net.finmath.montecarlo.interestrate.models.covariance.BlendedLocalVolatilityModel Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of finmath-lib Show documentation
Show all versions of finmath-lib Show documentation
finmath lib is a Mathematical Finance Library in Java.
It provides algorithms and methodologies related to mathematical finance.
/*
* (c) Copyright Christian P. Fries, Germany. Contact: [email protected].
*
* Created on 26.05.2013
*/
package net.finmath.montecarlo.interestrate.models.covariance;
import java.util.Map;
import net.finmath.exception.CalculationException;
import net.finmath.marketdata.model.curves.ForwardCurve;
import net.finmath.montecarlo.AbstractRandomVariableFactory;
import net.finmath.montecarlo.RandomVariableFactory;
import net.finmath.stochastic.RandomVariable;
import net.finmath.stochastic.Scalar;
/**
* Blended model (or displaced diffusion model) build on top of a standard covariance model.
*
* The model constructed for the i-th factor loading is
* \[
* ( a + (1-a) L_{i}(t) ) F_{i}(t) \text{,}
* \]
* or
* \[
* ( a L_{i,0} + (1-a) L_{i}(t) ) F_{i}(t) \text{,}
* \]
* if an initial forward curve \( i \mapsto L_{i,0} \) is given,
* where a is the displacement or blending parameter and Li is
* the realization of the i-th component of the stochastic process and
* Fi is the factor loading from the given covariance model.
*
* If a forward curve is provided, the deterministic value Li,0 is
* calculated form this curve (using fixing in Ti),
* otherwise it is replaced by 1.
*
* The parameter of this model is a joint parameter vector, consisting
* of the parameter vector of the given base covariance model and
* appending the displacement parameter at the end.
*
* If this model is not calibrateable, its parameter vector is that of the
* covariance model, i.e., only the displacement parameter will be not
* part of the calibration.
*
* @author Christian Fries
* @version 1.0
*/
public class BlendedLocalVolatilityModel extends AbstractLIBORCovarianceModelParametric {
private static final long serialVersionUID = -5042461187735524974L;
private AbstractRandomVariableFactory randomVariableFactory;
private AbstractLIBORCovarianceModelParametric covarianceModel;
private RandomVariable displacement;
private ForwardCurve forwardCurve;
private boolean isCalibrateable = false;
/**
* Displaced diffusion model build on top of a standard covariance model.
* The model constructed is (a L0 + (1-a)L) F where a is
* the displacement and L is
* the component of the stochastic process and F is the factor loading
* from the given covariance model.
*
* The parameter of this model is a joint parameter vector, where the first
* entry is the displacement and the remaining entries are the parameter vector
* of the given base covariance model.
*
* If this model is not calibrateable, its parameter vector is that of the
* covariance model.
*
* @param covarianceModel The given covariance model specifying the factor loadings F.
* @param forwardCurve The given forward curve L0
* @param displacement The displacement a.
* @param isCalibrateable If true, the parameter a is a free parameter. Note that the covariance model may have its own parameter calibration settings.
*/
public BlendedLocalVolatilityModel(AbstractLIBORCovarianceModelParametric covarianceModel, ForwardCurve forwardCurve, RandomVariable displacement, boolean isCalibrateable) {
super(covarianceModel.getTimeDiscretization(), covarianceModel.getLiborPeriodDiscretization(), covarianceModel.getNumberOfFactors());
this.covarianceModel = covarianceModel;
this.forwardCurve = forwardCurve;
this.displacement = displacement;
this.isCalibrateable = isCalibrateable;
}
/**
* Displaced diffusion model build on top of a standard covariance model.
* The model constructed is (a L0 + (1-a)L) F where a is
* the displacement and L is
* the component of the stochastic process and F is the factor loading
* from the given covariance model.
*
* The parameter of this model is a joint parameter vector, where the first
* entry is the displacement and the remaining entries are the parameter vector
* of the given base covariance model.
*
* If this model is not calibrateable, its parameter vector is that of the
* covariance model.
*
* @param randomVariableFactory The factory used to create RandomVariable objects from constants.
* @param covarianceModel The given covariance model specifying the factor loadings F.
* @param forwardCurve The given forward curve L0
* @param displacement The displacement a.
* @param isCalibrateable If true, the parameter a is a free parameter. Note that the covariance model may have its own parameter calibration settings.
*/
public BlendedLocalVolatilityModel(AbstractRandomVariableFactory randomVariableFactory, AbstractLIBORCovarianceModelParametric covarianceModel, ForwardCurve forwardCurve, double displacement, boolean isCalibrateable) {
super(covarianceModel.getTimeDiscretization(), covarianceModel.getLiborPeriodDiscretization(), covarianceModel.getNumberOfFactors());
this.randomVariableFactory = randomVariableFactory;
this.covarianceModel = covarianceModel;
this.forwardCurve = forwardCurve;
this.displacement = randomVariableFactory.createRandomVariable(displacement);
this.isCalibrateable = isCalibrateable;
}
/**
* Displaced diffusion model build on top of a standard covariance model.
*
* The model performs a linear interpolation of a log-normal model (a = 0) and a normal model (a = 1).
*
* The model constructed is (a + (1-a)L) F where a is
* the displacement and L is
* the component of the stochastic process and F is the factor loading
* loading from the given covariance model.
*
* The parameter of this model is a joint parameter vector, where the first
* entry is the displacement and the remaining entries are the parameter vector
* of the given base covariance model.
*
* If this model is not calibrateable, its parameter vector is that of the
* covariance model.
*
* @param randomVariableFactory The factory used to create RandomVariable objects from constants.
* @param covarianceModel The given covariance model specifying the factor loadings F.
* @param displacement The displacement a.
* @param isCalibrateable If true, the parameter a is a free parameter. Note that the covariance model may have its own parameter calibration settings.
*/
public BlendedLocalVolatilityModel(AbstractRandomVariableFactory randomVariableFactory, AbstractLIBORCovarianceModelParametric covarianceModel, double displacement, boolean isCalibrateable) {
this(randomVariableFactory, covarianceModel, null, displacement, isCalibrateable);
}
/**
* Displaced diffusion model build on top of a standard covariance model.
* The model constructed is (a L0 + (1-a)L) F where a is
* the displacement and L is
* the component of the stochastic process and F is the factor loading
* from the given covariance model.
*
* The parameter of this model is a joint parameter vector, where the first
* entry is the displacement and the remaining entries are the parameter vector
* of the given base covariance model.
*
* If this model is not calibrateable, its parameter vector is that of the
* covariance model.
*
* @param covarianceModel The given covariance model specifying the factor loadings F.
* @param forwardCurve The given forward curve L0
* @param displacement The displacement a.
* @param isCalibrateable If true, the parameter a is a free parameter. Note that the covariance model may have its own parameter calibration settings.
*/
public BlendedLocalVolatilityModel(AbstractLIBORCovarianceModelParametric covarianceModel, ForwardCurve forwardCurve, double displacement, boolean isCalibrateable) {
this(new RandomVariableFactory(), covarianceModel, forwardCurve, displacement, isCalibrateable);
}
/**
* Displaced diffusion model build on top of a standard covariance model.
*
* The model performs a linear interpolation of a log-normal model (a = 0) and a normal model (a = 1).
*
* The model constructed is (a + (1-a)L) F where a is
* the displacement and L is
* the component of the stochastic process and F is the factor loading
* loading from the given covariance model.
*
* The parameter of this model is a joint parameter vector, where the first
* entry is the displacement and the remaining entries are the parameter vector
* of the given base covariance model.
*
* If this model is not calibrateable, its parameter vector is that of the
* covariance model.
*
* @param covarianceModel The given covariance model specifying the factor loadings F.
* @param displacement The displacement a.
* @param isCalibrateable If true, the parameter a is a free parameter. Note that the covariance model may have its own parameter calibration settings.
*/
public BlendedLocalVolatilityModel(AbstractLIBORCovarianceModelParametric covarianceModel, double displacement, boolean isCalibrateable) {
this(new RandomVariableFactory(), covarianceModel, displacement, isCalibrateable);
}
@Override
public Object clone() {
return new BlendedLocalVolatilityModel(randomVariableFactory, (AbstractLIBORCovarianceModelParametric) covarianceModel.clone(), forwardCurve, displacement.doubleValue(), isCalibrateable);
}
/**
* Returns the base covariance model, i.e., the model providing the factor loading F
* such that this model's i-th factor loading is
*
* (a Li,0 + (1-a)Li(t)) Fi(t)
*
* where a is the displacement and Li is
* the realization of the i-th component of the stochastic process and
* Fi is the factor loading loading from the given covariance model.
*
* @return The base covariance model.
*/
public AbstractLIBORCovarianceModelParametric getBaseCovarianceModel() {
return covarianceModel;
}
@Override
public RandomVariable[] getParameter() {
if(!isCalibrateable) {
return covarianceModel.getParameter();
}
RandomVariable[] covarianceParameters = covarianceModel.getParameter();
if(covarianceParameters == null) {
return new RandomVariable[] { displacement };
}
// Append displacement to the end of covarianceParameters
RandomVariable[] jointParameters = new RandomVariable[covarianceParameters.length+1];
System.arraycopy(covarianceParameters, 0, jointParameters, 0, covarianceParameters.length);
jointParameters[covarianceParameters.length] = displacement;
return jointParameters;
}
@Override
public double[] getParameterAsDouble() {
RandomVariable[] parameters = getParameter();
double[] parametersAsDouble = new double[parameters.length];
for(int i=0; i dataModified)
throws CalculationException {
AbstractRandomVariableFactory randomVariableFactory = this.randomVariableFactory;
AbstractLIBORCovarianceModelParametric covarianceModel = this.covarianceModel;
ForwardCurve forwardCurve = this.forwardCurve;
double displacement = this.displacement.doubleValue();
boolean isCalibrateable = this.isCalibrateable;
if(dataModified != null) {
if (!dataModified.containsKey("covarianceModel")) {
covarianceModel = covarianceModel.getCloneWithModifiedData(dataModified);
}
// Explicitly passed covarianceModel has priority
covarianceModel = (AbstractLIBORCovarianceModelParametric)dataModified.getOrDefault("covarianceModel", covarianceModel);
isCalibrateable = (boolean)dataModified.getOrDefault("isCalibrateable", isCalibrateable);
randomVariableFactory = (AbstractRandomVariableFactory)dataModified.getOrDefault("randomVariableFactory", randomVariableFactory);
forwardCurve = (ForwardCurve)dataModified.getOrDefault("forwardCurve", forwardCurve);
// Explicitly passed RVFactory has priority (consistency)
if((dataModified.getOrDefault("displacement", displacement) instanceof RandomVariable)) {
displacement = ((RandomVariable)dataModified.get("displacement")).doubleValue();
}else {
displacement = (double)dataModified.getOrDefault("displacement", displacement);
}
}
AbstractLIBORCovarianceModelParametric newModel = new DisplacedLocalVolatilityModel(covarianceModel, displacement, isCalibrateable);
return newModel;
}
}
© 2015 - 2025 Weber Informatics LLC | Privacy Policy