeight * Math.Log(s) + nu * timeSum / N;
double forwardPrice = Math.Exp(muG + variance / 2.0);
double riskFreeDiscount = process_.riskFreeRate().link.discount(arguments_.exercise.lastDate());
BlackCalculator black = new BlackCalculator(payoff, forwardPrice, Math.Sqrt(variance), riskFreeDiscount);
results_.value = black.value();
results_.delta = futureWeight * black.delta(forwardPrice) * forwardPrice / s;
results_.gamma = forwardPrice * futureWeight / (s * s)
* (black.gamma(forwardPrice) * futureWeight * forwardPrice
- pastWeight * black.delta(forwardPrice));
double Nx_1, nx_1;
CumulativeNormalDistribution CND = new CumulativeNormalDistribution();
NormalDistribution ND = new NormalDistribution();
if (sigG > Const.QL_EPSILON)
{
double x_1 = (muG - Math.Log(payoff.strike()) + variance) / sigG;
Nx_1 = CND.value(x_1);
nx_1 = ND.value(x_1);
}
else
{
Nx_1 = (muG > Math.Log(payoff.strike()) ? 1.0 : 0.0);
nx_1 = 0.0;
}
results_.vega = forwardPrice * riskFreeDiscount *
((dmuG_dsig + sigG * dsigG_dsig) * Nx_1 + nx_1 * dsigG_dsig);
if (payoff.optionType() == Option.Type.Put)
{
results_.vega -= riskFreeDiscount * forwardPrice *
(dmuG_dsig + sigG * dsigG_dsig);
}
double tRho = rfdc.yearFraction(process_.riskFreeRate().link.referenceDate(),
arguments_.exercise.lastDate());
results_.rho = black.rho(tRho) * timeSum / (N * tRho)
- (tRho - timeSum / N) * results_.value;
double tDiv = divdc.yearFraction(
process_.dividendYield().link.referenceDate(),
arguments_.exercise.lastDate());
results_.dividendRho = black.dividendRho(tDiv) * timeSum / (N * tDiv);
results_.strikeSensitivity = black.strikeSensitivity();
results_.theta = Utils.blackScholesTheta(process_,
results_.value.GetValueOrDefault(),
results_.delta.GetValueOrDefault(),
results_.gamma.GetValueOrDefault());
}
public override void calculate()
{
if (arguments_.averageType != Average.Type.Geometric)
{
throw new ApplicationException("not a geometric average option");
}
if (arguments_.exercise.type() != Exercise.Type.European)
{
throw new ApplicationException("not an European Option");
}
Date exercise = arguments_.exercise.lastDate();
PlainVanillaPayoff payoff = (PlainVanillaPayoff)(arguments_.payoff);
if (payoff == null)
{
throw new ApplicationException("non-plain payoff given");
}
double volatility =
process_.blackVolatility().link.blackVol(exercise, payoff.strike());
double variance =
process_.blackVolatility().link.blackVariance(exercise,
payoff.strike());
double riskFreeDiscount =
process_.riskFreeRate().link.discount(exercise);
DayCounter rfdc = process_.riskFreeRate().link.dayCounter();
DayCounter divdc = process_.dividendYield().link.dayCounter();
DayCounter voldc = process_.blackVolatility().link.dayCounter();
double dividendYield = 0.5 * (
process_.riskFreeRate().link.zeroRate(exercise, rfdc,
Compounding.Continuous,
Frequency.NoFrequency).rate() +
process_.dividendYield().link.zeroRate(exercise, divdc,
Compounding.Continuous,
Frequency.NoFrequency).rate() +
volatility * volatility / 6.0);
double t_q = divdc.yearFraction(
process_.dividendYield().link.referenceDate(), exercise);
double dividendDiscount = Math.Exp(-dividendYield * t_q);
double spot = process_.stateVariable().link.value();
if (!(spot > 0.0))
{
throw new ApplicationException("negative or null underlying");
}
double forward = spot * dividendDiscount / riskFreeDiscount;
BlackCalculator black = new BlackCalculator(payoff,
forward,
Math.Sqrt(variance / 3.0),
riskFreeDiscount);
results_.value = black.value();
results_.delta = black.delta(spot);
results_.gamma = black.gamma(spot);
results_.dividendRho = black.dividendRho(t_q) / 2.0;
double t_r = rfdc.yearFraction(process_.riskFreeRate().link.referenceDate(),
arguments_.exercise.lastDate());
results_.rho = black.rho(t_r) + 0.5 * black.dividendRho(t_q);
double t_v = voldc.yearFraction(
process_.blackVolatility().link.referenceDate(),
arguments_.exercise.lastDate());
results_.vega = black.vega(t_v) / Math.Sqrt(3.0) +
black.dividendRho(t_q) * volatility / 6.0;
try{
results_.theta = black.theta(spot, t_v);
}
catch (System.Exception /*Error*/) {
results_.theta = double.NaN; //Null<Real>();
}
}
public override void calculate()
{
Utils.QL_REQUIRE(arguments_.exercise.type() == Exercise.Type.American, () => "not an American Option");
AmericanExercise ex = arguments_.exercise as AmericanExercise;
Utils.QL_REQUIRE(ex != null, () => "non-American exercise given");
Utils.QL_REQUIRE(!ex.payoffAtExpiry(), () => "payoff at expiry not handled");
StrikedTypePayoff payoff = arguments_.payoff as StrikedTypePayoff;
Utils.QL_REQUIRE(payoff != null, () => "non-striked payoff given");
double variance = process_.blackVolatility().link.blackVariance(ex.lastDate(), payoff.strike());
double dividendDiscount = process_.dividendYield().link.discount(ex.lastDate());
double riskFreeDiscount = process_.riskFreeRate().link.discount(ex.lastDate());
double spot = process_.stateVariable().link.value();
Utils.QL_REQUIRE(spot > 0.0, () => "negative or null underlying given");
double forwardPrice = spot * dividendDiscount / riskFreeDiscount;
BlackCalculator black = new BlackCalculator(payoff, forwardPrice, Math.Sqrt(variance), riskFreeDiscount);
if (dividendDiscount >= 1.0 && payoff.optionType() == Option.Type.Call)
{
// early exercise never optimal
results_.value = black.value();
results_.delta = black.delta(spot);
results_.deltaForward = black.deltaForward();
results_.elasticity = black.elasticity(spot);
results_.gamma = black.gamma(spot);
DayCounter rfdc = process_.riskFreeRate().link.dayCounter();
DayCounter divdc = process_.dividendYield().link.dayCounter();
DayCounter voldc = process_.blackVolatility().link.dayCounter();
double t = rfdc.yearFraction(process_.riskFreeRate().link.referenceDate(), arguments_.exercise.lastDate());
results_.rho = black.rho(t);
t = divdc.yearFraction(process_.dividendYield().link.referenceDate(), arguments_.exercise.lastDate());
results_.dividendRho = black.dividendRho(t);
t = voldc.yearFraction(process_.blackVolatility().link.referenceDate(), arguments_.exercise.lastDate());
results_.vega = black.vega(t);
results_.theta = black.theta(spot, t);
results_.thetaPerDay = black.thetaPerDay(spot, t);
results_.strikeSensitivity = black.strikeSensitivity();
results_.itmCashProbability = black.itmCashProbability();
}
else
{
// early exercise can be optimal
CumulativeNormalDistribution cumNormalDist = new CumulativeNormalDistribution();
NormalDistribution normalDist = new NormalDistribution();
double tolerance = 1e-6;
double Sk = BaroneAdesiWhaleyApproximationEngine.criticalPrice(payoff, riskFreeDiscount, dividendDiscount,
variance, tolerance);
double forwardSk = Sk * dividendDiscount / riskFreeDiscount;
double alpha = -2.0 * Math.Log(riskFreeDiscount) / (variance);
double beta = 2.0 * Math.Log(dividendDiscount / riskFreeDiscount) / (variance);
double h = 1 - riskFreeDiscount;
double phi = 0;
switch (payoff.optionType())
{
case Option.Type.Call:
phi = 1;
break;
case Option.Type.Put:
phi = -1;
break;
default:
Utils.QL_FAIL("invalid option type");
break;
}
//it can throw: to be fixed
// FLOATING_POINT_EXCEPTION
double temp_root = Math.Sqrt((beta - 1) * (beta - 1) + (4 * alpha) / h);
double lambda = (-(beta - 1) + phi * temp_root) / 2;
double lambda_prime = -phi * alpha / (h * h * temp_root);
double black_Sk = Utils.blackFormula(payoff.optionType(), payoff.strike(), forwardSk, Math.Sqrt(variance)) *
riskFreeDiscount;
double hA = phi * (Sk - payoff.strike()) - black_Sk;
double d1_Sk = (Math.Log(forwardSk / payoff.strike()) + 0.5 * variance) / Math.Sqrt(variance);
double d2_Sk = d1_Sk - Math.Sqrt(variance);
double part1 = forwardSk * normalDist.value(d1_Sk) / (alpha * Math.Sqrt(variance));
double part2 = -phi *forwardSk *cumNormalDist.value(phi *d1_Sk) * Math.Log(dividendDiscount) /
Math.Log(riskFreeDiscount);
double part3 = +phi *payoff.strike() * cumNormalDist.value(phi * d2_Sk);
double V_E_h = part1 + part2 + part3;
double b = (1 - h) * alpha * lambda_prime / (2 * (2 * lambda + beta - 1));
double c = -((1 - h) * alpha / (2 * lambda + beta - 1)) *
(V_E_h / (hA) + 1 / h + lambda_prime / (2 * lambda + beta - 1));
double temp_spot_ratio = Math.Log(spot / Sk);
double chi = temp_spot_ratio * (b * temp_spot_ratio + c);
if (phi * (Sk - spot) > 0)
{
results_.value = black.value() + hA * Math.Pow((spot / Sk), lambda) / (1 - chi);
}
else
{
results_.value = phi * (spot - payoff.strike());
}
double temp_chi_prime = (2 * b / spot) * Math.Log(spot / Sk);
double chi_prime = temp_chi_prime + c / spot;
double chi_double_prime = 2 * b / (spot * spot) - temp_chi_prime / spot - c / (spot * spot);
results_.delta = phi * dividendDiscount * cumNormalDist.value(phi * d1_Sk) +
(lambda / (spot * (1 - chi)) + chi_prime / ((1 - chi) * (1 - chi))) *
(phi * (Sk - payoff.strike()) - black_Sk) * Math.Pow((spot / Sk), lambda);
results_.gamma = phi * dividendDiscount * normalDist.value(phi * d1_Sk) / (spot * Math.Sqrt(variance)) +
(2 * lambda * chi_prime / (spot * (1 - chi) * (1 - chi)) +
2 * chi_prime * chi_prime / ((1 - chi) * (1 - chi) * (1 - chi)) +
chi_double_prime / ((1 - chi) * (1 - chi)) +
lambda * (1 - lambda) / (spot * spot * (1 - chi))) *
(phi * (Sk - payoff.strike()) - black_Sk) * Math.Pow((spot / Sk), lambda);
} // end of "early exercise can be optimal"
}
public override void calculate()
{
if (!(arguments_.exercise.type() == Exercise.Type.American))
{
throw new ApplicationException("not an American Option");
}
AmericanExercise ex = arguments_.exercise as AmericanExercise;
if (ex == null)
{
throw new ApplicationException("non-American exercise given");
}
if (ex.payoffAtExpiry())
{
throw new ApplicationException("payoff at expiry not handled");
}
StrikedTypePayoff payoff = arguments_.payoff as StrikedTypePayoff;
if (payoff == null)
{
throw new ApplicationException("non-striked payoff given");
}
double variance = process_.blackVolatility().link.blackVariance(ex.lastDate(), payoff.strike());
double dividendDiscount = process_.dividendYield().link.discount(ex.lastDate());
double riskFreeDiscount = process_.riskFreeRate().link.discount(ex.lastDate());
double spot = process_.stateVariable().link.value();
if (!(spot > 0.0))
{
throw new ApplicationException("negative or null underlying given");
}
double forwardPrice = spot * dividendDiscount / riskFreeDiscount;
BlackCalculator black = new BlackCalculator(payoff, forwardPrice, Math.Sqrt(variance), riskFreeDiscount);
if (dividendDiscount >= 1.0 && payoff.optionType() == Option.Type.Call)
{
// early exercise never optimal
results_.value = black.value();
results_.delta = black.delta(spot);
results_.deltaForward = black.deltaForward();
results_.elasticity = black.elasticity(spot);
results_.gamma = black.gamma(spot);
DayCounter rfdc = process_.riskFreeRate().link.dayCounter();
DayCounter divdc = process_.dividendYield().link.dayCounter();
DayCounter voldc = process_.blackVolatility().link.dayCounter();
double t = rfdc.yearFraction(process_.riskFreeRate().link.referenceDate(), arguments_.exercise.lastDate());
results_.rho = black.rho(t);
t = divdc.yearFraction(process_.dividendYield().link.referenceDate(), arguments_.exercise.lastDate());
results_.dividendRho = black.dividendRho(t);
t = voldc.yearFraction(process_.blackVolatility().link.referenceDate(), arguments_.exercise.lastDate());
results_.vega = black.vega(t);
results_.theta = black.theta(spot, t);
results_.thetaPerDay = black.thetaPerDay(spot, t);
results_.strikeSensitivity = black.strikeSensitivity();
results_.itmCashProbability = black.itmCashProbability();
}
else
{
// early exercise can be optimal
CumulativeNormalDistribution cumNormalDist = new CumulativeNormalDistribution();
double tolerance = 1e-6;
double Sk = criticalPrice(payoff, riskFreeDiscount,
dividendDiscount, variance, tolerance);
double forwardSk = Sk * dividendDiscount / riskFreeDiscount;
double d1 = (Math.Log(forwardSk / payoff.strike()) + 0.5 * variance)
/ Math.Sqrt(variance);
double n = 2.0 * Math.Log(dividendDiscount / riskFreeDiscount) / variance;
double K = -2.0 * Math.Log(riskFreeDiscount) /
(variance * (1.0 - riskFreeDiscount));
double Q, a;
switch (payoff.optionType())
{
case Option.Type.Call:
Q = (-(n - 1.0) + Math.Sqrt(((n - 1.0) * (n - 1.0)) + 4.0 * K)) / 2.0;
a = (Sk / Q) * (1.0 - dividendDiscount * cumNormalDist.value(d1));
if (spot < Sk)
{
results_.value = black.value() +
a * Math.Pow((spot / Sk), Q);
}
else
{
results_.value = spot - payoff.strike();
}
break;
case Option.Type.Put:
Q = (-(n - 1.0) - Math.Sqrt(((n - 1.0) * (n - 1.0)) + 4.0 * K)) / 2.0;
a = -(Sk / Q) *
(1.0 - dividendDiscount * cumNormalDist.value(-d1));
if (spot > Sk)
{
results_.value = black.value() +
a * Math.Pow((spot / Sk), Q);
}
else
{
results_.value = payoff.strike() - spot;
}
break;
default:
throw new ApplicationException("unknown option type");
}
} // end of "early exercise can be optimal"
}
public override void calculate()
{
if (!(arguments_.exercise.type() == Exercise.Type.American))
{
throw new ApplicationException("not an American Option");
}
AmericanExercise ex = arguments_.exercise as AmericanExercise;
if (ex == null)
{
throw new ApplicationException("non-American exercise given");
}
if (ex.payoffAtExpiry())
{
throw new ApplicationException("payoff at expiry not handled");
}
PlainVanillaPayoff payoff = arguments_.payoff as PlainVanillaPayoff;
if (payoff == null)
{
throw new ApplicationException("non-plain payoff given");
}
double variance = process_.blackVolatility().link.blackVariance(ex.lastDate(), payoff.strike());
double dividendDiscount = process_.dividendYield().link.discount(ex.lastDate());
double riskFreeDiscount = process_.riskFreeRate().link.discount(ex.lastDate());
double spot = process_.stateVariable().link.value();
if (!(spot > 0.0))
{
throw new ApplicationException("negative or null underlying given");
}
double strike = payoff.strike();
if (payoff.optionType() == Option.Type.Put)
{
// use put-call simmetry
Utils.swap <double>(ref spot, ref strike);
Utils.swap <double>(ref riskFreeDiscount, ref dividendDiscount);
payoff = new PlainVanillaPayoff(Option.Type.Call, strike);
}
if (dividendDiscount >= 1.0)
{
// early exercise is never optimal - use Black formula
double forwardPrice = spot * dividendDiscount / riskFreeDiscount;
BlackCalculator black = new BlackCalculator(payoff, forwardPrice, Math.Sqrt(variance), riskFreeDiscount);
results_.value = black.value();
results_.delta = black.delta(spot);
results_.deltaForward = black.deltaForward();
results_.elasticity = black.elasticity(spot);
results_.gamma = black.gamma(spot);
DayCounter rfdc = process_.riskFreeRate().link.dayCounter();
DayCounter divdc = process_.dividendYield().link.dayCounter();
DayCounter voldc = process_.blackVolatility().link.dayCounter();
double t = rfdc.yearFraction(process_.riskFreeRate().link.referenceDate(), arguments_.exercise.lastDate());
results_.rho = black.rho(t);
t = divdc.yearFraction(process_.dividendYield().link.referenceDate(), arguments_.exercise.lastDate());
results_.dividendRho = black.dividendRho(t);
t = voldc.yearFraction(process_.blackVolatility().link.referenceDate(), arguments_.exercise.lastDate());
results_.vega = black.vega(t);
results_.theta = black.theta(spot, t);
results_.thetaPerDay = black.thetaPerDay(spot, t);
results_.strikeSensitivity = black.strikeSensitivity();
results_.itmCashProbability = black.itmCashProbability();
}
else
{
// early exercise can be optimal - use approximation
results_.value = americanCallApproximation(spot, strike, riskFreeDiscount, dividendDiscount, variance);
}
}
public FdmBlackScholesMesher(int size,
GeneralizedBlackScholesProcess process,
double maturity, double strike,
double?xMinConstraint = null,
double?xMaxConstraint = null,
double eps = 0.0001,
double scaleFactor = 1.5,
Pair <double?, double?> cPoint
= null,
DividendSchedule dividendSchedule = null,
FdmQuantoHelper fdmQuantoHelper = null,
double spotAdjustment = 0.0)
: base(size)
{
double S = process.x0();
Utils.QL_REQUIRE(S > 0.0, () => "negative or null underlying given");
dividendSchedule = dividendSchedule == null ? new DividendSchedule() : dividendSchedule;
List <pair_double> intermediateSteps = new List <pair_double>();
for (int i = 0; i < dividendSchedule.Count &&
process.time(dividendSchedule[i].date()) <= maturity; ++i)
{
intermediateSteps.Add(
new pair_double(
process.time(dividendSchedule[i].date()),
dividendSchedule[i].amount()
));
}
int intermediateTimeSteps = (int)Math.Max(2, 24.0 * maturity);
for (int i = 0; i < intermediateTimeSteps; ++i)
{
intermediateSteps.Add(
new pair_double((i + 1) * (maturity / intermediateTimeSteps), 0.0));
}
intermediateSteps.Sort();
Handle <YieldTermStructure> rTS = process.riskFreeRate();
Handle <YieldTermStructure> qTS = fdmQuantoHelper != null
? new Handle <YieldTermStructure>(
new QuantoTermStructure(process.dividendYield(),
process.riskFreeRate(),
new Handle <YieldTermStructure>(fdmQuantoHelper.foreignTermStructure()),
process.blackVolatility(),
strike,
new Handle <BlackVolTermStructure>(fdmQuantoHelper.fxVolatilityTermStructure()),
fdmQuantoHelper.exchRateATMlevel(),
fdmQuantoHelper.equityFxCorrelation()))
: process.dividendYield();
double lastDivTime = 0.0;
double fwd = S + spotAdjustment;
double mi = fwd, ma = fwd;
for (int i = 0; i < intermediateSteps.Count; ++i)
{
double divTime = intermediateSteps[i].first;
double divAmount = intermediateSteps[i].second;
fwd = fwd / rTS.currentLink().discount(divTime) * rTS.currentLink().discount(lastDivTime)
* qTS.currentLink().discount(divTime) / qTS.currentLink().discount(lastDivTime);
mi = Math.Min(mi, fwd); ma = Math.Max(ma, fwd);
fwd -= divAmount;
mi = Math.Min(mi, fwd); ma = Math.Max(ma, fwd);
lastDivTime = divTime;
}
// Set the grid boundaries
double normInvEps = new InverseCumulativeNormal().value(1 - eps);
double sigmaSqrtT
= process.blackVolatility().currentLink().blackVol(maturity, strike)
* Math.Sqrt(maturity);
double?xMin = Math.Log(mi) - sigmaSqrtT * normInvEps * scaleFactor;
double?xMax = Math.Log(ma) + sigmaSqrtT * normInvEps * scaleFactor;
if (xMinConstraint != null)
{
xMin = xMinConstraint;
}
if (xMaxConstraint != null)
{
xMax = xMaxConstraint;
}
Fdm1dMesher helper;
if (cPoint != null &&
cPoint.first != null &&
Math.Log(cPoint.first.Value) >= xMin && Math.Log(cPoint.first.Value) <= xMax)
{
helper = new Concentrating1dMesher(xMin.Value, xMax.Value, size,
new Pair <double?, double?>(Math.Log(cPoint.first.Value), cPoint.second));
}
else
{
helper = new Uniform1dMesher(xMin.Value, xMax.Value, size);
}
locations_ = helper.locations();
for (int i = 0; i < locations_.Count; ++i)
{
dplus_[i] = helper.dplus(i);
dminus_[i] = helper.dminus(i);
}
}