/*! Roll back an asset until the given time, performing any needed adjustment. */ public abstract void rollback(DiscretizedAsset a, double to);
/*! Roll back an asset until the given time, but do not perform
the final adjustment.
\warning In version 0.3.7 and earlier, this method was called rollAlmostBack method and performed
pre-adjustment. This is no longer true; when migrating your code, you'll have to replace calls
such as:
\code
method->rollAlmostBack(asset,t);
\endcode
with the two statements:
\code
method->partialRollback(asset,t);
asset->preAdjustValues();
\endcode
*/
public abstract void partialRollback(DiscretizedAsset a, double to);
//! computes the present value of an asset. public abstract double presentValue(DiscretizedAsset a);
//! Computes the present value of an asset using Arrow-Debrew prices
public override double presentValue(DiscretizedAsset asset)
{
int i = t_.index(asset.time());
return(Vector.DotProduct(asset.values(), statePrices(i)));
}
/*! \name Numerical method interface
These methods are to be used by discretized assets and
must be overridden by developers implementing numerical
methods. Users are advised to use the corresponding
methods of DiscretizedAsset instead.
@{
*/
//! initialize an asset at the given time.
public abstract void initialize(DiscretizedAsset a, double time);
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();
Utils.QL_REQUIRE(s0 > 0.0, () => "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).value();
double q = process_.dividendYield().link.zeroRate(maturityDate, divdc, Compounding.Continuous, Frequency.NoFrequency).value();
Date referenceDate = process_.riskFreeRate().link.referenceDate();
// binomial trees with constant coefficient
Handle <YieldTermStructure> flatRiskFree = new Handle <YieldTermStructure>(new FlatForward(referenceDate, r, rfdc));
Handle <YieldTermStructure> flatDividends = new Handle <YieldTermStructure>(new FlatForward(referenceDate, q, divdc));
Handle <BlackVolTermStructure> flatVol = new Handle <BlackVolTermStructure>(new BlackConstantVol(referenceDate, volcal, v, voldc));
StrikedTypePayoff payoff = arguments_.payoff as StrikedTypePayoff;
Utils.QL_REQUIRE(payoff != null, () => "non-striked payoff given");
double maturity = rfdc.yearFraction(referenceDate, maturityDate);
StochasticProcess1D bs = new GeneralizedBlackScholesProcess(process_.stateVariable(),
flatDividends, flatRiskFree, flatVol);
// correct timesteps to ensure a (local) minimum, using Boyle and Lau
// approach. See Journal of Derivatives, 1/1994,
// "Bumping up against the barrier with the binomial method"
// Note: this approach works only for CoxRossRubinstein lattices, so
// is disabled if T is not a CoxRossRubinstein or derived from it.
int optimum_steps = timeSteps_;
if (maxTimeSteps_ > timeSteps_ && s0 > 0 && arguments_.barrier > 0) // boost::is_base_of<CoxRossRubinstein, T>::value &&
{
double divisor;
if (s0 > arguments_.barrier)
{
divisor = Math.Pow(Math.Log(s0 / arguments_.barrier.Value), 2);
}
else
{
divisor = Math.Pow(Math.Log(arguments_.barrier.Value / s0), 2);
}
if (!Utils.close(divisor, 0))
{
for (int i = 1; i < timeSteps_; ++i)
{
int optimum = (int)((i * i * v * v * maturity) / divisor);
if (timeSteps_ < optimum)
{
optimum_steps = optimum;
break; // found first minimum with iterations>=timesteps
}
}
}
if (optimum_steps > maxTimeSteps_)
{
optimum_steps = maxTimeSteps_; // too high, limit
}
}
TimeGrid grid = new TimeGrid(maturity, optimum_steps);
ITree tree = getTree_(bs, maturity, optimum_steps, payoff.strike());
BlackScholesLattice <ITree> lattice = new BlackScholesLattice <ITree>(tree, r, maturity, optimum_steps);
DiscretizedAsset option = getAsset_(arguments_, process_, grid);
option.initialize(lattice, maturity);
// Partial derivatives calculated from various points in the
// binomial tree
// (see J.C.Hull, "Options, Futures and other derivatives", 6th edition, pp 397/398)
// Rollback to third-last step, and get underlying prices (s2) &
// option values (p2) at this point
option.rollback(grid[2]);
Vector va2 = new Vector(option.values());
Utils.QL_REQUIRE(va2.size() == 3, () => "Expect 3 nodes in grid at second step");
double p2u = va2[2]; // up
double p2m = va2[1]; // mid
double p2d = va2[0]; // down (low)
double s2u = lattice.underlying(2, 2); // up price
double s2m = lattice.underlying(2, 1); // middle price
double s2d = lattice.underlying(2, 0); // down (low) price
// calculate gamma by taking the first derivate of the two deltas
double delta2u = (p2u - p2m) / (s2u - s2m);
double delta2d = (p2m - p2d) / (s2m - s2d);
double gamma = (delta2u - delta2d) / ((s2u - s2d) / 2);
// Rollback to second-last step, and get option values (p1) at
// this point
option.rollback(grid[1]);
Vector va = new Vector(option.values());
Utils.QL_REQUIRE(va.size() == 2, () => "Expect 2 nodes in grid at first step");
double p1u = va[1];
double p1d = va[0];
double s1u = lattice.underlying(1, 1); // up (high) price
double s1d = lattice.underlying(1, 0); // down (low) price
double delta = (p1u - p1d) / (s1u - s1d);
// Finally, rollback to t=0
option.rollback(0.0);
double p0 = option.presentValue();
// Store results
results_.value = p0;
results_.delta = delta;
results_.gamma = gamma;
// theta can be approximated by calculating the numerical derivative
// between mid value at third-last step and at t0. The underlying price
// is the same, only time varies.
results_.theta = (p2m - p0) / grid[2];
}
public DiscretizedOption(DiscretizedAsset underlying, Exercise.Type exerciseType, List <double> exerciseTimes)
{
underlying_ = underlying;
exerciseType_ = exerciseType;
exerciseTimes_ = exerciseTimes;
}
public DiscretizedOption(DiscretizedAsset underlying, Exercise.Type exerciseType, List<double> exerciseTimes)
{
underlying_ = underlying;
exerciseType_ = exerciseType;
exerciseTimes_ =exerciseTimes;
}
public override void rollback(DiscretizedAsset asset, double to)
{
partialRollback(asset, to);
asset.adjustValues();
}
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();
Utils.QL_REQUIRE(s0 > 0.0, () => "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).value();
double q = process_.dividendYield().link.zeroRate(maturityDate, divdc, Compounding.Continuous, Frequency.NoFrequency).value();
Date referenceDate = process_.riskFreeRate().link.referenceDate();
// binomial trees with constant coefficient
Handle <YieldTermStructure> flatRiskFree = new Handle <YieldTermStructure>(new FlatForward(referenceDate, r, rfdc));
Handle <YieldTermStructure> flatDividends = new Handle <YieldTermStructure>(new FlatForward(referenceDate, q, divdc));
Handle <BlackVolTermStructure> flatVol = new Handle <BlackVolTermStructure>(new BlackConstantVol(referenceDate, volcal, v, voldc));
StrikedTypePayoff payoff = arguments_.payoff as StrikedTypePayoff;
Utils.QL_REQUIRE(payoff != null, () => "non-striked payoff given");
double maturity = rfdc.yearFraction(referenceDate, maturityDate);
StochasticProcess1D bs = new GeneralizedBlackScholesProcess(process_.stateVariable(),
flatDividends, flatRiskFree, flatVol);
TimeGrid grid = new TimeGrid(maturity, timeSteps_);
ITree tree = getTree_(bs, maturity, timeSteps_, payoff.strike());
BlackScholesLattice <ITree> lattice = new BlackScholesLattice <ITree>(tree, r, maturity, timeSteps_);
DiscretizedAsset option = getAsset_(arguments_, process_, grid);
option.initialize(lattice, maturity);
// Partial derivatives calculated from various points in the
// binomial tree
// (see J.C.Hull, "Options, Futures and other derivatives", 6th edition, pp 397/398)
// Rollback to third-last step, and get underlying prices (s2) &
// option values (p2) at this point
option.rollback(grid[2]);
Vector va2 = new Vector(option.values());
Utils.QL_REQUIRE(va2.size() == 3, () => "Expect 3 nodes in grid at second step");
double p2u = va2[2]; // up
double p2m = va2[1]; // mid
double p2d = va2[0]; // down (low)
double s2u = lattice.underlying(2, 2); // up price
double s2m = lattice.underlying(2, 1); // middle price
double s2d = lattice.underlying(2, 0); // down (low) price
// calculate gamma by taking the first derivate of the two deltas
double delta2u = (p2u - p2m) / (s2u - s2m);
double delta2d = (p2m - p2d) / (s2m - s2d);
double gamma = (delta2u - delta2d) / ((s2u - s2d) / 2);
// Rollback to second-last step, and get option values (p1) at
// this point
option.rollback(grid[1]);
Vector va = new Vector(option.values());
Utils.QL_REQUIRE(va.size() == 2, () => "Expect 2 nodes in grid at first step");
double p1u = va[1];
double p1d = va[0];
double s1u = lattice.underlying(1, 1); // up (high) price
double s1d = lattice.underlying(1, 0); // down (low) price
double delta = (p1u - p1d) / (s1u - s1d);
// Finally, rollback to t=0
option.rollback(0.0);
double p0 = option.presentValue();
// Store results
results_.value = p0;
results_.delta = delta;
results_.gamma = gamma;
// theta can be approximated by calculating the numerical derivative
// between mid value at third-last step and at t0. The underlying price
// is the same, only time varies.
results_.theta = (p2m - p0) / grid[2];
}
//! computes the present value of an asset. public abstract double presentValue(DiscretizedAsset a);
/*! Roll back an asset until the given time, but do not perform * the final adjustment. */ public abstract void partialRollback(DiscretizedAsset a, double to);
/*! Roll back an asset until the given time, performing any needed adjustment. */ public abstract void rollback(DiscretizedAsset a, double to);
/* Numerical method interface
*
* These methods are to be used by discretized assets and
* must be overridden by developers implementing numerical
* methods. Users are advised to use the corresponding
* methods of DiscretizedAsset instead.
*/
//! initialize an asset at the given time.
public abstract void initialize(DiscretizedAsset a, double time);