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finmath lib is a Mathematical Finance Library in Java.
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/*
* (c) Copyright Christian P. Fries, Germany. Contact: [email protected].
*
* Created on 09.02.2004
*/
package net.finmath.montecarlo.interestrate.models;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
import net.finmath.exception.CalculationException;
import net.finmath.marketdata.model.AnalyticModel;
import net.finmath.marketdata.model.curves.DiscountCurve;
import net.finmath.marketdata.model.curves.DiscountCurveFromForwardCurve;
import net.finmath.marketdata.model.curves.ForwardCurve;
import net.finmath.montecarlo.RandomVariableFactory;
import net.finmath.montecarlo.RandomVariableFromArrayFactory;
import net.finmath.montecarlo.RandomVariableFromDoubleArray;
import net.finmath.montecarlo.interestrate.LIBORMarketModel;
import net.finmath.montecarlo.interestrate.LIBORModel;
import net.finmath.montecarlo.model.AbstractProcessModel;
import net.finmath.montecarlo.process.MonteCarloProcess;
import net.finmath.stochastic.RandomVariable;
import net.finmath.time.TimeDiscretization;
/**
* Implements a Hull-White model with constant coefficients.
*
*
* A more general implementation of the Hull-White model can be found in {@link net.finmath.montecarlo.interestrate.models.HullWhiteModel}.
* For details and documentation please see {@link net.finmath.montecarlo.interestrate.models.HullWhiteModel} for real applications.
*
*
* @author Christian Fries
* @version 1.0
*/
public class HullWhiteModelWithConstantCoeff extends AbstractProcessModel implements LIBORModel {
private final TimeDiscretization liborPeriodDiscretization;
private String forwardCurveName;
private final AnalyticModel analyticModel;
private final ForwardCurve forwardRateCurve;
private final DiscountCurve discountCurve;
private final DiscountCurve discountCurveFromForwardCurve;
private final RandomVariableFactory randomVariableFactory = new RandomVariableFromArrayFactory();
private final double meanReversion;
private final double volatility;
// Cache for the numeraires, needs to be invalidated if process changes
private final ConcurrentHashMap numeraires;
private MonteCarloProcess numerairesProcess = null;
// Initialized lazily using process time discretization
private RandomVariable[] initialState;
/**
* Creates a Hull-White model which implements LIBORMarketModel.
*
* @param liborPeriodDiscretization The forward rate discretization to be used in the getLIBOR method.
* @param analyticModel The analytic model to be used (currently not used, may be null).
* @param forwardRateCurve The forward curve to be used (currently not used, - the model uses disocuntCurve only.
* @param discountCurve The disocuntCurve (currently also used to determine the forward curve).
* @param meanReversion The mean reversion speed parameter a.
* @param volatility The short rate volatility \( \sigma \).
* @param properties A map specifying model properties (currently not used, may be null).
*/
public HullWhiteModelWithConstantCoeff(
final TimeDiscretization liborPeriodDiscretization,
final AnalyticModel analyticModel,
final ForwardCurve forwardRateCurve,
final DiscountCurve discountCurve,
final double meanReversion,
final double volatility,
final Map properties
) {
this.liborPeriodDiscretization = liborPeriodDiscretization;
this.analyticModel = analyticModel;
this.forwardRateCurve = forwardRateCurve;
this.discountCurve = discountCurve;
this.meanReversion = meanReversion;
this.volatility = volatility;
discountCurveFromForwardCurve = new DiscountCurveFromForwardCurve(forwardRateCurve);
numeraires = new ConcurrentHashMap<>();
}
@Override
public int getNumberOfComponents() {
return 1;
}
@Override
public int getNumberOfFactors()
{
return 1;
}
@Override
public RandomVariable applyStateSpaceTransform(final MonteCarloProcess process, final int timeIndex, final int componentIndex, final RandomVariable randomVariable) {
return randomVariable;
}
@Override
public RandomVariable applyStateSpaceTransformInverse(final MonteCarloProcess process, final int timeIndex, final int componentIndex, final RandomVariable randomVariable) {
return randomVariable;
}
@Override
public RandomVariable[] getInitialState(MonteCarloProcess process) {
if(initialState == null) {
final double dt = process.getTimeDiscretization().getTimeStep(0);
//liborPeriodDiscretization.getTimeStep(0);
initialState = new RandomVariable[] { new RandomVariableFromDoubleArray(Math.log(discountCurveFromForwardCurve.getDiscountFactor(0.0)/discountCurveFromForwardCurve.getDiscountFactor(dt))/dt) };
}
return initialState;
}
@Override
public RandomVariable getNumeraire(final MonteCarloProcess process, final double time) throws CalculationException {
if(time < 0) {
return randomVariableFactory.createRandomVariable(discountCurve.getDiscountFactor(analyticModel, time));
}
if(time == process.getTime(0)) {
// Initial value of numeraire is one - BrownianMotion serves as a factory here.
final RandomVariable one = randomVariableFactory.createRandomVariable(1.0);
return one;
}
final int timeIndex = process.getTimeIndex(time);
if(timeIndex < 0) {
/*
* time is not part of the time discretization.
*/
// Find the time index prior to the current time (note: if time does not match a discretization point, we get a negative value, such that -index is next point).
int previousTimeIndex = process.getTimeIndex(time);
if(previousTimeIndex < 0) {
previousTimeIndex = -previousTimeIndex-1;
}
previousTimeIndex--;
final double previousTime = process.getTime(previousTimeIndex);
// Get value of short rate for period from previousTime to time.
final RandomVariable value = getShortRate(process, previousTimeIndex);
// Piecewise constant rate for the increment
final RandomVariable integratedRate = value.mult(time-previousTime);
return getNumeraire(process, previousTime).mult(integratedRate.exp());
}
/*
* Check if numeraire cache is values (i.e. process did not change)
*/
if(process != numerairesProcess) {
numeraires.clear();
numerairesProcess = process;
}
/*
* Check if numeraire is part of the cache
*/
RandomVariable numeraire = numeraires.get(timeIndex);
if(numeraire == null) {
/*
* Calculate the numeraire for timeIndex
*/
final RandomVariable zero = process.getStochasticDriver().getRandomVariableForConstant(0.0);
RandomVariable integratedRate = zero;
// Add r(t_{i}) (t_{i+1}-t_{i}) for i = 0 to previousTimeIndex-1
for(int i=0; i 0 ? - Math.log(df1/df0) / (t1-t0) : getInitialState(process)[0].get(0);
final double forwardNext = - Math.log(df2/df1) / (t2-t1);
final double forwardChange = (forwardNext-forward) / ((t1-t0));
final double meanReversionEffective = meanReversion*getB(time,timeNext)/(timeNext-time);
final double shortRateVariance = getShortRateConditionalVariance(0, time);
/*
* The +meanReversionEffective * forwardPrev removes the previous forward from the mean-reversion part.
* The +forwardChange updates the forward to the next period.
*/
final double theta = forwardChange + meanReversionEffective * forward + shortRateVariance*getB(time,t1)/(t1-time);
return new RandomVariable[] { realizationAtTimeIndex[0].mult(-meanReversionEffective).add(theta) };
}
@Override
public RandomVariable[] getFactorLoading(final MonteCarloProcess process, final int timeIndex, final int componentIndex, final RandomVariable[] realizationAtTimeIndex) {
final double time = process.getTime(timeIndex);
final double timeNext = process.getTime(timeIndex+1);
final double scaling = Math.sqrt((1.0-Math.exp(-2.0 * meanReversion * (timeNext-time)))/(2.0 * meanReversion * (timeNext-time)));
final double volatilityEffective = scaling*volatility;
return new RandomVariable[] { new RandomVariableFromDoubleArray(volatilityEffective) };
}
@Override
public RandomVariable getRandomVariableForConstant(final double value) {
return randomVariableFactory.createRandomVariable(value);
}
@Override
public RandomVariable getForwardRate(final MonteCarloProcess process, final double time, final double periodStart, final double periodEnd) throws CalculationException
{
return getZeroCouponBond(process, time, periodStart).div(getZeroCouponBond(process, time, periodEnd)).sub(1.0).div(periodEnd-periodStart);
}
@Override
public RandomVariable getLIBOR(final MonteCarloProcess process, final int timeIndex, final int liborIndex) throws CalculationException {
return getZeroCouponBond(process, process.getTime(timeIndex), getLiborPeriod(liborIndex)).div(getZeroCouponBond(process, process.getTime(timeIndex), getLiborPeriod(liborIndex+1))).sub(1.0).div(getLiborPeriodDiscretization().getTimeStep(liborIndex));
}
@Override
public TimeDiscretization getLiborPeriodDiscretization() {
return liborPeriodDiscretization;
}
@Override
public int getNumberOfLibors() {
return liborPeriodDiscretization.getNumberOfTimeSteps();
}
@Override
public double getLiborPeriod(final int timeIndex) {
return liborPeriodDiscretization.getTime(timeIndex);
}
@Override
public int getLiborPeriodIndex(final double time) {
return liborPeriodDiscretization.getTimeIndex(time);
}
@Override
public AnalyticModel getAnalyticModel() {
return analyticModel;
}
@Override
public DiscountCurve getDiscountCurve() {
return discountCurve;
}
@Override
public ForwardCurve getForwardRateCurve() {
return forwardRateCurve;
}
@Override
public LIBORMarketModel getCloneWithModifiedData(final Map dataModified) {
throw new UnsupportedOperationException();
}
private RandomVariable getShortRate(final MonteCarloProcess process, final int timeIndex) throws CalculationException {
final RandomVariable value = process.getProcessValue(timeIndex, 0);
return value;
}
private RandomVariable getZeroCouponBond(final MonteCarloProcess process, final double time, final double maturity) throws CalculationException {
final RandomVariable shortRate = getShortRate(process, process.getTimeIndex(time));
return shortRate.mult(-getB(time,maturity)).exp().mult(getA(process, time, maturity));
}
/**
* Returns A(t,T) where
* \( A(t,T) = P(T)/P(t) \cdot exp(B(t,T) \cdot f(0,t) - \frac{1}{2} \phi(0,t) * B(t,T)^{2} ) \)
* and
* \( \phi(t,T) \) is the value calculated from integrating \( ( \sigma(s) B(s,T) )^{2} \) with respect to s from t to T
* in getShortRateConditionalVariance.
*
* @param time The parameter t.
* @param maturity The parameter T.
* @return The value A(t,T).
*/
private double getA(final MonteCarloProcess process, final double time, final double maturity) {
final int timeIndex = process.getTimeIndex(time);
final double timeStep = process.getTimeDiscretization().getTimeStep(timeIndex);
final double dt = timeStep;
final double zeroRate = -Math.log(discountCurveFromForwardCurve.getDiscountFactor(time+dt)/discountCurveFromForwardCurve.getDiscountFactor(time)) / dt;
final double B = getB(time,maturity);
final double lnA = Math.log(discountCurveFromForwardCurve.getDiscountFactor(maturity)/discountCurveFromForwardCurve.getDiscountFactor(time))
+ B * zeroRate - 0.5 * getShortRateConditionalVariance(0,time) * B * B;
return Math.exp(lnA);
}
/**
* Calculates \( B(t,T) = \int_{t}^{T} \exp(-\int_{s}^{T} a(\tau) \mathrm{d}\tau) \mathrm{d}s \), where a is the mean reversion parameter.
* For a constant \( a \) this results in \( \frac{1-\exp(-a (T-t)}{a} \), but the method also supports piecewise constant \( a \)'s.
*
* @param time The parameter t.
* @param maturity The parameter T.
* @return The value of B(t,T).
*/
private double getB(final double time, final double maturity) {
return (1-Math.exp(-meanReversion * (maturity-time)))/meanReversion;
}
/**
* Calculates the variance \( \mathop{Var}(r(t) \vert r(s) ) \), that is
* \(
* \int_{s}^{t} \sigma^{2}(\tau) \exp(-2 \cdot a \cdot (t-\tau)) \ \mathrm{d}\tau
* \) where \( a \) is the meanReversion and \( \sigma \) is the short rate instantaneous volatility.
*
* @param time The parameter s in \( \int_{s}^{t} \sigma^{2}(\tau) \exp(-2 \cdot a \cdot (t-\tau)) \ \mathrm{d}\tau \)
* @param maturity The parameter t in \( \int_{s}^{t} \sigma^{2}(\tau) \exp(-2 \cdot a \cdot (t-\tau)) \ \mathrm{d}\tau \)
* @return The integrated square volatility.
*/
public double getShortRateConditionalVariance(final double time, final double maturity) {
return volatility*volatility * (1 - Math.exp(-2*meanReversion*(maturity-time))) / (2*meanReversion);
}
public double getIntegratedBondSquaredVolatility(final double time, final double maturity) {
return getShortRateConditionalVariance(0, time) * getB(time,maturity) * getB(time,maturity);
}
@Override
public Map getModelParameters() {
// TODO Add implementation
throw new UnsupportedOperationException();
}
}