public double PriceVIXOption(VIXOption Option, Func <double, double> epsilon, double stock_0)
{
int n = 500;
double T = Option.maturity;
Grid grid = new Grid(0, T, (int)Math.Abs(T * n));
int n_T = grid.get_timeNmbrStep();
int kappa = 2;
//Correlation :
SymmetricMatrix correl = MakeCorrel(n, grid);
SquareMatrix choleskyCorrel = correl.CholeskyDecomposition().SquareRootMatrix();
double rho = 0.1;//correlation between the two Brownian
Func <double, double> payoff;
switch (Option.type)
{
case VIXOption.OptionType.Call:
payoff = S => Math.Max(S - Option.strike, 0);
break;
case VIXOption.OptionType.Put:
payoff = S => Math.Max(Option.strike - S, 0);
break;
default:
payoff = S => Math.Max(S - Option.strike, 0);
break;
}
double price = 0.0;
double McNbSimulation = 1E5;
for (int mc = 1; mc <= McNbSimulation; mc++)
{
// 1-simulation of volterra Hybrid Scheme
ColumnVector Z;
ColumnVector volterra;
//ColumnVector Z_Brownian;
HybridScheme hybridscheme = new HybridScheme(choleskyCorrel, grid, kappa, H);
hybridscheme.simulate(out Z, out volterra);
GaussianSimulator simulator = new GaussianSimulator();
//Z_Brownian = ExtractBrownian(kappa, n_T, Z, volterra);
ColumnVector Variance = new ColumnVector(n_T + 1);
Variance[0] = epsilon(grid.t(0));
for (int i = 1; i <= grid.get_timeNmbrStep(); i++)
{
Variance[i] = epsilon(grid.t(i)) * Math.Exp(2 * vi * Ch * volterra[i - 1] - Math.Pow(vi * Ch, 2) * Math.Pow(grid.t(i), 2 * H));
}
double X = Math.Log(stock_0);
for (int i = 0; i < n_T; i++)
{
double dW = (rho * Z[i] + Math.Sqrt(1 - Math.Pow(rho, 2)) * Math.Sqrt(grid.get_Step()) * simulator.Next());
X = X - 0.5 * Variance[i] * grid.get_Step() + Math.Sqrt(Variance[i]) * dW;
}
double S = Math.Exp(X);
price += payoff(S);
}
price /= McNbSimulation;
return(price);
}
public double VIXfuture_TruncatedChlsky(double T, Func <double, double> epsilon)
{
DateTime start = DateTime.Now;
//StreamWriter sw = new StreamWriter("C:\\Users\\Marouane\\Desktop\\M2IF\\rough volatility\\rBergomiFile.txt");
//Grid
int N = 100;
int S = 7;
Grid grid = new Grid(T, T + Delta, N);
#region Correlation "Correl Matrix Construction"
// Small Covariance Matrix t_0 , ... , t_7
SymmetricMatrix covM_Small = new SymmetricMatrix(S);
//the full Covariance Matrix
SymmetricMatrix covM = new SymmetricMatrix(N + 1);
//Setting for integrale approximations
EvaluationSettings settings = new EvaluationSettings();
settings.AbsolutePrecision = 1.0E-7;
settings.RelativePrecision = 0.0;
// Small Covariance Calcul t_0,.....,t_7
for (int i = 0; i < S; i++)
{
covM_Small[i, i] = (Math.Pow(grid.t(i), 2 * H) - Math.Pow(grid.t(i) - T, 2 * H)) / (2 * H);
for (int j = 0; j < i; j++)
{
Func <double, double> covfunc = t => Math.Pow((grid.t(j) - t) * (grid.t(i) - t), H - 0.5);
IntegrationResult integresult = FunctionMath.Integrate(covfunc, Meta.Numerics.Interval.FromEndpoints(0.0, T), settings);
covM_Small[i, j] = integresult.Value;
}
}
// full COrrelation Calcul
for (int i = 0; i <= N; i++)
{
covM[i, i] = (Math.Pow(grid.t(i), 2 * H) - Math.Pow(grid.t(i) - T, 2 * H)) / (2 * H);
for (int j = 0; j < i; j++)
{
Func <double, double> covfunc = t => Math.Pow((grid.t(j) - t) * (grid.t(i) - t), H - 0.5);
IntegrationResult integresult = FunctionMath.Integrate(covfunc, Meta.Numerics.Interval.FromEndpoints(0.0, T), settings);
covM[i, j] = integresult.Value;
}
}
Func <int, int, double> corrf_Small = (i, j) => covM_Small[i, j] / (Math.Sqrt(covM_Small[i, i] * covM_Small[j, j]));
SymmetricMatrix Correl_Small = new SymmetricMatrix(S);
Correl_Small.Fill(corrf_Small);
Func <int, int, double> corrf = (i, j) => covM[i, j] / (Math.Sqrt(covM[i, i] * covM[j, j]));
SymmetricMatrix Correl = new SymmetricMatrix(N + 1);
Correl.Fill(corrf);
#endregion
CholeskyDecomposition cholesky = Correl_Small.CholeskyDecomposition();
SquareMatrix choleskyCorrel_Small = new SquareMatrix(S);
choleskyCorrel_Small = cholesky.SquareRootMatrix();
GaussianSimulator simulator = new GaussianSimulator();
double VIX = 0.0;
var MC = 1.0E5;
for (int mc = 1; mc < MC; mc++)
{
ColumnVector GaussianVector = new ColumnVector(S);
// Simulating Volterra at 8 first steps on [T, T+Delta]
for (int i = 0; i < S; i++)
{
GaussianVector[i] = simulator.Next();
}
ColumnVector Volterra_small = choleskyCorrel_Small * GaussianVector;
//Adjusting the variance of Volterra Processus
for (int i = 0; i < S; i++)
{
Volterra_small[i] = Volterra_small[i] * Math.Sqrt((Math.Pow(grid.t(i), 2 * H) - Math.Pow(grid.t(i) - grid.t(0), 2 * H)) / (2 * H));
}
// Simulating VOlterra on t_8 ... t_N with the truncated Formula
double[] Volterra = new double[N + 1];
for (int i = 0; i <= N; i++)
{
if (i < S)
{
Volterra[i] = Volterra_small[i];
}
//Tranceted Formula
else
{
Volterra[i] = Math.Sqrt(covM[i, i]) * (Correl[i, i - 1] * Volterra[i - 1] / Math.Sqrt(covM[i - 1, i - 1]) + Math.Sqrt(1 - Math.Pow(Correl[i, i - 1], 2)) * simulator.Next());
}
}
double VIX_ = VIX_Calculate(epsilon, grid, Volterra);
VIX += VIX_;
}
//sw.Close();
VIX /= MC;
TimeSpan dur = DateTime.Now - start;
return(VIX);
}
