/// <summary>
/// This method allows to fit the implied volatility using different models.
/// </summary>
/// <param name="Hdataset"></param>
/// <returns></returns>
IFunction FitImplVolModel(CallPriceMarketData Hdataset)
{
int model = 1;
switch (model)
{
case 0:
PFunction2D.PFunction2D pf = new PFunction2D.PFunction2D(Hdataset.Maturity, Hdataset.Strike, Hdataset.Volatility);
pf.Interpolation = DVPLUtils.EInterpolationType.LEAST_SQUARES;
pf.Extrapolation = DVPLUtils.ExtrapolationType.USEMODEL;
pf.Parse(null);
return(pf);
case 1:
//define a model for fitting the implied volatility
Fairmat.Statistics.LinearModel impVol = new Fairmat.Statistics.LinearModel(
new Fairmat.Statistics.Predictor[] {
delegate(Vector xx) { return(1); },
delegate(Vector xx) { return(xx[0]); },
delegate(Vector xx) { return(xx[1]); },
delegate(Vector xx) { return(System.Math.Pow(xx[0], 2)); },
delegate(Vector xx) { return(System.Math.Pow(xx[1], 2)); },
delegate(Vector xx) { return(xx[0] * xx[1]); },
});
// Unroll matrix and coordinate vectors in order to make it suitable
// for the Quadratic model implementation.
int n = Hdataset.Volatility.R * Hdataset.Volatility.C;
Matrix xy = new Matrix(n, 2);
Vector z = new Vector(n);
int count = 0;
for (int x = 0; x < Hdataset.Volatility.R; x++)
{
for (int y = 0; y < Hdataset.Volatility.C; y++)
{
if (Hdataset.Volatility[x, y] > 0.01)
{
xy[count, Range.All] = (new Vector()
{
Hdataset.Maturity[x], Hdataset.Strike[y]
}).T;
z[count] = Hdataset.Volatility[x, y];
count++;
}
}
}
xy.Resize(count, xy.C);
z.Resize(count);
impVol.Estimate(xy, z);
return(impVol);
}
return(null);
}
/// <summary>
/// This method allows to fit the implied volatility using different models.
/// </summary>
/// <param name="Hdataset"></param>
/// <returns></returns>
IFunction FitImplVolModel(CallPriceMarketData Hdataset)
{
int model = 1;
switch (model)
{
case 0:
PFunction2D.PFunction2D pf = new PFunction2D.PFunction2D(Hdataset.Maturity, Hdataset.Strike, Hdataset.Volatility);
pf.Interpolation = DVPLUtils.EInterpolationType.LEAST_SQUARES;
pf.Extrapolation = DVPLUtils.ExtrapolationType.USEMODEL;
pf.Parse(null);
return pf;
case 1:
//define a model for fitting the implied volatility
Fairmat.Statistics.LinearModel impVol = new Fairmat.Statistics.LinearModel(
new Fairmat.Statistics.Predictor[] {
delegate(Vector xx) { return 1; },
delegate(Vector xx) { return xx[0]; },
delegate(Vector xx) { return xx[1]; },
delegate(Vector xx) { return System.Math.Pow(xx[0], 2); },
delegate(Vector xx) { return System.Math.Pow(xx[1], 2); },
delegate(Vector xx) { return xx[0] * xx[1]; }, });
// Unroll matrix and coordinate vectors in order to make it suitable
// for the Quadratic model implementation.
int n = Hdataset.Volatility.R * Hdataset.Volatility.C;
Matrix xy = new Matrix(n, 2);
Vector z = new Vector(n);
int count = 0;
for (int x = 0; x < Hdataset.Volatility.R; x++)
{
for (int y = 0; y < Hdataset.Volatility.C; y++)
{
if (Hdataset.Volatility[x, y] > 0.01)
{
xy[count, Range.All] = (new Vector() { Hdataset.Maturity[x],Hdataset.Strike[y] }).T;
z[count] = Hdataset.Volatility[x, y];
count++;
}
}
}
xy.Resize(count, xy.C);
z.Resize(count);
impVol.Estimate(xy, z);
return impVol;
}
return null;
}
