public EqualProbabilitiesBinomialTree(StochasticProcess1D process, double end, int steps)
: base(process, end, steps)
{
}
protected ShortRateDynamics(StochasticProcess1D process)
{
process_ = process;
}
public LeisenReimer factory(StochasticProcess1D process, double end, int steps, double strike)
{
return(new LeisenReimer(process, end, steps, strike));
}
public Joshi4 factory(StochasticProcess1D process, double end, int steps, double strike)
{
return(new Joshi4(process, end, steps, strike));
}
public Trigeorgis factory(StochasticProcess1D process, double end, int steps, double strike)
{
return(new Trigeorgis(process, end, steps, strike));
}
public Tian factory(StochasticProcess1D process, double end, int steps, double strike)
{
return(new Tian(process, end, steps, strike));
}
public AdditiveEQPBinomialTree(StochasticProcess1D process, double end, int steps, double strike)
: base(process, end, steps)
{
up_ = -0.5 * driftPerStep_ +
0.5 * Math.Sqrt(4.0 * process.variance(0.0, x0_, dt_) - 3.0 * driftPerStep_ * driftPerStep_);
}
public AdditiveEQPBinomialTree factory(StochasticProcess1D process, double end, int steps, double strike)
{
return(new AdditiveEQPBinomialTree(process, end, steps, strike));
}
public JarrowRudd factory(StochasticProcess1D process, double end, int steps, double strike)
{
return(new JarrowRudd(process, end, steps, strike));
}
public CoxRossRubinstein factory(StochasticProcess1D process, double end, int steps, double strike)
{
return(new CoxRossRubinstein(process, end, steps, strike));
}
public JarrowRudd(StochasticProcess1D process, double end, int steps, double strike)
: base(process, end, steps)
{
// drift removed
up_ = process.stdDeviation(0.0, x0_, dt_);
}
/*! Returns an approximation of the variance defined as
* \f$ \sigma(t_0, x_0)^2 \Delta t \f$. */
public double variance(StochasticProcess1D process, double t0, double x0, double dt)
{
double sigma = process.diffusion(t0, x0);
return(sigma * sigma * dt);
}
/*! Returns an approximation of the diffusion defined as
* \f$ \sigma(t_0, x_0) \sqrt{\Delta t} \f$. */
public double diffusion(StochasticProcess1D process, double t0, double x0, double dt)
{
return(process.diffusion(t0, x0) * Math.Sqrt(dt));
}
/*! Returns an approximation of the drift defined as
* \f$ \mu(t_0, x_0) \Delta t \f$. */
public double drift(StochasticProcess1D process, double t0, double x0, double dt)
{
return(process.drift(t0, x0) * dt);
}
public TrinomialTree(StochasticProcess1D process,
TimeGrid timeGrid)
: this(process, timeGrid, false)
{
}
