public DupireProcess()
{
this.s0 = new ModelParameter(string.Empty, "S0");
this.r = new ModelParameter(string.Empty, "Time Dependent Risk Free Rate (Zero Rate)");
this.q = new ModelParameter(string.Empty, "Time Dependent Continuous Dividend Yield");
this.localVol = new ModelParameter(string.Empty, "Local Volatility");
}
/// <summary>
/// Prepares the serialized model parameters with the provided values.
/// </summary>
private void SetParametersValue()
{
this.k = new ModelParameter(this.parameterValues[0], parameterNames[0]);
this.theta = new ModelParameter(this.parameterValues[1], parameterNames[1]);
this.sigma = new ModelParameter(this.parameterValues[2], parameterNames[2]);
this.r0 = new ModelParameter(this.parameterValues[3], parameterNames[3]);
}
/// <summary>
/// Initializes a new instance of the HW1 class given alpha, sigma, lambda and a zero rate reference.
/// </summary>
/// <param name="alpha">The rate of the mean reversion to be used to initialize HW.</param>
/// <param name="sigma">The standard deviation to be used to initialize HW.</param>
/// <param name="lambda">The market price of risk to be used to initialize HW.</param>
/// <param name="zeroRateReference">
/// Reference to the zero rate to be used to initialize HW.
/// </param>
public HW1(double alpha, double sigma, double lambda, string zeroRateReference)
{
this.alpha1 = new ModelParameter(alpha, alphaDescription);
this.sigma1 = new ModelParameter(sigma, sigmaDescription);
this.lambda0 = new ModelParameter(lambda, lambda0Description);
this.zrReference = new ModelParameter(zeroRateReference, zeroRateDescription);
}
/// <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 void OnDeserialized(StreamingContext context)
{
if (this.driftAdjustment == null)
{
this.driftAdjustment = new ModelParameter(0, driftAdjustmentDescription);
}
}
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;
}
}
void Init()
{
this.spot = new ModelParameter(0, spotLabel);
this.mu = new ModelParameter(0, muLabel);
this.lambda = new ModelParameter(0, lambdaLabel);
this.sigma = new ModelParameter(0, sigmaLabel);
}
/// <summary>
/// Constructor which builds a new SquareGaussianModel with provided values.
/// </summary>
/// <param name="sigma">The sigma factor of the model.</param>
/// <param name="lambda">The parameter is not used.</param>
/// <param name="a1">The alpha factor of the model.</param>
/// <param name="zeroRateReference">A reference to a zero rate.</param>
public SquaredGaussianModel(double sigma, double lambda, double a1, string zeroRateReference)
{
this.sigma1 = new ModelParameter(sigma, sigma1Description);
this.a1 = new ModelParameter(a1, a1Description);
this.zr = new ModelParameter(zeroRateReference, zrDescription);
this.lamda0 = new ModelParameter(lambda, lambda0Description);
}
/*
* /// <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);
}
/// <summary>
/// Sets some default values for the parameters.
/// * k = 2.5
/// * theta = 0.4
/// * sigma = 0.2
/// * S0 = 1
/// * V0 = 0.3.
/// </summary>
public void DefaultInstance()
{
this.k = new ModelParameter(2.5, "k");
this.theta = new ModelParameter(0.4, "theta");
this.sigma = new ModelParameter(0.2, "sigma");
this.S0 = new ModelParameter(1, "S0");
this.V0 = new ModelParameter(0.3, "V0");
}
/// <summary>
/// Populate editable fields from name and value vectors
/// specific to Pelsser.
/// </summary>
/// <param name="names">
/// An array with the names of the variable,
/// will search for alpha1 (or a), sigma1 (or sigma) and lamba0.
/// </param>
/// <param name="values">The values associated to the parameters in names.</param>
public void Populate(string[] names, double[] values)
{
bool found;
this.a1 = new ModelParameter(PopulateHelper.GetValue("alpha1", "a", names, values, out found), a1Description);
this.sigma1 = new ModelParameter(PopulateHelper.GetValue("sigma1", "sigma", names, values, out found), sigma1Description);
this.lamda0 = new ModelParameter(PopulateHelper.GetValue("lambda0", "lambda", names, values, out found), lambda0Description);
}
void OnDeserialized(StreamingContext context)
{
//Fix lambda0: some old files have it set to null
if (this.lamda0 == null)
{
this.lamda0 = new ModelParameter(0, lambda0Description);
}
}
/// <summary>
/// Initializes a new instance of the HestonExtendedProcess class.
/// This is the default constructor setting all parameters to zero/empty.
/// </summary>
public HestonExtendedProcess()
{
InitOptionalFields();
this.k = new ModelParameter(0.0, "k");
this.theta = new ModelParameter(0.0, "theta");
this.sigma = new ModelParameter(0.0, "sigma");
this.S0 = new ModelParameter(0.0, "S0");
this.V0 = new ModelParameter(0.0, "V0");
}
public void Populate(string[] names, double[] values)
{
bool found;
this.spot = new ModelParameter(PopulateHelper.GetValue(spotLabel, names, values, out found), spotLabel);
this.mu = new ModelParameter(PopulateHelper.GetValue(muLabel, names, values, out found), muLabel);
this.lambda = new ModelParameter(PopulateHelper.GetValue(lambdaLabel, names, values, out found), lambdaLabel);
this.sigma = new ModelParameter(PopulateHelper.GetValue(sigmaLabel, names, values, out found), sigmaLabel);
}
/// <summary>
/// Initializes a new instance of the VarianceGamma class.
/// Defines a new process with given parameter values.
/// </summary>
/// <param name="s0">Starting value.</param>
/// <param name="theta">VG theta parameter.</param>
/// <param name="sigma">VG sigma parameter.</param>
/// <param name="nu">VG nu parameter.</param>
/// <param name="r">Risk free rate.</param>
/// <param name="q">Continuous dividend yield.</param>
public VarianceGamma(double s0, double theta, double sigma, double nu, double r, double q)
{
this.s0 = new ModelParameter(s0, s0Description);
this.theta = new ModelParameter(theta, thetaDescription);
this.sigma = new ModelParameter(sigma, sigmaDescription);
this.nu = new ModelParameter(nu, nuDescription);
this.rate = new ModelParameter(r, rateDescription);
this.dividend = new ModelParameter(q, dividendDescription);
}
/// <summary>
/// Populate editable fields from name and value vectors
/// specific to HW2.
/// </summary>
/// <param name="names">
/// An array with the names of the variable,
/// will search for alpha1 (or a1), sigma1 (or sigma), alpha2 (or a2), sigma2 and rho.
/// </param>
/// <param name="values">The values associated to the parameters in names.</param>
public void Populate(string[] names, double[] values)
{
bool found = false;
this._a1 = new ModelParameter(PopulateHelper.GetValue("alpha1", "a1", names, values, out found), a1Description);
this._s1 = new ModelParameter(PopulateHelper.GetValue("sigma1", "sigma", names, values, out found), s1Description);
this._a2 = new ModelParameter(PopulateHelper.GetValue("alpha2", "a2", names, values, out found), a2Description);
this._s2 = new ModelParameter(PopulateHelper.GetValue("sigma2", names, values, out found), s2Description);
this._rho = new ModelParameter(PopulateHelper.GetValue("rho", names, values, out found), rhoDescription);
}
/// <summary>
/// Initializes a new instance of the HestonProcess class.
/// This is the default constructor and sets all parameters to 0.0.
/// </summary>
public HestonProcess()
{
this.r = new ModelParameter(0.0, "r");
this.q = new ModelParameter(0.0, "q");
this.k = new ModelParameter(0.0, "k");
this.theta = new ModelParameter(0.0, "theta");
this.sigma = new ModelParameter(0.0, "sigma");
this.S0 = new ModelParameter(0.0, "S0");
this.V0 = new ModelParameter(0.0, "V0");
}
/// <summary>
/// Initializes a new instance of the <see cref="HullAndWhiteTwoFactors.HW2"/> class.
/// This is the default constructor and sets the
/// default values of several components of the model:
/// * alpha 1 = 0.1
/// * sigma 1 = 0.001
/// * alpha 2 = 0.01
/// * sigma 2 = 0.001
/// * rho = 0.001
/// * No zero rate.
/// * drift correction = 0.
/// </summary>
public HW2()
{
this._zr = new ModelParameter(string.Empty, zeroRateDescription);
this._a1 = new ModelParameter(0.1, a1Description);
this._s1 = new ModelParameter(0.001, s1Description);
this._a2 = new ModelParameter(0.01, a2Description);
this._s2 = new ModelParameter(0.001, s2Description);
this._rho = new ModelParameter(0.001, rhoDescription);
this.driftAdjustment = new ModelParameter(0, driftAdjustmentDescription);
}
/// <summary>
/// Initializes a new instance of the HestonProcess class.
/// This is the default constructor and sets all parameters to 0.0.
/// </summary>
public HestonProcess()
{
this.r = new ModelParameter(0.0, "r");
this.q = new ModelParameter(0.0, "q");
this.k = new ModelParameter(0.0, "k");
this.theta = new ModelParameter(0.0, "theta");
this.sigma = new ModelParameter(0.0, "sigma");
this.S0 = new ModelParameter(0.0, "S0");
this.V0 = new ModelParameter(0.0, "V0");
}
/// <summary>
/// Initializes a new instance of the VarianceGamma class.
/// Initializes the object based on the serialized data.
/// </summary>
/// <param name="info">The SerializationInfo that holds the serialized object data.</param>
/// <param name="context">The StreamingContext that contains contextual
/// information about the source.</param>
protected VarianceGamma(SerializationInfo info, StreamingContext context)
{
int readVersion = info.GetInt32("varianceGammaVersion");
this.s0 = (IModelParameter)ObjectSerialization.GetValue2(info, "s0", typeof(IModelParameter));
this.theta = (IModelParameter)ObjectSerialization.GetValue2(info, "theta", typeof(IModelParameter));
this.sigma = (IModelParameter)ObjectSerialization.GetValue2(info, "sigma", typeof(IModelParameter));
this.nu = (IModelParameter)ObjectSerialization.GetValue2(info, "nu", typeof(IModelParameter));
this.rate = (IModelParameter)ObjectSerialization.GetValue2(info, "rate", typeof(IModelParameter));
this.dividend = (IModelParameter)ObjectSerialization.GetValue2(info, "dividend", typeof(IModelParameter));
}
/// <summary>
/// Populate editable fields from name and value vectors specific to VG.
/// </summary>
/// <param name="names">
/// An array with the names of the variables.
/// </param>
/// <param name="values">The values associated to the parameters in names.</param>
public void Populate(string[] names, double[] values)
{
bool found = false;
this.theta = new ModelParameter(PopulateHelper.GetValue("S0", names, values, out found), thetaDescription);
this.sigma = new ModelParameter(PopulateHelper.GetValue("theta", names, values, out found), sigmaDescription);
this.theta = new ModelParameter(PopulateHelper.GetValue("sigma", names, values, out found), thetaDescription);
this.theta = new ModelParameter(PopulateHelper.GetValue("nu", names, values, out found), thetaDescription);
this.theta = new ModelParameter(PopulateHelper.GetValue("rate", names, values, out found), thetaDescription);
this.theta = new ModelParameter(PopulateHelper.GetValue("dividend", names, values, out found), thetaDescription);
}
private void OnDeserialized(StreamingContext context)
{
if (this.driftAdjustment == null)
{
this.driftAdjustment = new ModelParameter(0, driftAdjustmentDescription);
}
if (this.lambda0 == null)
{
this.lambda0 = new ModelParameter(0, lambda0Description);
}
}
/// <summary>
/// Initializes the zr and dy reference to empty values, in case they are not initialized.
/// </summary>
private void InitOptionalFields()
{
if (this.zrReference == null)
{
this.zrReference = new ModelParameter(string.Empty, "zero rate curve");
}
if (this.dyReference == null)
{
this.dyReference = new ModelParameter(string.Empty, "dividend yield curve");
}
}
/// <summary>
/// Sets the default values for the components.
/// </summary>
public void DefaultValues()
{
this.k1 = new ModelParameter(0.026758131);
this.k2 = new ModelParameter(0.226406137);
this.theta1 = new ModelParameter(0.023147821);
this.theta2 = new ModelParameter(0.01);
this.sigma1 = new ModelParameter(0.1);
this.sigma2 = new ModelParameter(0.0001);
this.startingValue1 = new ModelParameter(0.00001);
this.startingValue2 = new ModelParameter(0.00001);
this.ZRReference = (ModelParameter)"@ZR";
SetDescription();
}
/// <summary>
/// Creates a list of all the sub-objects that can be edited.
/// </summary>
/// <param name="recursive">
/// The parameter is not used.
/// </param>
/// <returns>
/// The created list with all the sub objects that can be edited.
/// </returns>
public List <IExportable> ExportObjects(bool recursive)
{
List <IExportable> parameters = new List <IExportable>();
parameters.Add(this.a1);
parameters.Add(this.sigma1);
/// Manage the case in which lamda0 is null
if (this.lamda0 == null)
{
this.lamda0 = new ModelParameter(0.0, lambda0Description);
}
parameters.Add(this.lamda0);
parameters.Add(this.zr);
return(parameters);
}
/// <summary>
/// Parses the process (in this case nothing has to be done).
/// </summary>
/// <param name="context">The project representing the context of the parsing.</param>
/// <returns>true if the the parsing caused errors; otherwise false.</returns>
public bool Parse(IProject context)
{
bool errors = false;
errors = this.s0.Parse(context);
errors = BoolHelper.AddBool(errors, this.q.Parse(context));
errors = BoolHelper.AddBool(errors, this.r.Parse(context));
errors = BoolHelper.AddBool(errors, this.localVol.Parse(context));
this.context = new DupireContext();
this.context.s0 = this.s0.fV();
this.context.q = this.q.fVRef() as IFunction;
this.context.r = this.r.fVRef() as IFunction;
this.context.localVol = this.localVol.fVRef() as IFunction;
return(RetrieveCurve(context, errors));
}
/// <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>
/// Prepares the serialized model parameters with the provided values.
/// </summary>
private void SetParametersValue()
{
this.k = new ModelParameter(this.parameterValues[0], parameterNames[0]);
this.theta = new ModelParameter(this.parameterValues[1], parameterNames[1]);
this.sigma = new ModelParameter(this.parameterValues[2], parameterNames[2]);
this.r0 = new ModelParameter(this.parameterValues[3], parameterNames[3]);
}
public DupireProcess()
{
this.s0 = new ModelParameter(string.Empty, "S0");
this.r = new ModelParameter(string.Empty, "Time Dependent Risk Free Rate (Zero Rate)");
this.q = new ModelParameter(string.Empty, "Time Dependent Continuous Dividend Yield");
this.localVol = new ModelParameter(string.Empty, "Local Volatility");
}
/// <summary>
/// Associate the process to a zero rate and a dividend yield defined in the Fairmat model
/// (e.g. @zr1).
/// </summary>
/// <param name='zrstring'>
/// The zero rate reference.
/// </param>
/// <param name='dystring'>
/// The dividend yield reference.
/// </param>
public void SetCurveReference(string zrstring, string dystring)
{
this.zrReference = new ModelParameter(zrstring);
this.dyReference = new ModelParameter(dystring);
}
/// <summary>
/// Sets some default values for the parameters.
/// * r = 0.05
/// * q = 0.03
/// * k = 2.5
/// theta = 0.4
/// sigma = 0.2
/// S0 = 100
/// V0 = 0.3.
/// </summary>
public void DefaultInstance()
{
this.r = new ModelParameter(0.05, "r");
this.q = new ModelParameter(0.03, "q");
this.k = new ModelParameter(2.5, "k");
this.theta = new ModelParameter(0.4, "theta");
this.sigma = new ModelParameter(0.2, "sigma");
this.S0 = new ModelParameter(100, "S0");
this.V0 = new ModelParameter(0.3, "V0");
}
/// <summary>
/// Associate the process to a zero rate defined in the Fairmat model
/// (e.g. @zr1).
/// </summary>
/// <param name='zr'>
/// The zero rate reference.
/// </param>
public void SetZeroRateReference(string zr)
{
this.zr = new ModelParameter(zr, zrDescription);
}
/// <summary>
/// Creates a list of all the sub-objects that can be edited.
/// </summary>
/// <param name="recursive">
/// The parameter is not used.
/// </param>
/// <returns>
/// The created list with all the sub objects that can be edited.
/// </returns>
public List<IExportable> ExportObjects(bool recursive)
{
List<IExportable> parameters = new List<IExportable>();
parameters.Add(this.a1);
parameters.Add(this.sigma1);
/// Manage the case in which lamda0 is null
if (this.lamda0 == null)
this.lamda0 = new ModelParameter(0.0, lambda0Description);
parameters.Add(this.lamda0);
parameters.Add(this.zr);
return parameters;
}
/// <summary>
/// Associate the process to a zero rate defined in the Fairmat model
/// (e.g. @zr1).
/// </summary>
/// <param name='zr'>
/// The zero rate reference.
/// </param>
public void SetZeroRateReference(string zr)
{
this._zr = new ModelParameter(zr, "Zero Rate");
}
/// <summary>
/// Initializes a new instance of the <see cref="HullAndWhiteTwoFactors.HW2"/> class.
/// This is the default constructor and sets the
/// default values of several components of the model:
/// * alpha 1 = 0.1
/// * sigma 1 = 0.001
/// * alpha 2 = 0.01
/// * sigma 2 = 0.001
/// * rho = 0.001
/// * No zero rate.
/// * drift correction = 0.
/// </summary>
public HW2()
{
this._zr = new ModelParameter(string.Empty, zeroRateDescription);
this._a1 = new ModelParameter(0.1, a1Description);
this._s1 = new ModelParameter(0.001, s1Description);
this._a2 = new ModelParameter(0.01, a2Description);
this._s2 = new ModelParameter(0.001, s2Description);
this._rho = new ModelParameter(0.001, rhoDescription);
this.driftAdjustment = new ModelParameter(0, driftAdjustmentDescription);
}
/// <summary>
/// Populate editable fields from name and value vectors
/// specific to HW2.
/// </summary>
/// <param name="names">
/// An array with the names of the variable,
/// will search for alpha1 (or a1), sigma1 (or sigma), alpha2 (or a2), sigma2 and rho.
/// </param>
/// <param name="values">The values associated to the parameters in names.</param>
public void Populate(string[] names, double[] values)
{
bool found = false;
this._a1 = new ModelParameter(PopulateHelper.GetValue("alpha1", "a1", names, values, out found), a1Description);
this._s1 = new ModelParameter(PopulateHelper.GetValue("sigma1", "sigma", names, values, out found), s1Description);
this._a2 = new ModelParameter(PopulateHelper.GetValue("alpha2", "a2", names, values, out found), a2Description);
this._s2 = new ModelParameter(PopulateHelper.GetValue("sigma2", names, values, out found), s2Description);
this._rho = new ModelParameter(PopulateHelper.GetValue("rho", names, values, out found), rhoDescription);
}
/// <summary>
/// Sets the default values for the components.
/// </summary>
public void DefaultValues()
{
this.k1 = new ModelParameter(0.026758131);
this.k2 = new ModelParameter(0.226406137);
this.theta1 = new ModelParameter(0.023147821);
this.theta2 = new ModelParameter(0.01);
this.sigma1 = new ModelParameter(0.1);
this.sigma2 = new ModelParameter(0.0001);
this.startingValue1 = new ModelParameter(0.00001);
this.startingValue2 = new ModelParameter(0.00001);
this.ZRReference = (ModelParameter)"@ZR";
SetDescription();
}
/// <summary>
/// Populate editable fields from name and value vectors specific to VG.
/// </summary>
/// <param name="names">
/// An array with the names of the variables.
/// </param>
/// <param name="values">The values associated to the parameters in names.</param>
public void Populate(string[] names, double[] values)
{
bool found = false;
this.theta = new ModelParameter(PopulateHelper.GetValue("S0", names, values, out found), thetaDescription);
this.sigma = new ModelParameter(PopulateHelper.GetValue("theta", names, values, out found), sigmaDescription);
this.theta = new ModelParameter(PopulateHelper.GetValue("sigma", names, values, out found), thetaDescription);
this.theta = new ModelParameter(PopulateHelper.GetValue("nu", names, values, out found), thetaDescription);
this.theta = new ModelParameter(PopulateHelper.GetValue("rate", names, values, out found), thetaDescription);
this.theta = new ModelParameter(PopulateHelper.GetValue("dividend", names, values, out found), thetaDescription);
}
/// <summary>
/// Initializes a new instance of the VarianceGamma class.
/// Initializes the object based on the serialized data.
/// </summary>
/// <param name="info">The SerializationInfo that holds the serialized object data.</param>
/// <param name="context">The StreamingContext that contains contextual
/// information about the source.</param>
protected VarianceGamma(SerializationInfo info, StreamingContext context)
{
int readVersion = info.GetInt32("varianceGammaVersion");
this.s0 = (IModelParameter)ObjectSerialization.GetValue2(info, "s0", typeof(IModelParameter));
this.theta = (IModelParameter)ObjectSerialization.GetValue2(info, "theta", typeof(IModelParameter));
this.sigma = (IModelParameter)ObjectSerialization.GetValue2(info, "sigma", typeof(IModelParameter));
this.nu = (IModelParameter)ObjectSerialization.GetValue2(info, "nu", typeof(IModelParameter));
this.rate = (IModelParameter)ObjectSerialization.GetValue2(info, "rate", typeof(IModelParameter));
this.dividend = (IModelParameter)ObjectSerialization.GetValue2(info, "dividend", typeof(IModelParameter));
}
/// <summary>
/// Initializes a new instance of the VarianceGamma class.
/// Defines a new process with given parameter values.
/// </summary>
/// <param name="s0">Starting value.</param>
/// <param name="theta">VG theta parameter.</param>
/// <param name="sigma">VG sigma parameter.</param>
/// <param name="nu">VG nu parameter.</param>
/// <param name="r">Risk free rate.</param>
/// <param name="q">Continuous dividend yield.</param>
public VarianceGamma(double s0, double theta, double sigma, double nu, double r, double q)
{
this.s0 = new ModelParameter(s0, s0Description);
this.theta = new ModelParameter(theta, thetaDescription);
this.sigma = new ModelParameter(sigma, sigmaDescription);
this.nu = new ModelParameter(nu, nuDescription);
this.rate = new ModelParameter(r, rateDescription);
this.dividend = new ModelParameter(q, dividendDescription);
}
/// <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;
}
}
/// <summary>
/// Parses the process (in this case nothing has to be done).
/// </summary>
/// <param name="context">The project representing the context of the parsing.</param>
/// <returns>true if the the parsing caused errors; otherwise false.</returns>
public bool Parse(IProject context)
{
bool errors = false;
errors = this.s0.Parse(context);
errors = BoolHelper.AddBool(errors, this.q.Parse(context));
errors = BoolHelper.AddBool(errors, this.r.Parse(context));
errors = BoolHelper.AddBool(errors, this.localVol.Parse(context));
this.context = new DupireContext();
this.context.s0 = this.s0.fV();
this.context.q = this.q.fVRef() as IFunction;
this.context.r = this.r.fVRef() as IFunction;
this.context.localVol = this.localVol.fVRef() as IFunction;
return RetrieveCurve(context, errors);
}
public void OnDeserialized(StreamingContext context)
{
if (this.driftAdjustment == null)
{
this.driftAdjustment = new ModelParameter(0, driftAdjustmentDescription);
}
}
/*
/// <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);
}
/// <summary>
/// Initializes the zr and dy reference to empty values, in case they are not initialized.
/// </summary>
private void InitOptionalFields()
{
if (this.zrReference == null)
this.zrReference = new ModelParameter(string.Empty, "zero rate curve");
if (this.dyReference == null)
this.dyReference = new ModelParameter(string.Empty, "dividend yield curve");
}
/// <summary>
/// Associate the process to a zero rate defined in the Fairmat model
/// (e.g. @zr1).
/// </summary>
/// <param name='zr'>
/// The zero rate reference.
/// </param>
public void SetZeroRateReference(string zr)
{
this.zrReference = new ModelParameter(zr, "Zero Rate");
}
/// <summary>
/// Constructor which builds a new SquareGaussianModel with provided values.
/// </summary>
/// <param name="sigma">The sigma factor of the model.</param>
/// <param name="lambda">The parameter is not used.</param>
/// <param name="a1">The alpha factor of the model.</param>
/// <param name="zeroRateReference">A reference to a zero rate.</param>
public SquaredGaussianModel(double sigma, double lambda, double a1, string zeroRateReference)
{
this.sigma1 = new ModelParameter(sigma, sigma1Description);
this.a1 = new ModelParameter(a1, a1Description);
this.zr = new ModelParameter(zeroRateReference, zrDescription);
this.lamda0 = new ModelParameter(lambda, lambda0Description);
}
/// <summary>
/// Associate the process to a zero rate and a dividend yield defined in the Fairmat model
/// (e.g. @zr1).
/// </summary>
/// <param name='zrstring'>
/// The zero rate reference.
/// </param>
/// <param name='dystring'>
/// The dividend yield reference.
/// </param>
public void SetCurveReference(string zrstring, string dystring)
{
this.zrReference = new ModelParameter(zrstring);
this.dyReference = new ModelParameter(dystring);
}
/// <summary>
/// Populate editable fields from name and value vectors
/// specific to Pelsser.
/// </summary>
/// <param name="names">
/// An array with the names of the variable,
/// will search for alpha1 (or a), sigma1 (or sigma) and lamba0.
/// </param>
/// <param name="values">The values associated to the parameters in names.</param>
public void Populate(string[] names, double[] values)
{
bool found;
this.a1 = new ModelParameter(PopulateHelper.GetValue("alpha1", "a", names, values, out found), a1Description);
this.sigma1 = new ModelParameter(PopulateHelper.GetValue("sigma1", "sigma", names, values, out found), sigma1Description);
this.lamda0 = new ModelParameter(PopulateHelper.GetValue("lambda0", "lambda", names, values, out found), lambda0Description);
}
private void OnDeserialized(StreamingContext context)
{
if (this.driftAdjustment == null)
this.driftAdjustment = new ModelParameter(0, driftAdjustmentDescription);
if (this.lambda0 == null)
this.lambda0= new ModelParameter(0, lambda0Description);
}
void OnDeserialized(StreamingContext context)
{
//Fix lambda0: some old files have it set to null
if (this.lamda0 == null)
this.lamda0 = new ModelParameter(0, lambda0Description);
}
/// <summary>
/// Initializes a new instance of the HW1 class given alpha, sigma, lambda and a zero rate reference.
/// </summary>
/// <param name="alpha">The rate of the mean reversion to be used to initialize HW.</param>
/// <param name="sigma">The standard deviation to be used to initialize HW.</param>
/// <param name="lambda">The market price of risk to be used to initialize HW.</param>
/// <param name="zeroRateReference">
/// Reference to the zero rate to be used to initialize HW.
/// </param>
public HW1(double alpha, double sigma, double lambda, string zeroRateReference)
{
this.alpha1 = new ModelParameter(alpha, alphaDescription);
this.sigma1 = new ModelParameter(sigma, sigmaDescription);
this.lambda0 = new ModelParameter(lambda, lambda0Description);
this.zrReference = new ModelParameter(zeroRateReference, zeroRateDescription);
}
/// <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>
/// Associate the process to a zero rate defined in the Fairmat model
/// (e.g. @zr1).
/// </summary>
/// <param name='zr'>
/// The zero rate reference.
/// </param>
public void SetZeroRateReference(string zr)
{
this.zr = new ModelParameter(zr, zrDescription);
}
/// <summary>
/// Initializes a new instance of the HestonExtendedProcess class.
/// This is the default constructor setting all parameters to zero/empty.
/// </summary>
public HestonExtendedProcess()
{
InitOptionalFields();
this.k = new ModelParameter(0.0, "k");
this.theta = new ModelParameter(0.0, "theta");
this.sigma = new ModelParameter(0.0, "sigma");
this.S0 = new ModelParameter(0.0, "S0");
this.V0 = new ModelParameter(0.0, "V0");
}
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;
}
}
