public static object BivariateNormalStdCDF(
[ExcelArgument(Description = "X value")] double X,
[ExcelArgument(Description = "Y value")] double Y,
[ExcelArgument(Description = "Correlation")] double Correlation)
{
return(ExcelHelper.Execute(_logger, () =>
{
return BivariateNormal.CDF(X, Y, Correlation);
}));
}
public static object BivariateNormalPDF(
[ExcelArgument(Description = "X value")] double X,
[ExcelArgument(Description = "X mean")] double Xbar,
[ExcelArgument(Description = "X std deviation")] double XStdDev,
[ExcelArgument(Description = "Y values")] double Y,
[ExcelArgument(Description = "Y mean")] double Ybar,
[ExcelArgument(Description = "Y std deviation")] double YStdDev,
[ExcelArgument(Description = "Correlation")] double Correlation)
{
return(ExcelHelper.Execute(_logger, () =>
{
return BivariateNormal.PDF(X, Xbar, XStdDev, Y, Ybar, YStdDev, Correlation);
}));
}
public static IInterpolator1D GenerateCompositeSmileB(this IVolSurface surface, IVolSurface fxSurface, int numSamples, DateTime expiry, double fwdAsset, double fwdFx, double correlation, bool strikesInDeltaSpace = false)
{
var t = surface.OriginDate.CalculateYearFraction(expiry, DayCountBasis.Act365F);
var fxInv = new InverseFxSurface("fxInv", fxSurface as IATMVolSurface, null);
var atmFx = fxSurface.GetVolForDeltaStrike(0.5, t, fwdFx);
var atmA = surface.GetVolForDeltaStrike(0.5, t, fwdAsset);
var compoFwd = fwdAsset * fwdFx;
var atmCompo = Sqrt(atmFx * atmFx + atmA * atmA + 2.0 * correlation * atmA * atmFx);
var lowK = BlackFunctions.AbsoluteStrikefromDeltaKAnalytic(compoFwd, -0.01, 0, t, atmCompo);
var hiK = BlackFunctions.AbsoluteStrikefromDeltaKAnalytic(compoFwd, -0.99, 0, t, atmCompo);
//var cdfInvFx = fxSurface.GenerateCDF2(numSamples * 10, expiry, fwdFx, true);
//var cdfInvAsset = surface.GenerateCDF2(numSamples * 10, expiry, fwdAsset, true);
//var yFx = new Func<double, double>(z => cdfInvFx.Interpolate(Statistics.NormSDist(z)));
//var yAsset = new Func<double, double>(z => cdfInvAsset.Interpolate(Statistics.NormSDist(z)));
var fxCDFCache = new Dictionary <double, double>();
var assetCDFCache = new Dictionary <double, double>();
var yFx = new Func <double, double>(z =>
{
if (fxCDFCache.TryGetValue(z, out var K))
{
return(K);
}
K = fxInv.InverseCDF(expiry, 1.0 / fwdFx, Statistics.NormSDist(z));
fxCDFCache.Add(z, K);
return(K);
});
var yAsset = new Func <double, double>(z =>
{
if (assetCDFCache.TryGetValue(z, out var K))
{
return(K);
}
K = surface.InverseCDF(expiry, fwdAsset, Statistics.NormSDist(z));
assetCDFCache.Add(z, K);
return(K);
});
//var fxCDFCache = new Dictionary<double, double>();
//var assetCDFCache = new Dictionary<double, double>();
//var putFx = fxInv.GeneratePremiumInterpolator(numSamples * 10, expiry, 1.0/fwdFx, OptionType.P);
//var putAsset = surface.GeneratePremiumInterpolator(numSamples * 10, expiry, fwdAsset, OptionType.P);
//var yFx = new Func<double, double>(z =>
//{
// if (fxCDFCache.TryGetValue(z, out var K)) return K;
// K = InverseCDF(putFx, t, 1.0/fwdFx, Statistics.NormSDist(z));
// fxCDFCache.Add(z, K);
// return K;
//});
//var yAsset = new Func<double, double>(z =>
//{
// if (assetCDFCache.TryGetValue(z, out var K)) return K;
// K = InverseCDF(putAsset, t, fwdAsset, Statistics.NormSDist(z));
// var kl = assetCDFCache.Keys.ToList();
// var closerIx = kl.BinarySearch(z);
// var keyIx = ~closerIx;
// if (closerIx < 0 && z < 0 && kl.Count > keyIx)
// {
// if (assetCDFCache[kl[keyIx]] < K)
// K = assetCDFCache[kl[keyIx]];
// }
// assetCDFCache.Add(z, K);
// return K;
//});
var payoff = new Func <double, double, double, double>((z1, z2, kQ) => Max(kQ * yFx(z2) - yAsset(z1), 0));
var integrand = new Func <double, double, double, double>((z1, z2, kQ) => payoff(z1, z2, kQ) * BivariateNormal.PDF(z1, z2, -correlation));
var kStep = (hiK - lowK) / numSamples;
var ks = Enumerable.Range(0, numSamples).Select(kk => lowK + kk * kStep).ToArray();
var premiums = new double[ks.Length];
var vols = new double[ks.Length];
for (var i = 0; i < ks.Length; i++)
{
var ik = new Func <double, double, double>((z1, z2) => integrand(z1, z2, ks[i]));
var pk = Integration.TwoDimensionalGaussLegendre(ik, -5, 5, -5, 5, 16);
//var pk = Integration.TwoDimensionalSimpsons(ik, -5, 5, -5, 5, 100);
pk *= fwdFx;
var volK = BlackFunctions.BlackImpliedVol(compoFwd, ks[i], 0.0, t, pk, OptionType.P);
vols[i] = volK;
premiums[i] = pk;
}
if (strikesInDeltaSpace)
{
ks = ks.Select((ak, ix) => - BlackFunctions.BlackDelta(compoFwd, ak, 0.0, t, vols[ix], OptionType.P)).ToArray();
}
return(InterpolatorFactory.GetInterpolator(ks, vols, Interpolator1DType.CubicSpline));
}
public void BivariateNormalPDF_Facts() => Assert.Equal(BivariateNormal.PDF(.5, .5, .5, .5, .5, .5, .5), MathFunctions.BivariateNormalPDF(.5, .5, .5, .5, .5, .5, .5));
