/*
* (c) Copyright Christian P. Fries, Germany. All rights reserved. Contact: email@christian-fries.de.
*
* Created on 26.05.2013
*/
package net.finmath.montecarlo.interestrate.modelplugins;
import net.finmath.marketdata.model.curves.ForwardCurveInterface;
import net.finmath.stochastic.RandomVariableInterface;
/**
* Blended model (or displaced diffusion model) build on top of a standard covariance model.
*
* The model constructed for the <i>i</i>-th factor loading is
* <center>
* <i>(a L<sub>i,0</sub> + (1-a)L<sub>i</sub>(t)) F<sub>i</sub>(t)</i>
* </center>
* where <i>a</i> is the displacement and <i>L<sub>i</sub></i> is
* the realization of the <i>i</i>-th component of the stochastic process and
* <i>F<sub>i</sub></i> is the factor loading from the given covariance model.
*
* If a forward curve is provided, the deterministic value L<sub>i,0</sub> is
* calculated form this curve (using fixing in <i>T<sub>i</sub></i>.
*
* 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
*/
public class BlendedLocalVolatilityModel extends AbstractLIBORCovarianceModelParametric {
private AbstractLIBORCovarianceModelParametric covarianceModel;
private double displacement;
private ForwardCurveInterface forwardCurve;
private boolean isCalibrateable = false;
/**
* Displaced diffusion model build on top of a standard covariance model.
* The model constructed is <i>(a L<sub>0</sub> + (1-a)L) F</i> where <i>a</i> is
* the displacement and <i>L</i> is
* the component of the stochastic process and <i>F</i> 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 <i>F</i>.
* @param forwardCurve The given forward curve L<sub>0</sub>
* @param displacement The displacement <i>a</i>.
* @param isCalibrateable If true, the parameter <i>a</i> is a free parameter. Note that the covariance model may have its own parameter calibration settings.
*/
public BlendedLocalVolatilityModel(AbstractLIBORCovarianceModelParametric covarianceModel, ForwardCurveInterface forwardCurve, double 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 performs a linear interpolation of a log-normal model (a = 0) and a normal model (a = 1).
*
* The model constructed is <i>(a + (1-a)L) F</i> where <i>a</i> is
* the displacement and <i>L</i> is
* the component of the stochastic process and <i>F</i> 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 <i>F</i>.
* @param displacement The displacement <i>a</i>.
* @param isCalibrateable If true, the parameter <i>a</i> 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(covarianceModel, null, displacement, isCalibrateable);
}
@Override
public Object clone() {
return new BlendedLocalVolatilityModel((AbstractLIBORCovarianceModelParametric) covarianceModel.clone(), forwardCurve, displacement, isCalibrateable);
}
/**
* Returns the base covariance model, i.e., the model providing the factor loading <i>F</i>
* such that this model's <i>i</i>-th factor loading is
* <center>
* <i>(a L<sub>i,0</sub> + (1-a)L<sub>i</sub>(t)) F<sub>i</sub>(t)</i>
* </center>
* where <i>a</i> is the displacement and <i>L<sub>i</sub></i> is
* the realization of the <i>i</i>-th component of the stochastic process and
* <i>F<sub>i</sub></i> is the factor loading loading from the given covariance model.
*
* @return The base covariance model.
*/
public AbstractLIBORCovarianceModelParametric getBaseCovarianceModel() {
return covarianceModel;
}
@Override
public double[] getParameter() {
if(!isCalibrateable) return covarianceModel.getParameter();
double[] covarianceParameters = covarianceModel.getParameter();
if(covarianceParameters == null) return new double[] { displacement };
// Append displacement to the end of covarianceParameters
double[] jointParameters = new double[covarianceParameters.length+1];
System.arraycopy(covarianceParameters, 0, jointParameters, 0, covarianceParameters.length);
jointParameters[covarianceParameters.length] = displacement;
return jointParameters;
}
private void setParameter(double[] parameter) {
if(parameter == null || parameter.length == 0) return;
if(!isCalibrateable) {
covarianceModel = covarianceModel.getCloneWithModifiedParameters(parameter);
return;
}
double[] covarianceParameters = new double[parameter.length-1];
System.arraycopy(parameter, 0, covarianceParameters, 0, covarianceParameters.length);
covarianceModel = covarianceModel.getCloneWithModifiedParameters(covarianceParameters);
displacement = parameter[covarianceParameters.length];
}
@Override
public AbstractLIBORCovarianceModelParametric getCloneWithModifiedParameters(double[] parameters) {
BlendedLocalVolatilityModel model = (BlendedLocalVolatilityModel)this.clone();
model.setParameter(parameters);
return model;
}
@Override
public RandomVariableInterface[] getFactorLoading(int timeIndex, int component, RandomVariableInterface[] realizationAtTimeIndex) {
RandomVariableInterface[] factorLoading = covarianceModel.getFactorLoading(timeIndex, component, realizationAtTimeIndex);
double forward = 1.0;
if(forwardCurve != null) {
double timeToMaturity = getLiborPeriodDiscretization().getTime(component) - getTimeDiscretization().getTime(timeIndex);
// @TODO: Consider using a model context here
forward = forwardCurve.getForward(null, Math.max(timeToMaturity, 0.0));
}
if(realizationAtTimeIndex != null && realizationAtTimeIndex[component] != null) {
RandomVariableInterface localVolatilityFactor = realizationAtTimeIndex[component].mult(1.0-displacement).add(displacement * forward);
for (int factorIndex = 0; factorIndex < factorLoading.length; factorIndex++) {
factorLoading[factorIndex] = factorLoading[factorIndex].mult(localVolatilityFactor);
}
}
return factorLoading;
}
@Override
public RandomVariableInterface getFactorLoadingPseudoInverse(int timeIndex, int component, int factor, RandomVariableInterface[] realizationAtTimeIndex) {
throw new UnsupportedOperationException();
}
}