/*
* (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.stochastic.RandomVariableInterface;
/**
* Special variant of a blended model (or displaced diffusion model)
* build on top of a standard covariance model
* using the special function corresponding to the Hull-White local volatility.
*
* The model constructed for the <i>i</i>-th factor loading is
* <center>
* <i>(1+L<sub>i</sub>(t) d) F<sub>i</sub>(t)</i>
* </center>
* where <i>d</i> is a constant (the period length), <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 this model is combined with an exponential decay volatility model
* <code>LIBORVolatilityModelTwoParameterExponentialForm</code>, then
* the resulting LIBOR Market model corresponds to a Hull-White short rate model
* (with constant short rate volatility and mean reversion).
*
* The parameter of this model is the parameter vector of the given base covariance model.
*
* @author Christian Fries
*/
public class HullWhiteLocalVolatilityModel extends AbstractLIBORCovarianceModelParametric {
private final AbstractLIBORCovarianceModelParametric covarianceModel;
private final double periodLength;
/**
* The model constructed for the <i>i</i>-th factor loading is
* <center>
* <i>(1+L<sub>i</sub>(t) d) F<sub>i</sub>(t)</i>
* </center>
* where <i>d</i> is a constant (the period length), <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.
*
* The parameter of this model is the parameter vector of the given base covariance model.
*
* @param covarianceModel The given covariance model specifying the factor loadings <i>F</i>.
* @param periodLength The parameter <i>d</i> in the formula above.
*/
public HullWhiteLocalVolatilityModel(AbstractLIBORCovarianceModelParametric covarianceModel, double periodLength) {
super(covarianceModel.getTimeDiscretization(), covarianceModel.getLiborPeriodDiscretization(), covarianceModel.getNumberOfFactors());
this.covarianceModel = covarianceModel;
this.periodLength = periodLength;
}
@Override
public Object clone() {
return new HullWhiteLocalVolatilityModel((AbstractLIBORCovarianceModelParametric) covarianceModel.clone(), periodLength);
}
/**
* Returns the base covariance model, i.e., the model providing the factor loading <i>F</i>.
*
* @return The base covariance model.
*/
public AbstractLIBORCovarianceModelParametric getBaseCovarianceModel() {
return covarianceModel;
}
@Override
public double[] getParameter() {
return covarianceModel.getParameter();
}
@Override
public AbstractLIBORCovarianceModelParametric getCloneWithModifiedParameters(double[] parameters) {
return new HullWhiteLocalVolatilityModel((AbstractLIBORCovarianceModelParametric) covarianceModel.getCloneWithModifiedParameters(parameters), periodLength);
}
@Override
public RandomVariableInterface[] getFactorLoading(int timeIndex, int component, RandomVariableInterface[] realizationAtTimeIndex) {
RandomVariableInterface[] factorLoading = covarianceModel.getFactorLoading(timeIndex, component, realizationAtTimeIndex);
if(realizationAtTimeIndex != null && realizationAtTimeIndex[component] != null) {
RandomVariableInterface localVolatilityFactor = realizationAtTimeIndex[component].mult(periodLength).add(1.0);
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();
}
}