public Path(TimeGrid timeGrid, Vector values) {
timeGrid_ = timeGrid;
values_ = (Vector)values.Clone();
if (values_.empty())
values_ = new Vector(timeGrid_.size());
if (values_.size() != timeGrid_.size())
throw new ApplicationException("different number of times and asset values");
}
public Path(TimeGrid timeGrid, Vector values)
{
timeGrid_ = timeGrid;
values_ = (Vector)values.Clone();
if (values_.empty())
{
values_ = new Vector(timeGrid_.size());
}
if (values_.size() != timeGrid_.size())
{
throw new ApplicationException("different number of times and asset values");
}
}
public MultiPathGenerator(StochasticProcess process, TimeGrid times, GSG generator, bool brownianBridge)
{
brownianBridge_ = brownianBridge;
process_ = process;
generator_ = generator;
next_ = new Sample <IPath>(new MultiPath(process.size(), times), 1.0);
Utils.QL_REQUIRE(generator_.dimension() == process.factors() * (times.size() - 1), () =>
"dimension (" + generator_.dimension()
+ ") is not equal to ("
+ process.factors() + " * " + (times.size() - 1)
+ ") the number of factors "
+ "times the number of time steps");
Utils.QL_REQUIRE(times.size() > 1, () => "no times given");
}
public LongstaffSchwartzPathPricer(TimeGrid times, IEarlyExercisePathPricer <PathType, double> pathPricer,
YieldTermStructure termStructure)
{
calibrationPhase_ = true;
pathPricer_ = pathPricer;
coeff_ = new InitializedList <Vector>(times.size() - 1);
dF_ = new InitializedList <double>(times.size() - 1);
v_ = pathPricer_.basisSystem();
for (int i = 0; i < times.size() - 1; ++i)
{
dF_[i] = termStructure.discount(times[i + 1])
/ termStructure.discount(times[i]);
}
}
public override Lattice tree(TimeGrid grid) {
TermStructureFittingParameter phi = new TermStructureFittingParameter(termStructure());
ShortRateDynamics numericDynamics = new Dynamics(phi, a(), sigma());
TrinomialTree trinomial = new TrinomialTree(numericDynamics.process(), grid);
ShortRateTree numericTree = new ShortRateTree(trinomial, numericDynamics, grid);
TermStructureFittingParameter.NumericalImpl impl =
(TermStructureFittingParameter.NumericalImpl)phi.implementation();
impl.reset();
for (int i=0; i<(grid.size() - 1); i++) {
double discountBond = termStructure().link.discount(grid[i+1]);
Vector statePrices = numericTree.statePrices(i);
int size = numericTree.size(i);
double dt = numericTree.timeGrid().dt(i);
double dx = trinomial.dx(i);
double x = trinomial.underlying(i,0);
double value = 0.0;
for (int j=0; j<size; j++) {
value += statePrices[j]*Math.Exp(-x*dt);
x += dx;
}
value = Math.Log(value/discountBond)/dt;
impl.setvalue(grid[i], value);
}
return numericTree;
}
protected override IPathGenerator <IRNG> pathGenerator()
{
TimeGrid grid = timeGrid();
IRNG gen = new RNG().make_sequence_generator(grid.size() - 1, seed_);
return(new PathGenerator <IRNG>(process_, grid, gen, brownianBridge_));
}
public override Lattice tree(TimeGrid grid)
{
TermStructureFittingParameter phi = new TermStructureFittingParameter(termStructure());
ShortRateDynamics numericDynamics =
new Dynamics(phi, a(), sigma());
TrinomialTree trinomial =
new TrinomialTree(numericDynamics.process(), grid);
ShortRateTree numericTree =
new ShortRateTree(trinomial, numericDynamics, grid);
TermStructureFittingParameter.NumericalImpl impl =
(TermStructureFittingParameter.NumericalImpl)phi.implementation();
impl.reset();
double value = 1.0;
double vMin = -50.0;
double vMax = 50.0;
for (int i = 0; i < (grid.size() - 1); i++)
{
double discountBond = termStructure().link.discount(grid[i + 1]);
double xMin = trinomial.underlying(i, 0);
double dx = trinomial.dx(i);
Helper finder = new Helper(i, xMin, dx, discountBond, numericTree);
Brent s1d = new Brent();
s1d.setMaxEvaluations(1000);
value = s1d.solve(finder, 1e-7, value, vMin, vMax);
impl.setvalue(grid[i], value);
}
return(numericTree);
}
public override Lattice tree( TimeGrid grid)
{
TermStructureFittingParameter phi= new TermStructureFittingParameter(termStructure());
ShortRateDynamics numericDynamics=
new Dynamics(phi, a(), sigma());
TrinomialTree trinomial=
new TrinomialTree(numericDynamics.process(), grid);
ShortRateTree numericTree =
new ShortRateTree(trinomial, numericDynamics, grid);
TermStructureFittingParameter.NumericalImpl impl =
(TermStructureFittingParameter.NumericalImpl)phi.implementation();
impl.reset();
double value = 1.0;
double vMin = -50.0;
double vMax = 50.0;
for (int i=0; i<(grid.size() - 1); i++) {
double discountBond = termStructure().link.discount(grid[i+1]);
double xMin = trinomial.underlying(i, 0);
double dx = trinomial.dx(i);
Helper finder = new Helper(i, xMin, dx, discountBond, numericTree);
Brent s1d = new Brent();
s1d.setMaxEvaluations(1000);
value = s1d.solve(finder, 1e-7, value, vMin, vMax);
impl.setvalue(grid[i], value);
// vMin = value - 10.0;
// vMax = value + 10.0;
}
return numericTree;
}
public override Lattice tree(TimeGrid grid)
{
TermStructureFittingParameter phi = new TermStructureFittingParameter(termStructure());
ShortRateDynamics numericDynamics = new Dynamics(phi, a(), sigma());
TrinomialTree trinomial = new TrinomialTree(numericDynamics.process(), grid);
ShortRateTree numericTree = new ShortRateTree(trinomial, numericDynamics, grid);
TermStructureFittingParameter.NumericalImpl impl =
(TermStructureFittingParameter.NumericalImpl)phi.implementation();
impl.reset();
for (int i = 0; i < (grid.size() - 1); i++)
{
double discountBond = termStructure().link.discount(grid[i + 1]);
Vector statePrices = numericTree.statePrices(i);
int size = numericTree.size(i);
double dt = numericTree.timeGrid().dt(i);
double dx = trinomial.dx(i);
double x = trinomial.underlying(i, 0);
double value = 0.0;
for (int j = 0; j < size; j++)
{
value += statePrices[j] * Math.Exp(-x * dt);
x += dx;
}
value = Math.Log(value / discountBond) / dt;
impl.setvalue(grid[i], value);
}
return(numericTree);
}
//! Tree build-up + numerical fitting to term-structure
public ShortRateTree(TrinomialTree tree,
ShortRateDynamics dynamics,
TermStructureFittingParameter.NumericalImpl theta,
TimeGrid timeGrid)
: base(timeGrid, tree.size(1))
{
tree_ = tree;
dynamics_ = dynamics;
theta.reset();
double value = 1.0;
double vMin = -100.0;
double vMax = 100.0;
for (int i = 0; i < (timeGrid.size() - 1); i++)
{
double discountBond = theta.termStructure().link.discount(t_[i + 1]);
Helper finder = new Helper(i, discountBond, theta, this);
Brent s1d = new Brent();
s1d.setMaxEvaluations(1000);
value = s1d.solve(finder, 1e-7, value, vMin, vMax);
// vMin = value - 1.0;
// vMax = value + 1.0;
theta.change(value);
}
}
public PathGenerator(StochasticProcess process, TimeGrid timeGrid, GSG generator, bool brownianBridge)
{
brownianBridge_ = brownianBridge;
generator_ = generator;
dimension_ = generator_.dimension();
timeGrid_ = timeGrid;
process_ = process as StochasticProcess1D;
next_ = new Sample <IPath>(new Path(timeGrid_), 1.0);
temp_ = new InitializedList <double>(dimension_);
bb_ = new BrownianBridge(timeGrid_);
if (dimension_ != timeGrid_.size() - 1)
{
throw new Exception("sequence generator dimensionality (" + dimension_
+ ") != timeSteps (" + (timeGrid_.size() - 1) + ")");
}
}
public LatticeShortRateModelEngine(ShortRateModel model,
TimeGrid timeGrid)
: base(model)
{
timeGrid_ = new TimeGrid(timeGrid.Last(), timeGrid.size() - 1 /*timeGrid.dt(1) - timeGrid.dt(0)*/);
timeGrid_ = timeGrid;
timeSteps_ = 0;
lattice_ = this.model_.link.tree(timeGrid);
}
public MultiPathGenerator(StochasticProcess process, TimeGrid times, GSG generator, bool brownianBridge)
{
brownianBridge_ = brownianBridge;
process_ = process;
generator_ = generator;
next_ = new Sample <IPath>(new MultiPath(process.size(), times), 1.0);
if (generator_.dimension() != process.factors() * (times.size() - 1))
{
throw new Exception("dimension (" + generator_.dimension()
+ ") is not equal to ("
+ process.factors() + " * " + (times.size() - 1)
+ ") the number of factors "
+ "times the number of time steps");
}
if (!(times.size() > 1))
{
throw new Exception("no times given");
}
}
protected override PathPricer <IPath> pathPricer()
{
PlainVanillaPayoff payoff = arguments_.payoff as PlainVanillaPayoff;
Utils.QL_REQUIRE(payoff != null, () => "non-plain payoff given");
TimeGrid grid = timeGrid();
List <double> discounts = new InitializedList <double>(grid.size());
for (int i = 0; i < grid.size(); i++)
{
discounts[i] = process_.riskFreeRate().currentLink().discount(grid[i]);
}
// do this with template parameters?
if (isBiased_)
{
return(new BiasedBarrierPathPricer(arguments_.barrierType,
arguments_.barrier,
arguments_.rebate,
payoff.optionType(),
payoff.strike(),
discounts));
}
else
{
IRNG sequenceGen = new RandomSequenceGenerator <MersenneTwisterUniformRng>(grid.size() - 1, 5);
return(new BarrierPathPricer(arguments_.barrierType,
arguments_.barrier,
arguments_.rebate,
payoff.optionType(),
payoff.strike(),
discounts,
process_,
sequenceGen));
}
}
private void pathGenerator(UnderlyingInfo under)
{
ulong seed = 1;
int timeSteps = 365;
//
int dimensions = this.processArr_.factors();
double t = processArr_.time(maturity);
TimeGrid grid = new TimeGrid(t, timeSteps);
IRNG rndGenerator = (IRNG)new PseudoRandom().make_sequence_generator(dimensions * (grid.size() - 1), seed);
this.pathGenerator_ = new MultiPathGenerator<IRNG>(this.processArr_, grid, rndGenerator, false);
}
//! generic times
public BrownianBridge(TimeGrid timeGrid)
{
size_ = timeGrid.size() - 1;
t_ = new InitializedList <double>(size_);
sqrtdt_ = new InitializedList <double>(size_);
sqrtdt_ = new InitializedList <double>(size_);
bridgeIndex_ = new InitializedList <int>(size_);
leftIndex_ = new InitializedList <int>(size_);
rightIndex_ = new InitializedList <int>(size_);
leftWeight_ = new InitializedList <double>(size_);
rightWeight_ = new InitializedList <double>(size_);
stdDev_ = new InitializedList <double>(size_);
for (int i = 0; i < size_; ++i)
{
t_[i] = timeGrid[i + 1];
}
initialize();
}
public void testLambdaBootstrapping()
{
//"Testing caplet LMM lambda bootstrapping..."
//SavedSettings backup;
double tolerance = 1e-10;
double[] lambdaExpected = {14.3010297550, 19.3821411939, 15.9816590141,
15.9953118303, 14.0570815635, 13.5687599894,
12.7477197786, 13.7056638165, 11.6191989567};
LiborForwardModelProcess process = makeProcess();
Matrix covar = process.covariance(0.0, null, 1.0);
for (int i=0; i<9; ++i) {
double calculated = Math.Sqrt(covar[i+1,i+1]);
double expected = lambdaExpected[i]/100;
if (Math.Abs(calculated - expected) > tolerance)
Assert.Fail("Failed to reproduce expected lambda values"
+ "\n calculated: " + calculated
+ "\n expected: " + expected);
}
LfmCovarianceParameterization param = process.covarParam();
List<double> tmp = process.fixingTimes();
TimeGrid grid= new TimeGrid(tmp.Last(), 14);
for (int t=0; t<grid.size(); ++t) {
//verifier la presence du null
Matrix diff = param.integratedCovariance(grid[t],null)
- param.integratedCovariance(grid[t], null);
for (int i=0; i<diff.rows(); ++i)
{
for (int j=0; j<diff.columns(); ++j)
{
if (Math.Abs(diff[i,j]) > tolerance)
{
Assert.Fail("Failed to reproduce integrated covariance"
+ "\n calculated: " + diff[i,j]
+ "\n expected: " + 0);
}
}
}
}
}
public void testMonteCarloCapletPricing()
{
//"Testing caplet LMM Monte-Carlo caplet pricing..."
//SavedSettings backup;
/* factor loadings are taken from Hull & White article
plus extra normalisation to get orthogonal eigenvectors
http://www.rotman.utoronto.ca/~amackay/fin/libormktmodel2.pdf */
double[] compValues = {0.85549771, 0.46707264, 0.22353259,
0.91915359, 0.37716089, 0.11360610,
0.96438280, 0.26413316,-0.01412414,
0.97939148, 0.13492952,-0.15028753,
0.95970595,-0.00000000,-0.28100621,
0.97939148,-0.13492952,-0.15028753,
0.96438280,-0.26413316,-0.01412414,
0.91915359,-0.37716089, 0.11360610,
0.85549771,-0.46707264, 0.22353259};
Matrix volaComp=new Matrix(9,3);
List<double> lcompValues=new InitializedList<double>(27,0);
List<double> ltemp = new InitializedList<double>(3, 0);
lcompValues=compValues.ToList();
//std::copy(compValues, compValues+9*3, volaComp.begin());
for (int i = 0; i < 9; i++)
{
ltemp = lcompValues.GetRange(3*i, 3);
for (int j = 0; j < 3; j++)
volaComp[i, j] = ltemp[j];
}
LiborForwardModelProcess process1 = makeProcess();
LiborForwardModelProcess process2 = makeProcess(volaComp);
List<double> tmp = process1.fixingTimes();
TimeGrid grid=new TimeGrid(tmp ,12);
List<int> location=new List<int>();
for (int i=0; i < tmp.Count; ++i) {
location.Add(grid.index(tmp[i])) ;
}
// set-up a small Monte-Carlo simulation to price caplets
// and ratchet caps using a one- and a three factor libor market model
ulong seed = 42;
LowDiscrepancy.icInstance = new InverseCumulativeNormal();
IRNG rsg1 = (IRNG)new LowDiscrepancy().make_sequence_generator(
process1.factors()*(grid.size()-1), seed);
IRNG rsg2 = (IRNG)new LowDiscrepancy().make_sequence_generator(
process2.factors()*(grid.size()-1), seed);
MultiPathGenerator<IRNG> generator1=new MultiPathGenerator<IRNG> (process1, grid, rsg1, false);
MultiPathGenerator<IRNG> generator2=new MultiPathGenerator<IRNG> (process2, grid, rsg2, false);
const int nrTrails = 250000;
List<GeneralStatistics> stat1 = new InitializedList<GeneralStatistics>(process1.size());
List<GeneralStatistics> stat2 = new InitializedList<GeneralStatistics>(process2.size());
List<GeneralStatistics> stat3 = new InitializedList<GeneralStatistics>(process2.size() - 1);
for (int i=0; i<nrTrails; ++i) {
Sample<MultiPath> path1 = generator1.next();
Sample<MultiPath> path2 = generator2.next();
List<double> rates1=new InitializedList<double>(len);
List<double> rates2 = new InitializedList<double>(len);
for (int j=0; j<process1.size(); ++j) {
rates1[j] = path1.value[j][location[j]];
rates2[j] = path2.value[j][location[j]];
}
List<double> dis1 = process1.discountBond(rates1);
List<double> dis2 = process2.discountBond(rates2);
for (int k=0; k<process1.size(); ++k) {
double accrualPeriod = process1.accrualEndTimes()[k]
- process1.accrualStartTimes()[k];
// caplet payoff function, cap rate at 4%
double payoff1 = Math.Max(rates1[k] - 0.04, 0.0) * accrualPeriod;
double payoff2 = Math.Max(rates2[k] - 0.04, 0.0) * accrualPeriod;
stat1[k].add(dis1[k] * payoff1);
stat2[k].add(dis2[k] * payoff2);
if (k != 0) {
// ratchet cap payoff function
double payoff3 = Math.Max(rates2[k] - (rates2[k-1]+0.0025), 0.0)
* accrualPeriod;
stat3[k-1].add(dis2[k] * payoff3);
}
}
}
double[] capletNpv = {0.000000000000, 0.000002841629, 0.002533279333,
0.009577143571, 0.017746502618, 0.025216116835,
0.031608230268, 0.036645683881, 0.039792254012,
0.041829864365};
double[] ratchetNpv = {0.0082644895, 0.0082754754, 0.0082159966,
0.0082982822, 0.0083803357, 0.0084366961,
0.0084173270, 0.0081803406, 0.0079533814};
for (int k=0; k < process1.size(); ++k) {
double calculated1 = stat1[k].mean();
double tolerance1 = stat1[k].errorEstimate();
double expected = capletNpv[k];
if (Math.Abs(calculated1 - expected) > tolerance1) {
Assert.Fail("Failed to reproduce expected caplet NPV"
+ "\n calculated: " + calculated1
+ "\n error int: " + tolerance1
+ "\n expected: " + expected);
}
double calculated2 = stat2[k].mean();
double tolerance2 = stat2[k].errorEstimate();
if (Math.Abs(calculated2 - expected) > tolerance2) {
Assert.Fail("Failed to reproduce expected caplet NPV"
+ "\n calculated: " + calculated2
+ "\n error int: " + tolerance2
+ "\n expected: " + expected);
}
if (k != 0) {
double calculated3 = stat3[k-1].mean();
double tolerance3 = stat3[k-1].errorEstimate();
expected = ratchetNpv[k-1];
double refError = 1e-5; // 1e-5. error bars of the reference values
if (Math.Abs(calculated3 - expected) > tolerance3 + refError) {
Assert.Fail("Failed to reproduce expected caplet NPV"
+ "\n calculated: " + calculated3
+ "\n error int: " + tolerance3 + refError
+ "\n expected: " + expected);
}
}
}
}
public int length()
{
return(timeGrid_.size());
}
protected override IPathGenerator <IRNG> controlPathGenerator()
{
int dimensions = process_.factors();
TimeGrid grid = this.timeGrid();
IRNG generator = (IRNG) new RNG().make_sequence_generator(dimensions * (grid.size() - 1), this.seed_);
HybridHestonHullWhiteProcess process = process_ as HybridHestonHullWhiteProcess;
Utils.QL_REQUIRE(process != null, () => "invalid process");
HybridHestonHullWhiteProcess cvProcess = new HybridHestonHullWhiteProcess(process.hestonProcess(),
process.hullWhiteProcess(), 0.0, process.discretization());
return(new MultiPathGenerator <IRNG>(cvProcess, grid, generator, false));
}
//! generic times
public BrownianBridge(TimeGrid timeGrid) {
size_ = timeGrid.size()-1;
t_ = new InitializedList<double>(size_);
sqrtdt_ = new InitializedList<double>(size_);
sqrtdt_ = new InitializedList<double>(size_);
bridgeIndex_ = new InitializedList<int>(size_);
leftIndex_ = new InitializedList<int>(size_);
rightIndex_ = new InitializedList<int>(size_);
leftWeight_ = new InitializedList<double>(size_);
rightWeight_ = new InitializedList<double>(size_);
stdDev_ = new InitializedList<double>(size_);
for (int i=0; i<size_; ++i)
t_[i] = timeGrid[i+1];
initialize();
}
public void testSwaptionPricing()
{
//"Testing forward swap and swaption pricing...");
//SavedSettings backup;
const int size = 10;
const int steps = 8*size;
#if QL_USE_INDEXED_COUPON
const double tolerance = 1e-6;
#else
const double tolerance = 1e-12;
#endif
List<Date> dates = new List<Date>();
List<double> rates = new List<double>();
dates.Add(new Date(4,9,2005));
dates.Add(new Date(4,9,2011));
rates.Add(0.04);
rates.Add(0.08);
IborIndex index = makeIndex(dates, rates);
LiborForwardModelProcess process = new LiborForwardModelProcess(size, index);
LmCorrelationModel corrModel = new LmExponentialCorrelationModel(size, 0.5);
LmVolatilityModel volaModel = new LmLinearExponentialVolatilityModel(process.fixingTimes(),
0.291, 1.483, 0.116, 0.00001);
// set-up pricing engine
process.setCovarParam((LfmCovarianceParameterization)
new LfmCovarianceProxy(volaModel, corrModel));
// set-up a small Monte-Carlo simulation to price swations
List<double> tmp = process.fixingTimes();
TimeGrid grid=new TimeGrid(tmp ,steps);
List<int> location=new List<int>();
for (int i=0; i < tmp.Count; ++i) {
location.Add(grid.index(tmp[i])) ;
}
ulong seed=42;
const int nrTrails = 5000;
LowDiscrepancy.icInstance = new InverseCumulativeNormal();
IRNG rsg = (InverseCumulativeRsg<RandomSequenceGenerator<MersenneTwisterUniformRng>
,InverseCumulativeNormal>)
new PseudoRandom().make_sequence_generator(process.factors()*(grid.size()-1),seed);
MultiPathGenerator<IRNG> generator=new MultiPathGenerator<IRNG>(process,
grid,
rsg, false);
LiborForwardModel liborModel = new LiborForwardModel(process, volaModel, corrModel);
Calendar calendar = index.fixingCalendar();
DayCounter dayCounter = index.forwardingTermStructure().link.dayCounter();
BusinessDayConvention convention = index.businessDayConvention();
Date settlement = index.forwardingTermStructure().link.referenceDate();
SwaptionVolatilityMatrix m = liborModel.getSwaptionVolatilityMatrix();
for (int i=1; i < size; ++i) {
for (int j=1; j <= size-i; ++j) {
Date fwdStart = settlement + new Period(6*i, TimeUnit.Months);
Date fwdMaturity = fwdStart + new Period(6*j, TimeUnit.Months);
Schedule schedule =new Schedule(fwdStart, fwdMaturity, index.tenor(), calendar,
convention, convention, DateGeneration.Rule.Forward, false);
double swapRate = 0.0404;
VanillaSwap forwardSwap = new VanillaSwap(VanillaSwap.Type.Receiver, 1.0,
schedule, swapRate, dayCounter,
schedule, index, 0.0, index.dayCounter());
forwardSwap.setPricingEngine(new DiscountingSwapEngine(index.forwardingTermStructure()));
// check forward pricing first
double expected = forwardSwap.fairRate();
double calculated = liborModel.S_0(i-1,i+j-1);
if (Math.Abs(expected - calculated) > tolerance)
Assert.Fail("Failed to reproduce fair forward swap rate"
+ "\n calculated: " + calculated
+ "\n expected: " + expected);
swapRate = forwardSwap.fairRate();
forwardSwap =
new VanillaSwap(VanillaSwap.Type.Receiver, 1.0,
schedule, swapRate, dayCounter,
schedule, index, 0.0, index.dayCounter());
forwardSwap.setPricingEngine(new DiscountingSwapEngine(index.forwardingTermStructure()));
if (i == j && i<=size/2) {
IPricingEngine engine =
new LfmSwaptionEngine(liborModel, index.forwardingTermStructure());
Exercise exercise =
new EuropeanExercise(process.fixingDates()[i]);
Swaption swaption =
new Swaption(forwardSwap, exercise);
swaption.setPricingEngine(engine);
GeneralStatistics stat = new GeneralStatistics();
for (int n=0; n<nrTrails; ++n) {
Sample<MultiPath> path = (n%2!=0) ? generator.antithetic()
: generator.next();
//Sample<MultiPath> path = generator.next();
List<double> rates_ = new InitializedList<double>(size);
for (int k=0; k<process.size(); ++k) {
rates_[k] = path.value[k][location[i]];
}
List<double> dis = process.discountBond(rates_);
double npv=0.0;
for (int k=i; k < i+j; ++k) {
npv += (swapRate - rates_[k])
* ( process.accrualEndTimes()[k]
- process.accrualStartTimes()[k])*dis[k];
}
stat.add(Math.Max(npv, 0.0));
}
if (Math.Abs(swaption.NPV() - stat.mean())
> stat.errorEstimate()*2.35)
Assert.Fail("Failed to reproduce swaption npv"
+ "\n calculated: " + stat.mean()
+ "\n expected: " + swaption.NPV());
}
}
}
}
public override void calculate()
{
// this is an european option pricer
Utils.QL_REQUIRE(arguments_.exercise.type() == Exercise.Type.European, () => "not an European option");
// plain vanilla
PlainVanillaPayoff payoff = arguments_.payoff as PlainVanillaPayoff;
Utils.QL_REQUIRE(payoff != null, () => "non-striked payoff given");
double v0 = model_.link.v0();
double spotPrice = model_.link.s0();
Utils.QL_REQUIRE(spotPrice > 0.0, () => "negative or null underlying given");
double strike = payoff.strike();
double term = model_.link.riskFreeRate().link.dayCounter().yearFraction(
model_.link.riskFreeRate().link.referenceDate(), arguments_.exercise.lastDate());
double riskFreeDiscount = model_.link.riskFreeRate().link.discount(arguments_.exercise.lastDate());
double dividendDiscount = model_.link.dividendYield().link.discount(arguments_.exercise.lastDate());
//average values
TimeGrid timeGrid = model_.link.timeGrid();
int n = timeGrid.size() - 1;
double kappaAvg = 0.0, thetaAvg = 0.0, sigmaAvg = 0.0, rhoAvg = 0.0;
for (int i = 1; i <= n; ++i)
{
double t = 0.5 * (timeGrid[i - 1] + timeGrid[i]);
kappaAvg += model_.link.kappa(t);
thetaAvg += model_.link.theta(t);
sigmaAvg += model_.link.sigma(t);
rhoAvg += model_.link.rho(t);
}
kappaAvg /= n;
thetaAvg /= n;
sigmaAvg /= n;
rhoAvg /= n;
double c_inf = Math.Min(10.0, Math.Max(0.0001,
Math.Sqrt(1.0 - Math.Pow(rhoAvg, 2)) / sigmaAvg)) * (v0 + kappaAvg * thetaAvg * term);
double p1 = integration_.calculate(c_inf,
new Fj_Helper(model_, term, strike, 1).value) / Const.M_PI;
double p2 = integration_.calculate(c_inf,
new Fj_Helper(model_, term, strike, 2).value) / Const.M_PI;
switch (payoff.optionType())
{
case Option.Type.Call:
results_.value = spotPrice * dividendDiscount * (p1 + 0.5)
- strike * riskFreeDiscount * (p2 + 0.5);
break;
case Option.Type.Put:
results_.value = spotPrice * dividendDiscount * (p1 - 0.5)
- strike * riskFreeDiscount * (p2 - 0.5);
break;
default:
Utils.QL_FAIL("unknown option type");
break;
}
}
