public void Populate(IStochasticProcess container, EstimationResult estimate)
{
bool found;
this.s0 = new ModelParameter(PopulateHelper.GetValue("S0", estimate.Names, estimate.Values, out found), this.s0.Description);
bool errors = RetrieveCurve(container.Context, false);
if (!errors)
{
PFunction rFunc = estimate.Objects[0] as PFunction;
PFunction rFuncDest = this.r.fVRef() as PFunction;
rFuncDest.Expr = rFunc.Expr;
PFunction qFunc = estimate.Objects[1] as PFunction;
PFunction qFuncDest = this.q.fVRef() as PFunction;
qFuncDest.Expr = qFunc.Expr;
//Calibrator assumes dividend yield is a step constant function, the simulation model must be coherent with that assumption.
qFuncDest.m_Function.iType = DVPLUtils.EInterpolationType.ZERO_ORDER_LEFT;
PFunction2D.PFunction2D localVolSrc = estimate.Objects[2] as PFunction2D.PFunction2D;
PFunction2D.PFunction2D localVolDest = this.localVol.fVRef() as PFunction2D.PFunction2D;
localVolDest.Expr = localVolSrc.Expr;
localVolDest.Interpolation = localVolSrc.Interpolation;
}
}
public static StochasticManifoldPoint Apply(this IStochasticProcess process, StochasticManifoldPoint stochasticPoint, double time)
{
var diff = process.ExpectationDifferential(stochasticPoint.Expectation, time);
var prediction = process.Apply(stochasticPoint.Expectation, time);
return(new StochasticManifoldPoint(prediction.Expectation, stochasticPoint.Covariance.Conjugate(diff) + prediction.Covariance));
}
/// <summary>
/// Populate editable fields from name and value vectors
/// specific to the Heston extended process.
/// </summary>
/// <param name="stocProcess">
/// The stochastic process which is being referenced to.
/// </param>
/// <param name="estimate">
/// The estimation result which contains values and names of parameters.
/// It will be searched for S0, kappa, theta, sigma, V0 and rho.
/// </param>
public void Populate(IStochasticProcess stocProcess, EstimationResult estimate)
{
bool found;
this.S0 = new ModelParameter(PopulateHelper.GetValue("S0", estimate.Names, estimate.Values, out found), this.S0.Description);
this.k = new ModelParameter(PopulateHelper.GetValue("kappa", estimate.Names, estimate.Values, out found), this.k.Description);
this.theta = new ModelParameter(PopulateHelper.GetValue("theta", estimate.Names, estimate.Values, out found), this.theta.Description);
this.sigma = new ModelParameter(PopulateHelper.GetValue("sigma", estimate.Names, estimate.Values, out found), this.sigma.Description);
this.V0 = new ModelParameter(PopulateHelper.GetValue("V0", estimate.Names, estimate.Values, out found), this.V0.Description);
int index = stocProcess.NoiseIndex;
ProjectProcess prj = stocProcess.Context as ProjectProcess;
// Update the correlation matrix.
prj.Processes.r.Set(index, index + 1, (RightValue)PopulateHelper.GetValue("rho", estimate.Names, estimate.Values, out found));
bool errors = RetrieveCurve(stocProcess.Context, false);
if (!errors)
{
this.zrCurve.Expr = (estimate.Objects[0] as Matrix).ToArray();
this.dyCurve.Expr = (estimate.Objects[1] as Matrix).ToArray();
//Calibrator assumes dividend yield is a step constant function, the simulation model must be coherent with that assumption.
(this.dyCurve as PFunction).m_Function.iType = DVPLUtils.EInterpolationType.ZERO_ORDER_LEFT;
}
}
public TotalIntegralStochasticProcess(IStochasticProcess baseProcess, IManifold fiber) : base(baseProcess, fiber)
{
if (!(baseProcess.StateSpace is AffineSpace))
{
throw new ArgumentException("Expected process in affine space", "baseProcess");
}
}
/*
* /// <summary>
* /// Populate editable fields from name and value vectors
* /// specific to HW.
* /// </summary>
* /// <param name="names">
* /// An array with the names of the variable,
* /// will search for alpha (or a1), sigma (or sigma1).
* /// </param>
* /// <param name="values">The values associated to the parameters in names.</param>
* public void Populate(string[] names, double[] values)
* {
* bool found = false;
* this.alpha1 = new ModelParameter(PopulateHelper.GetValue("alpha", "a1", names, values, out found), alphaDescription);
* this.sigma1 = new ModelParameter(PopulateHelper.GetValue("sigma", "sigma1", names, values, out found), sigmaDescription);
* this.lambda0 = new ModelParameter(PopulateHelper.GetValue("Lambda0", "lambda0", names, values, out found), lambda0Description);
* }
*/
/// <summary>
/// Populate editable fields from name and value vectors
/// specific to the Heston extended process.
/// </summary>
/// <param name="stocProcess">
/// The stochastic process which is being referenced to.
/// </param>
/// <param name="estimate">
/// The estimation result which contains values and names of parameters.
/// It will be searched for S0, kappa, theta, sigma, V0 and rho.
/// </param>
public void Populate(IStochasticProcess stocProcess, EstimationResult estimate)
{
bool found;
this.alpha1 = new ModelParameter(PopulateHelper.GetValue("alpha", "a1", estimate.Names, estimate.Values, out found), alphaDescription);
this.sigma1 = new ModelParameter(PopulateHelper.GetValue("sigma", "sigma1", estimate.Names, estimate.Values, out found), sigmaDescription);
this.lambda0 = new ModelParameter(PopulateHelper.GetValue("Lambda0", "lambda0", estimate.Names, estimate.Values, out found), lambda0Description);
}
public static void ValidateProcess(IStochasticProcess process, IManifoldPoint point, double time, double eps, double tolerance)
{
var trivialEvolution = process.Apply(point, time: 0);
Assert.That(process.StateSpace.GetTranslation(trivialEvolution.Expectation, point).Norm(), Is.LessThan(_tolerance));
Assert.That(trivialEvolution.Covariance.FrobeniusNorm(), Is.LessThan(_tolerance));
ValidateMappingDifferential(process.Apply(time), point, eps, tolerance);
}
public KalmanFilter(IStochasticProcess processModel, StochasticManifoldPoint initialEstimate, double initialTime)
{
ArgAssert.NotNull(processModel, "processModel");
ArgAssert.Equal(processModel.StateSpace.Dimension, "processModel.StateSpace.Dimension", initialEstimate.Dimension, "initialEstimate.Dimension");
_time = initialTime;
_estimate = initialEstimate;
_processModel = processModel;
}
public AffineIntegralStochasticProcess(IStochasticProcess baseProcess, IManifold fiber, OMatrix gain, OVector offset)
: base(baseProcess, fiber)
{
if (!(baseProcess.StateSpace is AffineSpace))
{
throw new ArgumentException("Expected process in affine space", "baseProcess");
}
ArgAssert.Equal(baseProcess.StateSpace.Dimension, "baseProcess.StateSpace.Dimension", gain.ColumnCount, "gain.ColumnCount");
ArgAssert.Equal(fiber.Dimension, "fiber.Dimension", gain.RowCount, "gain.RowCount");
ArgAssert.Equal(offset.Dimension, "offset.Dimension", gain.RowCount, "gain.RowCount");
_gain = gain;
_offset = offset;
}
/// <summary>
/// Populate editable fields from name and value vectors
/// specific to the CIR process.
/// </summary>
/// <param name="container">
/// The stochastic process which is being referenced to.
/// </param>
/// <param name="estimate">
/// The estimation result which contains values and names of parameters.
/// </param>
public void Populate(IStochasticProcess container, EstimationResult estimate)
{
bool found;
this.parameterValues = new double[4];
for (int i = 0; i < parameterNames.Length; i++)
{
this.parameterValues[i] = PopulateHelper.GetValue(parameterNames[i],
estimate.Names, estimate.Values,
out found);
Console.WriteLine("ParameterValues[{0}] = {1}\t ParameterNames[{0}] = {2}",
i, this.parameterValues[i], parameterNames[i]);
}
SetParametersValue();
}
/// <summary>
/// Populate editable fields from name and value vectors
/// specific to the Heston extended process.
/// </summary>
/// <param name="stocProcess">
/// The stochastic process which is being referenced to.
/// </param>
/// <param name="estimate">
/// The estimation result which contains values and names of parameters.
/// It will be searched for S0, kappa, theta, sigma, V0, r, q and rho.
/// </param>
public void Populate(IStochasticProcess stocProcess, EstimationResult estimate)
{
bool found;
this.S0 = new ModelParameter(PopulateHelper.GetValue("S0", estimate.Names, estimate.Values, out found), this.S0.Description);
this.k = new ModelParameter(PopulateHelper.GetValue("kappa", estimate.Names, estimate.Values, out found), this.k.Description);
this.theta = new ModelParameter(PopulateHelper.GetValue("theta", estimate.Names, estimate.Values, out found), this.theta.Description);
this.sigma = new ModelParameter(PopulateHelper.GetValue("sigma", estimate.Names, estimate.Values, out found), this.sigma.Description);
this.V0 = new ModelParameter(PopulateHelper.GetValue("V0", estimate.Names, estimate.Values, out found), this.V0.Description);
this.r = new ModelParameter(PopulateHelper.GetValue("r", estimate.Names, estimate.Values, out found), this.r.Description);
this.q = new ModelParameter(PopulateHelper.GetValue("q", estimate.Names, estimate.Values, out found), this.q.Description);
int index = stocProcess.NoiseIndex;
ProjectProcess prj = stocProcess.Context as ProjectProcess;
// Updates the correlation.
prj.Processes.r.Set(index, index + 1, (RightValue)PopulateHelper.GetValue("rho", estimate.Names, estimate.Values, out found));
}
/// <summary>
/// Populate editable fields from name and value vectors
/// specific to the CIR process.
/// </summary>
/// <param name="container">
/// The stochastic process which is being referenced to.
/// </param>
/// <param name="estimate">
/// The estimation result which contains values and names of parameters.
/// </param>
public void Populate(IStochasticProcess container, EstimationResult estimate)
{
bool found;
this.parameterValues = new double[4];
for (int i = 0; i < parameterNames.Length; i++)
{
this.parameterValues[i] = PopulateHelper.GetValue(parameterNames[i],
estimate.Names, estimate.Values,
out found);
Console.WriteLine("ParameterValues[{0}] = {1}\t ParameterNames[{0}] = {2}",
i, this.parameterValues[i], parameterNames[i]);
}
SetParametersValue();
}
/// <summary>
/// Populate editable fields from name and value vectors
/// specific to the Heston extended process.
/// </summary>
/// <param name="stocProcess">
/// The stochastic process which is being referenced to.
/// </param>
/// <param name="estimate">
/// The estimation result which contains values and names of parameters.
/// It will be searched for S0, kappa, theta, sigma, V0, r, q and rho.
/// </param>
public void Populate(IStochasticProcess stocProcess, EstimationResult estimate)
{
bool found;
this.S0 = new ModelParameter(PopulateHelper.GetValue("S0", estimate.Names, estimate.Values, out found), this.S0.Description);
this.k = new ModelParameter(PopulateHelper.GetValue("kappa", estimate.Names, estimate.Values, out found), this.k.Description);
this.theta = new ModelParameter(PopulateHelper.GetValue("theta", estimate.Names, estimate.Values, out found), this.theta.Description);
this.sigma = new ModelParameter(PopulateHelper.GetValue("sigma", estimate.Names, estimate.Values, out found), this.sigma.Description);
this.V0 = new ModelParameter(PopulateHelper.GetValue("V0", estimate.Names, estimate.Values, out found), this.V0.Description);
this.r = new ModelParameter(PopulateHelper.GetValue("r", estimate.Names, estimate.Values, out found), this.r.Description);
this.q = new ModelParameter(PopulateHelper.GetValue("q", estimate.Names, estimate.Values, out found), this.q.Description);
int index = stocProcess.NoiseIndex;
ProjectProcess prj = stocProcess.Context as ProjectProcess;
// Updates the correlation.
prj.Processes.r.Set(index, index + 1, (RightValue)PopulateHelper.GetValue("rho", estimate.Names, estimate.Values, out found));
}
/// <summary>
/// Populate editable fields from name and value vectors
/// specific to the Heston extended process.
/// </summary>
/// <param name="stocProcess">
/// The stochastic process which is being referenced to.
/// </param>
/// <param name="estimate">
/// The estimation result which contains values and names of parameters.
/// It will be searched for S0, kappa, theta, sigma, V0 and rho.
/// </param>
public void Populate(IStochasticProcess stocProcess, EstimationResult estimate)
{
bool found;
this.S0 = new ModelParameter(PopulateHelper.GetValue("S0", estimate.Names, estimate.Values, out found), this.S0.Description);
this.k = new ModelParameter(PopulateHelper.GetValue("kappa", estimate.Names, estimate.Values, out found), this.k.Description);
this.theta = new ModelParameter(PopulateHelper.GetValue("theta", estimate.Names, estimate.Values, out found), this.theta.Description);
this.sigma = new ModelParameter(PopulateHelper.GetValue("sigma", estimate.Names, estimate.Values, out found), this.sigma.Description);
this.V0 = new ModelParameter(PopulateHelper.GetValue("V0", estimate.Names, estimate.Values, out found), this.V0.Description);
int index = stocProcess.NoiseIndex;
ProjectProcess prj = stocProcess.Context as ProjectProcess;
// Update the correlation matrix.
prj.Processes.r.Set(index, index + 1, (RightValue)PopulateHelper.GetValue("rho", estimate.Names, estimate.Values, out found));
bool errors = RetrieveCurve(stocProcess.Context, false);
if (!errors)
{
this.zrCurve.Expr = (estimate.Objects[0] as Matrix).ToArray();
this.dyCurve.Expr = (estimate.Objects[1] as Matrix).ToArray();
//Calibrator assumes dividend yield is a step constant function, the simulation model must be coherent with that assumption.
(this.dyCurve as PFunction).m_Function.iType = DVPLUtils.EInterpolationType.ZERO_ORDER_LEFT;
}
}
public IntegralStochasticProcess(IStochasticProcess baseProcess, IManifold fiber) : base(baseProcess, fiber)
{
}
private ExtensionStochasticProcess CreateIntegralProcess(IStochasticProcess baseProcess)
{
return(new AffineIntegralStochasticProcess(baseProcess, new AffineSpace(_gain.RowCount), _gain, _offset));
}
public static IStochasticMapping Apply(this IStochasticProcess process, double time)
{
return(new StochasticProcessTimeSlice(process, time));
}
public void Populate(IStochasticProcess container, EstimationResult estimate)
{
bool found;
this.s0 = new ModelParameter(PopulateHelper.GetValue("S0", estimate.Names, estimate.Values, out found), this.s0.Description);
bool errors = RetrieveCurve(container.Context, false);
if (!errors)
{
PFunction rFunc = estimate.Objects[0] as PFunction;
PFunction rFuncDest = this.r.fVRef() as PFunction;
rFuncDest.Expr = rFunc.Expr;
PFunction qFunc = estimate.Objects[1] as PFunction;
PFunction qFuncDest = this.q.fVRef() as PFunction;
qFuncDest.Expr = qFunc.Expr;
//Calibrator assumes dividend yield is a step constant function, the simulation model must be coherent with that assumption.
qFuncDest.m_Function.iType = DVPLUtils.EInterpolationType.ZERO_ORDER_LEFT;
PFunction2D.PFunction2D localVolSrc = estimate.Objects[2] as PFunction2D.PFunction2D;
PFunction2D.PFunction2D localVolDest = this.localVol.fVRef() as PFunction2D.PFunction2D;
localVolDest.Expr = localVolSrc.Expr;
localVolDest.Interpolation = localVolSrc.Interpolation;
}
}
public StochasticProcessTimeSlice(IStochasticProcess process, double time)
{
_process = process;
_time = time;
}
public ExtensionStochasticProcess(IStochasticProcess baseProcess, IManifold fiber)
{
_baseProcess = baseProcess;
_stateSpace = new ProductManifold(_baseProcess.StateSpace, fiber);
}
/*
/// <summary>
/// Populate editable fields from name and value vectors
/// specific to HW.
/// </summary>
/// <param name="names">
/// An array with the names of the variable,
/// will search for alpha (or a1), sigma (or sigma1).
/// </param>
/// <param name="values">The values associated to the parameters in names.</param>
public void Populate(string[] names, double[] values)
{
bool found = false;
this.alpha1 = new ModelParameter(PopulateHelper.GetValue("alpha", "a1", names, values, out found), alphaDescription);
this.sigma1 = new ModelParameter(PopulateHelper.GetValue("sigma", "sigma1", names, values, out found), sigmaDescription);
this.lambda0 = new ModelParameter(PopulateHelper.GetValue("Lambda0", "lambda0", names, values, out found), lambda0Description);
}
*/
/// <summary>
/// Populate editable fields from name and value vectors
/// specific to the Heston extended process.
/// </summary>
/// <param name="stocProcess">
/// The stochastic process which is being referenced to.
/// </param>
/// <param name="estimate">
/// The estimation result which contains values and names of parameters.
/// It will be searched for S0, kappa, theta, sigma, V0 and rho.
/// </param>
public void Populate(IStochasticProcess stocProcess, EstimationResult estimate)
{
bool found;
this.alpha1 = new ModelParameter(PopulateHelper.GetValue("alpha", "a1", estimate.Names, estimate.Values, out found), alphaDescription);
this.sigma1 = new ModelParameter(PopulateHelper.GetValue("sigma", "sigma1", estimate.Names, estimate.Values, out found), sigmaDescription);
this.lambda0 = new ModelParameter(PopulateHelper.GetValue("Lambda0", "lambda0", estimate.Names, estimate.Values, out found), lambda0Description);
}
