// Objective function ===========================================================================
public double f(double[] param, OFSet ofset)
{
BGMPrice BGM = new BGMPrice();
BisectionAlgo BA = new BisectionAlgo();
double S = ofset.opset.S;
double r = ofset.opset.r;
double q = ofset.opset.q;
int trap = ofset.opset.trap;
int ObjFunction = ofset.ObjFunction;
OPSet opset = ofset.opset;
double[,] MktIV = ofset.data.MktIV;
double[,] MktPrice = ofset.data.MktPrice;
string[,] PutCall = ofset.data.PutCall;
double[] K = ofset.data.K;
double[] T = ofset.data.T;
double[] X = ofset.X;
double[] W = ofset.W;
int NK = PutCall.GetLength(0);
int NT = PutCall.GetLength(1);
// Separate out the parameters from the "param" vector;
int N = param.Length;
double kappa = param[0];
double v0 = param[1];
double[] THETA = new double[NT];
double[] SIGMA = new double[NT];
double[] RHO = new double[NT];
for (int k = 0; k <= NT - 1; k++)
{
THETA[k] = param[3 * k + 2];
SIGMA[k] = param[3 * k + 3];
RHO[k] = param[3 * k + 4];
}
// Settings for the Bisection algorithm
double a = 0.001;
double b = 3.0;
double Tol = 1e5;
int MaxIter = 10000;
// Initialize the model price and model implied vol vectors, and the objective function value
double[,] ModelPrice = new double[NK, NT];
double[,] ModelIV = new double[NK, NT];
double Vega = 0.0;
double Error = 0.0;
double pi = Math.PI;
double[] lb = ofset.lb;
double[] ub = ofset.ub;
double kappaLB = lb[0]; double kappaUB = ub[0];
double thetaLB = lb[1]; double thetaUB = ub[1];
double sigmaLB = lb[2]; double sigmaUB = ub[2];
double v0LB = lb[3]; double v0UB = ub[3];
double rhoLB = lb[4]; double rhoUB = ub[4];
List <double> MatList = new List <double>();
List <double> thetaList = new List <double>();
List <double> sigmaList = new List <double>();
List <double> rhoList = new List <double>();
double[] Mat, theta, sigma, rho;
if ((kappa <= kappaLB) || (kappa >= kappaUB) || (v0 <= v0LB) || (v0 >= v0UB))
{
Error = 1e50;
}
for (int k = 0; k <= NT - 1; k++)
{
if ((THETA[k] <= thetaLB) || (THETA[k] >= thetaUB) || (SIGMA[k] <= sigmaLB) || (SIGMA[k] >= sigmaUB) || (RHO[k] <= rhoLB) || (RHO[k] >= rhoUB))
{
Error = 1e50;
}
}
{
for (int t = 0; t < NT; t++)
{
MatList.Add(T[t]);
thetaList.Add(THETA[t]);
sigmaList.Add(SIGMA[t]);
rhoList.Add(RHO[t]);
Mat = MatList.ToArray();
theta = thetaList.ToArray();
sigma = sigmaList.ToArray();
rho = rhoList.ToArray();
Mat.Reverse();
Array.Reverse(theta);
Array.Reverse(sigma);
Array.Reverse(rho);
for (int k = 0; k < NK; k++)
{
ModelPrice[k, t] = BGM.BGMApproxPriceTD(kappa, v0, theta, sigma, rho, opset, K[k], Mat);
switch (ObjFunction)
{
case 1:
// MSE Loss Function
Error += Math.Pow(ModelPrice[k, t] - MktPrice[k, t], 2.0);
break;
case 2:
// RMSE Loss Function
Error += Math.Pow(ModelPrice[k, t] - MktPrice[k, t], 2.0) / MktPrice[k, t];
break;
case 3:
// IVMSE Loss Function
ModelIV[k, t] = BA.BisecBSIV(opset, K[k], T[t], a, b, ModelPrice[k, t], Tol, MaxIter);
Error += Math.Pow(ModelIV[k, t] - MktIV[k, t], 2);
break;
case 4:
// IVRMSE Christoffersen, Heston, Jacobs proxy
double d = (Math.Log(S / K[k]) + (r - q + MktIV[k, t] * MktIV[k, t] / 2.0) * T[t]) / MktIV[k, t] / Math.Sqrt(T[t]);
double NormPDF = Math.Exp(-0.5 * d * d) / Math.Sqrt(2.0 * pi);
Vega = S * NormPDF * Math.Sqrt(T[t]);
Error += Math.Pow(ModelPrice[k, t] - MktPrice[k, t], 2) / (Vega * Vega);
break;
}
}
}
}
return(Error);
}
static void Main(string[] args)
{
// Classes
BGMPrice BGM = new BGMPrice();
BisectionAlgo BA = new BisectionAlgo();
// 32-point Gauss-Laguerre Abscissas and weights
double[] X = new Double[32];
double[] W = new Double[32];
using (TextReader reader = File.OpenText("../../GaussLaguerre32.txt"))
for (int k = 0; k <= 31; k++)
{
string text = reader.ReadLine();
string[] bits = text.Split(' ');
X[k] = double.Parse(bits[0]);
W[k] = double.Parse(bits[1]);
}
// DJIA ETF (ticker DIA) put data
double[,] PutIV = new double[13, 4] {
{ 0.1962, 0.1947, 0.2019, 0.2115 }, { 0.1910, 0.1905, 0.1980, 0.2082 },
{ 0.1860, 0.1861, 0.1943, 0.2057 }, { 0.1810, 0.1812, 0.1907, 0.2021 },
{ 0.1761, 0.1774, 0.1871, 0.2000 }, { 0.1718, 0.1743, 0.1842, 0.1974 },
{ 0.1671, 0.1706, 0.1813, 0.1950 }, { 0.1644, 0.1671, 0.1783, 0.1927 },
{ 0.1645, 0.1641, 0.1760, 0.1899 }, { 0.1661, 0.1625, 0.1743, 0.1884 },
{ 0.1701, 0.1602, 0.1726, 0.1862 }, { 0.1755, 0.1610, 0.1716, 0.1846 },
{ 0.1796, 0.1657, 0.1724, 0.1842 }
};
double[] K = new double[] { 124.0, 125.0, 126.0, 127.0, 128.0, 129.0, 130.0, 131.0, 132.0, 133.0, 134.0, 135.0, 136.0 };
double[] T = new double[] { 37.0 / 365.0, 72.0 / 365.0, 135.0 / 365.0, 226.0 / 365.0, };
double[,] MktIV = PutIV;
double S = 129.14;
double r = 0.0010;
double q = 0.0068;
int NT = 4;
int NK = 13;
// Create the market prices
BlackScholesPrice BS = new BlackScholesPrice();
string[,] PutCall = new string[NK, NT];
double[,] MktPrice = new double[NK, NT];
for (int t = 0; t < NT; t++)
{
for (int k = 0; k < NK; k++)
{
PutCall[k, t] = "P";
MktPrice[k, t] = BS.BlackScholes(S, K[k], T[t], r, q, MktIV[k, t], PutCall[k, t]);
}
}
// Bounds on the parameter estimates
double e = 1e-3;
double kappaU = 20.0; double kappaL = e;
double thetaU = 3.0; double thetaL = e;
double sigmaU = 10.0; double sigmaL = e;
double v0U = 3.0; double v0L = e;
double rhoU = 0.99; double rhoL = -0.99;
int N = 2 + 3 * NT;
double[] ub = new double[N];
double[] lb = new double[N];
for (int j = 0; j <= N; j++)
{
ub[0] = kappaU; lb[0] = kappaL;
ub[1] = v0U; lb[1] = v0L;
for (int k = 1; k <= NT; k++)
{
ub[3 * k - 1] = thetaU; lb[3 * k - 1] = thetaL;
ub[3 * k] = sigmaU; lb[3 * k - 1] = sigmaL;
ub[3 * k + 1] = rhoU; lb[3 * k + 1] = rhoL;
}
}
// Starting values for the parameters
// Parameter order is [kappa v0 | theta[0] sigma[0] rho[0] | theta[1] sigma[1] rho[1] | etc...];
double kappaS = 2.0;
double thetaS = 0.1;
double sigmaS = 1.2;
double v0S = 0.05;
double rhoS = -0.5;
double[,] s = new double[N, N + 1];
for (int j = 0; j <= N; j++)
{
s[0, j] = kappaS + BS.RandomNum(-0.01, 0.01) * kappaS;
s[1, j] = v0S + BS.RandomNum(-0.01, 0.01) * v0S;
for (int k = 1; k <= NT; k++)
{
s[3 * k - 1, j] = thetaS + BS.RandomNum(-0.01, 0.01) * thetaS;
s[3 * k, j] = sigmaS + BS.RandomNum(-0.01, 0.01) * sigmaS;
s[3 * k + 1, j] = rhoS + BS.RandomNum(-0.01, 0.01) * rhoS;
}
}
// Structure for the option settings
OPSet opset;
opset.S = S;
opset.r = r;
opset.q = q;
opset.trap = 1;
opset.PutCall = "P";
// Structure for the market data
MktData data;
data.MktIV = MktIV;
data.MktPrice = MktPrice;
data.K = K;
data.T = T;
data.PutCall = PutCall;
// Structure for the objective function settings
OFSet ofset;
ofset.opset = opset;
ofset.data = data;
ofset.X = X;
ofset.W = W;
ofset.ObjFunction = 4;
ofset.lb = lb;
ofset.ub = ub;
// Structure for the Nelder Mead algorithm settings
NMSet nmset;
nmset.ofset = ofset;
nmset.MaxIters = 5000;
nmset.Tolerance = 1e-8;
nmset.N = N;
// Run the Nelder Mead Algorithm
NelderMeadAlgo NM = new NelderMeadAlgo();
ObjectiveFunction OF = new ObjectiveFunction();
double[] B = NM.NelderMead(OF.f, nmset, s);
int NB = B.Length;
// Separate the parameter vector into different vectors
double kappa = B[0];
double v0 = B[1];
double[] theta = new double[NT];
double[] sigma = new double[NT];
double[] rho = new double[NT];
for (int k = 0; k <= NT - 1; k++)
{
theta[k] = B[3 * k + 2];
sigma[k] = B[3 * k + 3];
rho[k] = B[3 * k + 4];
}
// Bisection algorithm settings
double a = 0.01;
double b = 4.0;
double Tol = 1e-5;
int MaxIter = 2500;
// Find the implied volatilities
double[,] ModelPrice = new double[NK, NT];
double[,] ModelIV = new double[NK, NT];
double Error = 0.00;
List <double> MatList = new List <double>();
List <double> thetaList = new List <double>();
List <double> sigmaList = new List <double>();
List <double> rhoList = new List <double>();
double[] Mat, Theta, Sigma, Rho;
for (int t = 0; t < NT; t++)
{
// Stack the parameters
MatList.Add(T[t]);
thetaList.Add(theta[t]);
sigmaList.Add(sigma[t]);
rhoList.Add(rho[t]);
// Convert to arrays
Mat = MatList.ToArray();
Theta = thetaList.ToArray();
Sigma = sigmaList.ToArray();
Rho = rhoList.ToArray();
for (int k = 0; k < NK; k++)
{
ModelPrice[k, t] = BGM.BGMApproxPriceTD(kappa, v0, Theta, Sigma, Rho, opset, K[k], Mat);
ModelIV[k, t] = BA.BisecBSIV(opset, K[k], T[t], a, b, ModelPrice[k, t], Tol, MaxIter);
Error += Math.Pow(ModelIV[k, t] - MktIV[k, t], 2.0) / Convert.ToDouble(NT * NK);
}
}
// Output the results
Console.WriteLine("----------------------------------------------------------");
Console.WriteLine(" kappa v0 theta sigma rho");
Console.WriteLine("----------------------------------------------------------");
for (int k = 0; k <= NT - 1; k++)
{
Console.WriteLine("{0,10:F5} {1,10:F5} {2,10:F5} {3,10:F5} {4,10:F5}", kappa, v0, theta[k], sigma[k], rho[k]);
}
Console.WriteLine("----------------------------------------------------------");
Console.WriteLine("Value of objective function {0:F5} ", B[NB - 2]);
Console.WriteLine("Number of iterations required {0:0} ", B[NB - 1]);
Console.WriteLine("----------------------------------------------------------");
Console.WriteLine(" ");
Console.WriteLine("Model/Market implied volatilities");
Console.WriteLine("----------------------------------------------------------");
Console.WriteLine("Strike Mat1 Mat2 Mat3 Mat4");
Console.WriteLine("----------------------------------------------------------");
for (int k = 0; k <= NK - 1; k++)
{
Console.WriteLine("{0,5:0} {1,10:F5} {2,10:F5} {3,10:F5} {4,10:F5}", K[k], ModelIV[k, 0], ModelIV[k, 1], ModelIV[k, 2], ModelIV[k, 3]);
Console.WriteLine("{0,5:0} {1,10:F5} {2,10:F5} {3,10:F5} {4,10:F5}", K[k], MktIV[k, 0], MktIV[k, 1], MktIV[k, 2], MktIV[k, 3]);
Console.WriteLine("----------------------------------------------------------");
}
Console.WriteLine(" ");
Console.WriteLine("IV Estimation error {0,5:E5}", Error);
Console.WriteLine(" ");
}
