/// <summary>
/// Attempts to solve the Variance Gamma Optimization problem using
/// <see cref="Heston.VarianceGammaOptimizationProblem"/>.
/// </summary>
/// <param name="data">
/// The data to be used in order to perform the optimization.
/// </param>
/// <param name="settings">The parameter is not used.</param>
/// <returns>The results of the optimization.</returns>
public EstimationResult Estimate(List <object> data, IEstimationSettings settings = null, IController controller = null, Dictionary <string, object> properties = null)
{
EquitySpotMarketData espmd = data[0] as EquitySpotMarketData;
CallPriceMarketData cpmd = data[1] as CallPriceMarketData;
DiscountingCurveMarketData dcmd = data[2] as DiscountingCurveMarketData;
EquityCalibrationData ecd = new EquityCalibrationData(cpmd, dcmd);
this.s0 = espmd.Price;
this.r = espmd.RiskFreeRate;
this.q = espmd.DividendYield;
this.k = ecd.Hdata.Strike;
this.m = ecd.Hdata.Maturity;
this.cp = ecd.Hdata.CallPrice;
Vector x0 = (Vector) new double[] { -0.1, 0.2, 0.1 };
IOptimizationAlgorithm algorithm = new QADE();
OptimizationSettings optimizationSettings = new DESettings();
// Maximum number of iteration allowed.
// Positive integer values print debug info.
optimizationSettings.Verbosity = 1;
// Tolerance.
optimizationSettings.epsilon = 10e-5;
var solution = algorithm.Minimize(new VarianceGammaOptimizationProblem(this.q, this.s0, this.k,
this.r, this.cp, this.m),
optimizationSettings, x0);
if (solution.errors)
{
return(new EstimationResult(solution.message));
}
var er = new EstimationResult();
er.Names = new string[] { "S0", "theta", "sigma", "nu", "rate", "dividend" };
er.Values = new double[] { this.s0, solution.x[0], solution.x[1], solution.x[2], this.r, this.q };
return(er);
}
private static Vector Test(int nm, int nk, double q, double s0, double r, double t, double theta, double sigma, double nu)
{
// Simulate synthetic data.
Vector m = new Vector(nm);
Vector k = new Vector(nk);
Matrix cp = new Matrix(nm, nk);
Random rand = new Random();
for (int i = 0; i < nm; i++)
{
m[i] = 0.01 + rand.NextDouble() * 0.99;
}
for (int i = 0; i < nk; i++)
{
k[i] = 60 + rand.NextDouble() * 90;
}
for (int i = 0; i < nm; i++)
{
for (int j = 0; j < nk; j++)
{
cp[i, j] = VarianceGammaOptionsCalibration.VGCall(theta, sigma, nu, m[i], k[j], q, s0, r);
}
}
Console.WriteLine("Benchmark value");
Console.WriteLine(new Vector()
{
theta, sigma, nu
});
Console.WriteLine("Call prices");
Console.WriteLine(cp);
// VGDiff at optimum.
double fopt = VarianceGammaOptimizationProblem.VGDiff(new Vector()
{
theta, sigma, nu
}, q, s0, k, r, cp, m);
Console.WriteLine("fopt");
Console.WriteLine(fopt);
VarianceGammaOptionsCalibration c = new VarianceGammaOptionsCalibration();
List <object> marketData = new List <object>();
var espmd = new EquitySpotMarketData();
espmd.Price = s0;
espmd.RiskFreeRate = r;
espmd.DividendYield = q;
var cpmd = new CallPriceMarketData();
cpmd.Strike = k;
cpmd.Maturity = m;
cpmd.CallPrice = cp;
var dc = new DiscountingCurveMarketData();
dc.Durations = new Vector()
{
0
};
dc.Values = new Vector()
{
r
};
marketData.Add(espmd);
marketData.Add(cpmd);
marketData.Add(dc);
EstimationResult res = c.Estimate(marketData, null);
return((Vector)res.Values);
}
