/// <summary>
/// Attempts a calibration through <see cref="SwaptionHW1OptimizationProblem"/>
/// using swaption matrices.
/// </summary>
/// <param name="data">The data to be used in order to perform the calibration.</param>
/// <param name="settings">The parameter is not used.</param>
/// <param name="controller">The controller which may be used to cancel the process.</param>
/// <returns>The results of the calibration.</returns>
public EstimationResult Estimate(List <object> data, IEstimationSettings settings = null, IController controller = null, Dictionary <string, object> properties = null)
{
InterestRateMarketData dataset = data[0] as InterestRateMarketData;
MatrixMarketData normalVol = null;
if (data.Count > 1)
{
normalVol = (MatrixMarketData)data[1];
}
PFunction zr = new PFunction(null);
// Loads the zero rate.
double[,] zrvalue = (double[, ])ArrayHelper.Concat(dataset.ZRMarketDates.ToArray(), dataset.ZRMarket.ToArray());
zr.Expr = zrvalue;
double deltak = dataset.SwaptionTenor;
Console.WriteLine("Swaption Tenor\t" + dataset.SwaptionTenor);
var swaptionsFiltering = settings as SwaptionsFiltering;
if (swaptionsFiltering == null)
{
swaptionsFiltering = new SwaptionsFiltering();//creates a default
}
//F stands for Full matrix
var optionMaturityF = normalVol != null ? normalVol.RowValues: dataset.OptionMaturity;
var swapDurationF = normalVol != null ? normalVol.ColumnValues: dataset.SwapDuration;
var swaptionsVolatilityF = normalVol != null ? normalVol.Values: dataset.SwaptionsVolatility;
int maturitiesCount = optionMaturityF.Count(x => x >= swaptionsFiltering.MinSwaptionMaturity && x <= swaptionsFiltering.MaxSwaptionMaturity);
int durationsCount = swapDurationF.Count(x => x >= swaptionsFiltering.MinSwapDuration && x <= swaptionsFiltering.MaxSwapDuration);
Console.WriteLine(string.Format("Calibrating on {0} swaptions prices [#maturiries x #durations]=[{1} x {2}]", maturitiesCount * durationsCount, maturitiesCount, durationsCount));
if (maturitiesCount * durationsCount == 0)
{
return(new EstimationResult("No swaptions satisfying criteria found, please relax filters"));
}
//reduced version
var swaptionsVolatility = new Matrix(maturitiesCount, durationsCount); // dataset.SwaptionsVolatility;
var optionMaturity = new Vector(maturitiesCount); // dataset.OptionMaturity;
var swapDuration = new Vector(durationsCount); // dataset.SwapDuration;
//Build filtered matrix and vectors
int fm = 0;
for (int m = 0; m < optionMaturityF.Length; m++)
{
int fd = 0;
if (optionMaturityF[m] >= swaptionsFiltering.MinSwaptionMaturity && optionMaturityF[m] <= swaptionsFiltering.MaxSwaptionMaturity)
{
for (int d = 0; d < swapDurationF.Length; d++)
{
if (swapDurationF[d] >= swaptionsFiltering.MinSwapDuration && swapDurationF[d] <= swaptionsFiltering.MaxSwapDuration)
{
swaptionsVolatility[fm, fd] = swaptionsVolatilityF[m, d];
swapDuration[fd] = swapDurationF[d];
fd++;
}
}
optionMaturity[fm] = optionMaturityF[m];
fm++;
}
}
var swbm = new SwaptionsBlackModel(zr, BlackModelFactory(zr));
Matrix fsr;
var blackSwaptionPrice = 1000.0 * swbm.SwaptionsSurfBM(optionMaturity, swapDuration, swaptionsVolatility, deltak, out fsr);
Console.WriteLine("Maturities\t" + optionMaturity);
Console.WriteLine("swapDuration\t" + swapDuration);
Console.WriteLine("SwaptionHWEstimator: Black model prices");
Console.WriteLine(blackSwaptionPrice);
SwaptionHW1 swhw1 = new SwaptionHW1(zr);
SwaptionHW1OptimizationProblem problem = new SwaptionHW1OptimizationProblem(swhw1, blackSwaptionPrice, optionMaturity, swapDuration, deltak);
IOptimizationAlgorithm solver = new QADE();
IOptimizationAlgorithm solver2 = new SteepestDescent();
DESettings o = new DESettings();
o.NP = 20;
o.MaxIter = 5;
o.Verbosity = 1;
o.controller = controller;
SolutionInfo solution = null;
Vector x0 = new Vector(new double[] { 0.1, 0.1 });
solution = solver.Minimize(problem, o, x0);
if (solution.errors)
{
return(new EstimationResult(solution.message));
}
o.epsilon = 10e-8;
o.h = 10e-8;
o.MaxIter = 1000;
// We can permit this, given it is fast.
o.accourate_numerical_derivatives = true;
if (solution != null)
{
solution = solver2.Minimize(problem, o, solution.x);
}
else
{
solution = solver2.Minimize(problem, o, x0);
}
if (solution.errors)
{
return(new EstimationResult(solution.message));
}
Console.WriteLine("Solution:");
Console.WriteLine(solution);
string[] names = new string[] { "Alpha", "Sigma" };
Console.WriteLine("SwaptionHWEstimator: hw model prices and error");
problem.Obj(solution.x, true);
EstimationResult result = new EstimationResult(names, solution.x);
result.ZRX = (double[])dataset.ZRMarketDates.ToArray();
result.ZRY = (double[])dataset.ZRMarket.ToArray();
double obj = problem.Obj(solution.x);
return(result);
}
/// <summary>
/// Attempts a calibration through <see cref="SwaptionHW1OptimizationProblem"/>
/// using swaption matrices.
/// </summary>
/// <param name="data">The data to be used in order to perform the calibration.</param>
/// <param name="settings">The parameter is not used.</param>
/// <param name="controller">The controller which may be used to cancel the process.</param>
/// <returns>The results of the calibration.</returns>
public EstimationResult Estimate(List<object> data, IEstimationSettings settings = null, IController controller = null, Dictionary<string, object> properties = null)
{
InterestRateMarketData dataset = data[0] as InterestRateMarketData;
PFunction zr = new PFunction(null);
// Loads the zero rate.
double[,] zrvalue = (double[,])ArrayHelper.Concat(dataset.ZRMarketDates.ToArray(), dataset.ZRMarket.ToArray());
zr.Expr = zrvalue;
double deltak = dataset.SwaptionTenor;
var swaptionsFiltering = settings as SwaptionsFiltering;
if (swaptionsFiltering == null)
swaptionsFiltering = new SwaptionsFiltering();//creates a default
int maturitiesCount = dataset.OptionMaturity.Count(x => x >= swaptionsFiltering.MinSwaptionMaturity && x <= swaptionsFiltering.MaxSwaptionMaturity);
int durationsCount = dataset.SwapDuration.Count(x => x >= swaptionsFiltering.MinSwapDuration && x <= swaptionsFiltering.MaxSwapDuration);
Console.WriteLine(string.Format("Calibrating on {0} swaptions prices [#maturiries x #durations]=[{1} x {2}]", maturitiesCount * durationsCount, maturitiesCount,durationsCount));
if (maturitiesCount * durationsCount == 0)
return new EstimationResult("No swaptions satisfying criteria found, please relax filters");
Matrix swaptionsVolatility = new Matrix(maturitiesCount, durationsCount);// dataset.SwaptionsVolatility;
Vector optionMaturity = new Vector(maturitiesCount);// dataset.OptionMaturity;
Vector swapDuration = new Vector(durationsCount);// dataset.SwapDuration;
//Build filtered matrix and vectors
int fm=0;
for (int m = 0; m < dataset.OptionMaturity.Length; m++)
{
int fd=0;
if (dataset.OptionMaturity[m] >= swaptionsFiltering.MinSwaptionMaturity && dataset.OptionMaturity[m] <= swaptionsFiltering.MaxSwaptionMaturity)
{
for (int d = 0; d < dataset.SwapDuration.Length; d++)
{
if (dataset.SwapDuration[d] >= swaptionsFiltering.MinSwapDuration && dataset.SwapDuration[d] <= swaptionsFiltering.MaxSwapDuration)
{
swaptionsVolatility[fm, fd] = dataset.SwaptionsVolatility[m, d];
swapDuration[fd] = dataset.SwapDuration[d];
fd++; }
}
optionMaturity[fm] = dataset.OptionMaturity[m];
fm++;
}
}
SwaptionsBlackModel swbm = new SwaptionsBlackModel(zr);
Matrix fsr;
var blackSwaptionPrice = 1000.0 * swbm.SwaptionsSurfBM(optionMaturity, swapDuration, swaptionsVolatility, deltak, out fsr);
Console.WriteLine("SwaptionHWEstimator: Black model prices");
Console.WriteLine(blackSwaptionPrice);
SwaptionHW1 swhw1 = new SwaptionHW1(zr);
SwaptionHW1OptimizationProblem problem = new SwaptionHW1OptimizationProblem(swhw1, blackSwaptionPrice, optionMaturity, swapDuration, deltak);
IOptimizationAlgorithm solver = new QADE();
IOptimizationAlgorithm solver2 = new SteepestDescent();
DESettings o = new DESettings();
o.NP = 20;
o.MaxIter = 5;
o.Verbosity = 1;
o.controller = controller;
SolutionInfo solution = null;
Vector x0 = new Vector(new double[] { 0.1, 0.1 });
solution = solver.Minimize(problem, o, x0);
if (solution.errors)
return new EstimationResult(solution.message);
o.epsilon = 10e-8;
o.h = 10e-8;
o.MaxIter = 1000;
// We can permit this, given it is fast.
o.accourate_numerical_derivatives = true;
if (solution != null)
solution = solver2.Minimize(problem, o, solution.x);
else
solution = solver2.Minimize(problem, o, x0);
if (solution.errors)
return new EstimationResult(solution.message);
Console.WriteLine("Solution:");
Console.WriteLine(solution);
string[] names = new string[] { "Alpha", "Sigma" };
EstimationResult result = new EstimationResult(names, solution.x);
result.ZRX = (double[])dataset.ZRMarketDates.ToArray();
result.ZRY = (double[])dataset.ZRMarket.ToArray();
double obj = problem.Obj(solution.x);
return result;
}