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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.io.IOException;
import java.io.ObjectInputStream;
import java.io.Serializable;
import java.time.LocalDateTime;
import java.time.LocalTime;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.TreeMap;
import java.util.logging.Logger;
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.interestrate.CalibrationProduct;
import net.finmath.montecarlo.interestrate.LIBORModel;
import net.finmath.montecarlo.interestrate.ShortRateModel;
import net.finmath.montecarlo.interestrate.models.covariance.ShortRateVolatilityModel;
import net.finmath.montecarlo.interestrate.models.covariance.ShortRateVolatilityModelCalibrateable;
import net.finmath.montecarlo.interestrate.models.covariance.ShortRateVolatilityModelParametric;
import net.finmath.montecarlo.model.AbstractProcessModel;
import net.finmath.montecarlo.process.MonteCarloProcess;
import net.finmath.stochastic.RandomVariable;
import net.finmath.stochastic.Scalar;
import net.finmath.time.TimeDiscretization;
/**
* Implements a Hull-White model with time dependent mean reversion speed and time dependent short rate volatility.
*
*
* Model Dynamics
*
*
* The Hull-While model assumes the following dynamic for the short rate:
* \[ d r(t) = ( \theta(t) - a(t) r(t) ) d t + \sigma(t) d W(t) \text{,} \quad r(t_{0}) = r_{0} \text{,} \]
* where the function \( \theta \) determines the calibration to the initial forward curve,
* \( a \) is the mean reversion and \( \sigma \) is the instantaneous volatility.
*
* The dynamic above is under the equivalent martingale measure corresponding to the numeraire
* \[ N(t) = \exp\left( \int_0^t r(\tau) \mathrm{d}\tau \right) \text{.} \]
*
* The main task of this class is to provide the risk-neutral drift and the volatility to the numerical scheme (given the volatility model), simulating
* \( r(t_{i}) \). The class then also provides and the corresponding numeraire and forward rates (LIBORs).
*
*
* Time Discrete Model
*
*
* Assuming piecewise constant coefficients (mean reversion speed \( a \) and short
* rate volatility \( \sigma \) the class specifies the drift and factor loadings as
* piecewise constant functions for an Euler-scheme.
* The class provides the exact Euler step for the joint distribution of
* \( (r,N) \), where \( r \) denotes the short rate and \( N \) denotes the
* numeraire, following the scheme in ssrn.com/abstract=2737091.
*
* More specifically (assuming a constant mean reversion speed \( a \) for a moment), considering
* \[ \Delta \bar{r}(t_{i}) = \frac{1}{t_{i+1}-t_{i}} \int_{t_{i}}^{t_{i+1}} d r(t) \]
* we find from
* \[ \exp(-a t) \ \left( \mathrm{d} \left( \exp(a t) r(t) \right) \right) \ = \ a r(t) + \mathrm{d} r(t) \ = \ \theta(t) \mathrm{d}t + \sigma(t) \mathrm{d}W(t) \]
* that
* \[ \exp(a t_{i+1}) r(t_{i+1}) - \exp(a t_{i}) r(t_{i}) \ = \ \int_{t_{i}}^{t_{i+1}} \left[ \exp(a t) \theta(t) \mathrm{d}t + \exp(a t) \sigma(t) \mathrm{d}W(t) \right] \]
* that is
* \[ r(t_{i+1}) - r(t_{i}) \ = \ -(1-\exp(-a (t_{i+1}-t_{i})) r(t_{i}) + \int_{t_{i}}^{t_{i+1}} \left[ \exp(-a (t_{i+1}-t)) \theta(t) \mathrm{d}t + \exp(-a (t_{i+1}-t)) \sigma(t) \mathrm{d}W(t) \right] \]
*
* Assuming piecewise constant \( \sigma \) and \( \theta \), being constant over \( (t_{i},t_{i}+\Delta t_{i}) \), we thus find
* \[ r(t_{i+1}) - r(t_{i}) \ = \ \frac{1-\exp(-a \Delta t_{i})}{a \Delta t_{i}} \left( ( \theta(t_{i}) - a \bar{r}(t_{i})) \Delta t_{i} \right) + \sqrt{\frac{1-\exp(-2 a \Delta t_{i})}{2 a \Delta t_{i}}} \sigma(t_{i}) \Delta W(t_{i}) \] .
*
* In other words, the Euler scheme is exact if the mean reversion \( a \) is replaced by the effective mean reversion
* \( \frac{1-\exp(-a \Delta t_{i})}{a \Delta t_{i}} a \) and the volatility is replaced by the
* effective volatility \( \sqrt{\frac{1-\exp(-2 a \Delta t_{i})}{2 a \Delta t_{i}}} \sigma(t_{i}) \).
*
* In the calculations above the mean reversion speed is treated as a constants, but it is straight
* forward to see that the same holds for piecewise constant mean reversion speeds, replacing
* the expression \( a \ t \) by \( \int_{0}^t a(s) \mathrm{d}s \).
*
*
* Calibration
*
*
* The drift of the short rate is calibrated to the given forward curve using
* \[ \theta(t) = \frac{\partial}{\partial T} f(0,t) + a(t) f(0,t) + \phi(t) \text{,} \]
* where the function \( f \) denotes the instantanenous forward rate and
* \( \phi(t) = \frac{1}{2} a \sigma^{2}(t) B(t)^{2} + \sigma^{2}(t) B(t) \frac{\partial}{\partial t} B(t) \) with \( B(t) = \frac{1-\exp(-a t)}{a} \).
*
*
* Volatility Model
*
*
* The Hull-White model is essentially equivalent to LIBOR Market Model where the forward rate normal volatility \( \sigma(t,T) \) is
* given by
* \[ \sigma(t,T_{i}) \ = \ (1 + L_{i}(t) (T_{i+1}-T_{i})) \sigma(t) \exp(-a (T_{i}-t)) \frac{1-\exp(-a (T_{i+1}-T_{i}))}{a (T_{i+1}-T_{i})} \]
* (where \( \{ T_{i} \} \) is the forward rates tenor time discretization (note that this is the normal volatility, not the log-normal volatility)
* (see ssrn.com/abstract=2737091 for details on the derivation).
* Hence, we interpret both, short rate mean reversion speed and short rate volatility as part of the volatility model.
*
* The mean reversion speed and the short rate volatility have to be provided to this class via an object implementing
* {@link net.finmath.montecarlo.interestrate.models.covariance.ShortRateVolatilityModel}.
*
* This implementation supports different method for the interpolation of the curves.
* The property "isInterpolateDiscountFactorsOnLiborPeriodDiscretization" is a boolean. If true, the
* given curves are used only at the discretization points given by liborPeriodDiscretization.
* This implies that the model reports only a limited set of risk factors in the methods {@link HullWhiteModel#getModelParameters()}.
*
* @see net.finmath.montecarlo.interestrate.models.covariance.ShortRateVolatilityModel
* @see ssrn.com/abstract=2737091
*
* @author Christian Fries
* @version 1.4
*/
public class HullWhiteModel extends AbstractProcessModel implements ShortRateModel, LIBORModel, Serializable {
private static final long serialVersionUID = 8677410149401310062L;
private static final Logger logger = Logger.getLogger("net.finmath");
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;
private final ShortRateVolatilityModel volatilityModel;
private final Map properties;
private final boolean isInterpolateDiscountFactorsOnLiborPeriodDiscretization;
/*
* Cache
*/
private transient List numeraireDiscountFactors = new ArrayList<>();
private transient List numeraireDiscountFactorForwardRates = new ArrayList<>();
private transient List discountFactorFromForwardCurveCache = new ArrayList<>();
private transient List forwardRateCache = new ArrayList<>();
/**
* Creates a Hull-White model which implements LIBORMarketModel.
*
* @param randomVariableFactory The factory to be used to construct random variables.
* @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 volatilityModel The volatility model specifying mean reversion and instantaneous volatility of the short rate.
* @param properties A map specifying model properties.
*/
public HullWhiteModel(
final RandomVariableFactory randomVariableFactory,
final TimeDiscretization liborPeriodDiscretization,
final AnalyticModel analyticModel,
final ForwardCurve forwardRateCurve,
final DiscountCurve discountCurve,
final ShortRateVolatilityModel volatilityModel,
final Map properties
) {
this.randomVariableFactory = randomVariableFactory;
this.liborPeriodDiscretization = liborPeriodDiscretization;
this.analyticModel = analyticModel;
this.forwardRateCurve = forwardRateCurve;
this.discountCurve = discountCurve;
this.volatilityModel = volatilityModel;
this.properties = new HashMap<>();
if(properties != null) {
for(final Map.Entry property : properties.entrySet()) {
if(Serializable.class.isAssignableFrom(property.getValue().getClass())) {
properties.put(property.getKey(), property.getValue());
}
else {
logger.warning("Ignored non serializable property under the key " + property.getKey() + ":" + property.getValue());
}
}
}
isInterpolateDiscountFactorsOnLiborPeriodDiscretization = (Boolean) this.properties.getOrDefault("isInterpolateDiscountFactorsOnLiborPeriodDiscretization", Boolean.valueOf(true));
discountCurveFromForwardCurve = new DiscountCurveFromForwardCurve(forwardRateCurve);
}
/**
* 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 volatilityModel The volatility model specifying mean reversion and instantaneous volatility of the short rate.
* @param properties A map specifying model properties (currently not used, may be null).
*/
public HullWhiteModel(
final TimeDiscretization liborPeriodDiscretization,
final AnalyticModel analyticModel,
final ForwardCurve forwardRateCurve,
final DiscountCurve discountCurve,
final ShortRateVolatilityModel volatilityModel,
final Map properties
) {
this(new RandomVariableFromArrayFactory(), liborPeriodDiscretization, analyticModel, forwardRateCurve, discountCurve, volatilityModel, properties);
}
/**
* Creates a Hull-White model which implements LIBORMarketModel.
*
* @param randomVariableFactory The randomVariableFactory
* @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 volatilityModel The volatility model specifying mean reversion and instantaneous volatility of the short rate.
* @param calibrationProducts The products to be used for calibration
* @param properties The calibration properties
* @return A (possibly calibrated) Hull White model.
* @throws CalculationException Thrown if calibration fails.
*/
public static HullWhiteModel of(
final RandomVariableFactory randomVariableFactory,
final TimeDiscretization liborPeriodDiscretization,
final AnalyticModel analyticModel,
final ForwardCurve forwardRateCurve,
final DiscountCurve discountCurve,
final ShortRateVolatilityModel volatilityModel,
final CalibrationProduct[] calibrationProducts,
final Map properties
) throws CalculationException {
final HullWhiteModel model = new HullWhiteModel(randomVariableFactory, liborPeriodDiscretization, analyticModel, forwardRateCurve, discountCurve, volatilityModel, properties);
// Perform calibration, if data is given
if(calibrationProducts != null && calibrationProducts.length > 0) {
ShortRateVolatilityModelCalibrateable volatilityModelParametric = null;
try {
volatilityModelParametric = (ShortRateVolatilityModelCalibrateable)volatilityModel;
}
catch(final Exception e) {
throw new ClassCastException("Calibration restricted to covariance models implementing HullWhiteModelCalibrateable.");
}
Map calibrationParameters = null;
if(properties != null && properties.containsKey("calibrationParameters")) {
calibrationParameters = (Map)properties.get("calibrationParameters");
}
final ShortRateVolatilityModelCalibrateable volatilityModelCalibrated = volatilityModelParametric.getCloneCalibrated(model, calibrationProducts, calibrationParameters);
final HullWhiteModel modelCalibrated = model.getCloneWithModifiedVolatilityModel(volatilityModelCalibrated);
return modelCalibrated;
}
else {
return model;
}
}
@Override
public LocalDateTime getReferenceDate() {
return LocalDateTime.of(discountCurve.getReferenceDate(), LocalTime.of(0, 0));
}
@Override
public int getNumberOfComponents() {
return 2;
}
@Override
public int getNumberOfFactors()
{
return 1;
}
@Override
public RandomVariable applyStateSpaceTransform(MonteCarloProcess process, int timeIndex, final int componentIndex, final RandomVariable randomVariable) {
return randomVariable;
}
@Override
public RandomVariable applyStateSpaceTransformInverse(MonteCarloProcess process, int timeIndex, final int componentIndex, final RandomVariable randomVariable) {
return randomVariable;
}
@Override
public RandomVariable[] getInitialState(MonteCarloProcess process) {
// Initial value is zero - BrownianMotion serves as a factory here.
final RandomVariable zero = getRandomVariableForConstant(0.0);
return new RandomVariable[] { zero, zero };
}
@Override
public RandomVariable getNumeraire(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);
final double nextTime = process.getTime(previousTimeIndex+1);
// Log-linear interpolation
return getNumeraire(process, previousTime).log().mult(nextTime-time)
.add(getNumeraire(process, nextTime).log().mult(time-previousTime))
.div(nextTime-previousTime).exp();
}
final RandomVariable logNum = process.getProcessValue(timeIndex, 1).add(getV(0,time).mult(0.5));
RandomVariable numeraireNormalized = logNum.exp();
// Control variate on zero bond
numeraireNormalized = numeraireNormalized.mult(numeraireNormalized.invert().getAverage());
// Apply discount factor scaling
RandomVariable discountFactor;
if(discountCurve != null) {
discountFactor = getDiscountFactor(process, time).div(getDiscountFactorFromForwardCurve(process, time).getAverage()).mult(getDiscountFactorFromForwardCurve(process, time));
// discountFactor = getDiscountFactor(time);
}
else {
discountFactor = getDiscountFactorFromForwardCurve(process, time);
}
final RandomVariable numeraire = numeraireNormalized.div(discountFactor);
return numeraire;
}
@Override
public RandomVariable getForwardDiscountBond(final MonteCarloProcess process, final double time, final double maturity) throws CalculationException {
final RandomVariable inverseForwardBondAsOfTime = getForwardRate(process, time, time, maturity).mult(maturity-time).add(1.0);
final RandomVariable inverseForwardBondAsOfZero = getForwardRate(process, 0.0, time, maturity).mult(maturity-time).add(1.0);
final RandomVariable forwardDiscountBondAsOfZero = getDiscountFactor(process, maturity).div(getDiscountFactor(process, time));
return forwardDiscountBondAsOfZero.mult(inverseForwardBondAsOfZero).div(inverseForwardBondAsOfTime);
}
@Override
public RandomVariable[] getDrift(final MonteCarloProcess process, final int timeIndex, final RandomVariable[] realizationAtTimeIndex, final RandomVariable[] realizationPredictor) {
final double time = process.getTime(timeIndex);
final double timeNext = process.getTime(timeIndex+1);
if(timeNext == time) {
return new RandomVariable[] { null, null };
}
int timeIndexVolatility = volatilityModel.getTimeDiscretization().getTimeIndex(time);
if(timeIndexVolatility < 0) {
timeIndexVolatility = -timeIndexVolatility-2;
}
final RandomVariable meanReversion = volatilityModel.getMeanReversion(timeIndexVolatility);
final RandomVariable driftShortRate = realizationAtTimeIndex[0].mult(meanReversion.mult(getB(time,timeNext).div(-1*(timeNext-time))));
final RandomVariable driftLogNumeraire = realizationAtTimeIndex[0].mult(getB(time,timeNext).div(timeNext-time));
return new RandomVariable[] { driftShortRate, driftLogNumeraire };
}
@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);
int timeIndexVolatility = volatilityModel.getTimeDiscretization().getTimeIndex(time);
if(timeIndexVolatility < 0) {
timeIndexVolatility = -timeIndexVolatility-2;
}
final RandomVariable meanReversion = volatilityModel.getMeanReversion(timeIndexVolatility);
final RandomVariable meanReversionTimesTime = meanReversion.mult(-2.0 * (timeNext-time));
// double scaling = Math.sqrt((1.0-Math.exp(-2.0 * meanReversion * (timeNext-time)))/(2.0 * meanReversion * (timeNext-time)));
final RandomVariable scaling = meanReversionTimesTime.exp().sub(1.0).div(meanReversionTimesTime).sqrt();
final RandomVariable volatilityEffective = scaling.mult(volatilityModel.getVolatility(timeIndexVolatility));
RandomVariable factorLoading1, factorLoading2;
if(componentIndex == 0) {
// Factor loadings for the short rate driver.
factorLoading1 = volatilityEffective;
factorLoading2 = new Scalar(0.0);
}
else if(componentIndex == 1) {
// Factor loadings for the numeraire driver.
final RandomVariable volatilityLogNumeraire = getV(time,timeNext).div(timeNext-time).sqrt();
final RandomVariable rho = getDV(time,timeNext).div(timeNext-time).div(volatilityEffective.mult(volatilityLogNumeraire));
factorLoading1 = volatilityLogNumeraire.mult(rho);
factorLoading2 = volatilityLogNumeraire.mult(rho.squared().sub(1).mult(-1).sqrt());
}
else {
throw new IllegalArgumentException();
}
return new RandomVariable[] { factorLoading1, factorLoading2 };
}
@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 LIBORModel getCloneWithModifiedData(final Map dataModified) {
if(dataModified == null) {
return new HullWhiteModel(randomVariableFactory, liborPeriodDiscretization, analyticModel, forwardRateCurve, discountCurve, volatilityModel, properties);
}
final RandomVariableFactory newRandomVariableFactory = (RandomVariableFactory) dataModified.getOrDefault("randomVariableFactory", randomVariableFactory);
final ShortRateVolatilityModel newVolatilityModel = (ShortRateVolatilityModel) dataModified.getOrDefault("volatilityModel", volatilityModel);
return new HullWhiteModel(newRandomVariableFactory, liborPeriodDiscretization, analyticModel, forwardRateCurve, discountCurve, newVolatilityModel, properties);
}
/**
* Returns the "short rate" from timeIndex to timeIndex+1.
*
* @param timeIndex The time index (corresponding to {@link MonteCarloProcess#getTime(int)}).
* @return The "short rate" from timeIndex to timeIndex+1.
* @throws CalculationException Thrown if simulation failed.
*/
private RandomVariable getShortRate(final MonteCarloProcess process, final int timeIndex) throws CalculationException {
final double time = process.getTime(timeIndex);
final double timePrev = timeIndex > 0 ? process.getTime(timeIndex-1) : time;
final double timeNext = process.getTime(timeIndex+1);
final RandomVariable zeroRate = getZeroRateFromForwardCurve(process, time); //getDiscountFactorFromForwardCurve(time).div(getDiscountFactorFromForwardCurve(timeNext)).log().div(timeNext-time);
final RandomVariable alpha = getDV(0, time);
RandomVariable value = process.getProcessValue(timeIndex, 0);
value = value.add(alpha);
// value = value.sub(Math.log(value.exp().getAverage()));
value = value.add(zeroRate);
return value;
}
private RandomVariable getZeroCouponBond(final MonteCarloProcess process, final double time, final double maturity) throws CalculationException {
final int timeIndex = process.getTimeIndex(time);
if(timeIndex < 0) {
final int timeIndexLo = -timeIndex-1-1;
final double timeLo = process.getTime(timeIndexLo);
return getZeroCouponBond(process, timeLo, maturity).div(getZeroCouponBond(process, timeLo, time));
}
final RandomVariable shortRate = getShortRate(process, timeIndex);
final RandomVariable A = getA(process, time, maturity);
final RandomVariable B = getB(time, maturity);
return shortRate.mult(B.mult(-1)).exp().mult(A);
}
/**
* This is the shift alpha of the process, which essentially represents
* the integrated drift of the short rate (without the interest rate curve related part).
*
* @param timeIndex Time index associated with the time discretization obtained from getProcess
* @return The integrated drift (integrating from 0 to getTime(timeIndex)).
*/
private RandomVariable getIntegratedDriftAdjustment(final MonteCarloProcess process, final int timeIndex) {
RandomVariable integratedDriftAdjustment = new Scalar(0.0);
for(int i=1; i<=timeIndex; i++) {
final double t = process.getTime(i-1);
final double t2 = process.getTime(i);
int timeIndexVolatilityModel = volatilityModel.getTimeDiscretization().getTimeIndex(t);
if(timeIndexVolatilityModel < 0) {
timeIndexVolatilityModel = -timeIndexVolatilityModel-2; // Get timeIndex corresponding to previous point
}
final RandomVariable meanReversion = volatilityModel.getMeanReversion(timeIndexVolatilityModel);
integratedDriftAdjustment = integratedDriftAdjustment.add(getShortRateConditionalVariance(0, t).mult(getB(t,t2))).sub(integratedDriftAdjustment.mult(meanReversion.mult(getB(t,t2))));
}
return integratedDriftAdjustment;
}
/**
* 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 \( ( \sigma(s) exp(-\int_{s}^{T} a(\tau) \mathrm{d}\tau ) )^{2} \) 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 RandomVariable getA(final MonteCarloProcess process, final double time, final double maturity) {
final int timeIndex = process.getTimeIndex(time);
final double timeStep = process.getTimeDiscretization().getTimeStep(timeIndex);
final RandomVariable zeroRate = getZeroRateFromForwardCurve(process, time); //getDiscountFactorFromForwardCurve(time).div(getDiscountFactorFromForwardCurve(timeNext)).log().div(timeNext-time);
final RandomVariable forwardBond = getDiscountFactorFromForwardCurve(process, maturity).div(getDiscountFactorFromForwardCurve(process, time)).log();
final RandomVariable B = getB(time,maturity);
final RandomVariable lnA = B.mult(zeroRate).sub(B.squared().mult(getShortRateConditionalVariance(0,time).div(2))).add(forwardBond);
return lnA.exp();
}
/**
* Calculates \( \int_{t}^{T} a(s) \mathrm{d}s \), where \( a \) is the mean reversion parameter.
*
* @param time The parameter t.
* @param maturity The parameter T.
* @return The value of \( \int_{t}^{T} a(s) \mathrm{d}s \).
*/
private RandomVariable getMRTime(final double time, final double maturity) {
int timeIndexStart = volatilityModel.getTimeDiscretization().getTimeIndex(time);
if(timeIndexStart < 0) {
timeIndexStart = -timeIndexStart-2; // Get timeIndex corresponding to previous point
}
int timeIndexEnd =volatilityModel.getTimeDiscretization().getTimeIndex(maturity);
if(timeIndexEnd < 0) {
timeIndexEnd = -timeIndexEnd-2; // Get timeIndex corresponding to previous point
}
RandomVariable integral = new Scalar(0.0);
double timePrev = time;
double timeNext;
for(int timeIndex=timeIndexStart+1; timeIndex<=timeIndexEnd; timeIndex++) {
timeNext = volatilityModel.getTimeDiscretization().getTime(timeIndex);
final RandomVariable meanReversion = volatilityModel.getMeanReversion(timeIndex-1);
integral = integral.add(meanReversion.mult(timeNext-timePrev));
timePrev = timeNext;
}
timeNext = maturity;
final RandomVariable meanReversion = volatilityModel.getMeanReversion(timeIndexEnd);
integral = integral.add(meanReversion.mult(timeNext-timePrev));
return integral;
}
/**
* 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 RandomVariable getB(final double time, final double maturity) {
int timeIndexStart = volatilityModel.getTimeDiscretization().getTimeIndex(time);
if(timeIndexStart < 0) {
timeIndexStart = -timeIndexStart-2; // Get timeIndex corresponding to previous point
}
int timeIndexEnd =volatilityModel.getTimeDiscretization().getTimeIndex(maturity);
if(timeIndexEnd < 0) {
timeIndexEnd = -timeIndexEnd-2; // Get timeIndex corresponding to previous point
}
RandomVariable integral = new Scalar(0.0);
double timePrev = time;
double timeNext;
for(int timeIndex=timeIndexStart+1; timeIndex<=timeIndexEnd; timeIndex++) {
timeNext = volatilityModel.getTimeDiscretization().getTime(timeIndex);
final RandomVariable meanReversion = volatilityModel.getMeanReversion(timeIndex-1);
integral = integral.add(
getMRTime(timeNext,maturity).mult(-1.0).exp().sub(
getMRTime(timePrev,maturity).mult(-1.0).exp()).div(meanReversion));
timePrev = timeNext;
}
final RandomVariable meanReversion = volatilityModel.getMeanReversion(timeIndexEnd);
timeNext = maturity;
integral = integral.add(
getMRTime(timeNext,maturity).mult(-1.0).exp().sub(
getMRTime(timePrev,maturity).mult(-1.0).exp()).div(meanReversion));
return integral;
}
/**
* Calculates the drift adjustment for the log numeraire, that is
* \(
* \int_{t}^{T} \sigma^{2}(s) B(s,T)^{2} \mathrm{d}s
* \) where \( B(t,T) = \int_{t}^{T} \exp(-\int_{s}^{T} a(\tau) \mathrm{d}\tau) \mathrm{d}s \).
*
* @param time The parameter t in \( \int_{t}^{T} \sigma^{2}(s) B(s,T)^{2} \mathrm{d}s \)
* @param maturity The parameter T in \( \int_{t}^{T} \sigma^{2}(s) B(s,T)^{2} \mathrm{d}s \)
* @return The integral \( \int_{t}^{T} \sigma^{2}(s) B(s,T)^{2} \mathrm{d}s \).
*/
private RandomVariable getV(final double time, final double maturity) {
if(time == maturity) {
return new Scalar(0.0);
}
int timeIndexStart = volatilityModel.getTimeDiscretization().getTimeIndex(time);
if(timeIndexStart < 0) {
timeIndexStart = -timeIndexStart-2; // Get timeIndex corresponding to previous point
}
int timeIndexEnd =volatilityModel.getTimeDiscretization().getTimeIndex(maturity);
if(timeIndexEnd < 0) {
timeIndexEnd = -timeIndexEnd-2; // Get timeIndex corresponding to previous point
}
RandomVariable integral = new Scalar(0.0);
double timePrev = time;
double timeNext;
RandomVariable expMRTimePrev = getMRTime(timePrev,maturity).mult(-1).exp();
for(int timeIndex=timeIndexStart+1; timeIndex<=timeIndexEnd; timeIndex++) {
timeNext = volatilityModel.getTimeDiscretization().getTime(timeIndex);
final RandomVariable meanReversion = volatilityModel.getMeanReversion(timeIndex-1);
final RandomVariable volatility = volatilityModel.getVolatility(timeIndex-1);
final RandomVariable volatilityPerMeanReversionSquared = volatility.squared().div(meanReversion.squared());
final RandomVariable expMRTimeNext = getMRTime(timeNext,maturity).mult(-1).exp();
integral = integral.add(volatilityPerMeanReversionSquared.mult(
expMRTimeNext.sub(expMRTimePrev).mult(-2).div(meanReversion)
.add( expMRTimeNext.squared().sub(expMRTimePrev.squared()).div(meanReversion).div(2.0))
.add(timeNext-timePrev)
));
timePrev = timeNext;
expMRTimePrev = expMRTimeNext;
}
timeNext = maturity;
final RandomVariable meanReversion = volatilityModel.getMeanReversion(timeIndexEnd);
final RandomVariable volatility = volatilityModel.getVolatility(timeIndexEnd);
final RandomVariable volatilityPerMeanReversionSquared = volatility.squared().div(meanReversion.squared());
final RandomVariable expMRTimeNext = getMRTime(timeNext,maturity).mult(-1).exp();
integral = integral.add(volatilityPerMeanReversionSquared.mult(
expMRTimeNext.sub(expMRTimePrev).mult(-2).div(meanReversion)
.add( expMRTimeNext.squared().sub(expMRTimePrev.squared()).div(meanReversion).div(2.0))
.add(timeNext-timePrev)
));
return integral;
}
private RandomVariable getDV(final double time, final double maturity) {
if(time==maturity) {
return new Scalar(0.0);
}
int timeIndexStart = volatilityModel.getTimeDiscretization().getTimeIndex(time);
if(timeIndexStart < 0) {
timeIndexStart = -timeIndexStart-2; // Get timeIndex corresponding to next point
}
int timeIndexEnd =volatilityModel.getTimeDiscretization().getTimeIndex(maturity);
if(timeIndexEnd < 0) {
timeIndexEnd = -timeIndexEnd-2; // Get timeIndex corresponding to previous point
}
RandomVariable integral = new Scalar(0.0);
double timePrev = time;
double timeNext;
RandomVariable expMRTimePrev = getMRTime(timePrev,maturity).mult(-1).exp();
for(int timeIndex=timeIndexStart+1; timeIndex<=timeIndexEnd; timeIndex++) {
timeNext = volatilityModel.getTimeDiscretization().getTime(timeIndex);
final RandomVariable meanReversion = volatilityModel.getMeanReversion(timeIndex-1);
final RandomVariable volatility = volatilityModel.getVolatility(timeIndex-1);
final RandomVariable volatilityPerMeanReversionSquared = volatility.squared().div(meanReversion.squared());
final RandomVariable expMRTimeNext = getMRTime(timeNext,maturity).mult(-1).exp();
integral = integral.add(volatilityPerMeanReversionSquared.mult(
expMRTimeNext.sub(expMRTimePrev).add(
expMRTimeNext.squared().sub(expMRTimePrev.squared()).div(-2.0)
) ));
timePrev = timeNext;
expMRTimePrev = expMRTimeNext;
}
timeNext = maturity;
final RandomVariable meanReversion = volatilityModel.getMeanReversion(timeIndexEnd);
final RandomVariable volatility = volatilityModel.getVolatility(timeIndexEnd);
final RandomVariable volatilityPerMeanReversionSquared = volatility.squared().div(meanReversion.squared());
final RandomVariable expMRTimeNext = getMRTime(timeNext,maturity).mult(-1).exp();
integral = integral.add(volatilityPerMeanReversionSquared.mult(
expMRTimeNext.sub(expMRTimePrev).add(
expMRTimeNext.squared().sub(expMRTimePrev.squared()).div(-2.0)
) ));
return integral;
}
/**
* Calculates the variance \( \mathop{Var}(r(t) \vert r(s) ) \), that is
* \(
* \int_{s}^{t} \sigma^{2}(\tau) \exp(-2 \cdot \int_{\tau}^{t} a(u) \mathrm{d}u ) \ \mathrm{d}\tau
* \) where \( a \) is the meanReversion and \( \sigma \) is the short rate instantaneous volatility.
*
* @param time The parameter s in \( \int_{s}^{t} \sigma^{2}(\tau) \exp(-2 \cdot \int_{\tau}^{t} a(u) \mathrm{d}u ) \ \mathrm{d}\tau \)
* @param maturity The parameter t in \( \int_{s}^{t} \sigma^{2}(\tau) \exp(-2 \cdot \int_{\tau}^{t} a(u) \mathrm{d}u ) \ \mathrm{d}\tau \)
* @return The conditional variance of the short rate, \( \mathop{Var}(r(t) \vert r(s) ) \).
*/
public RandomVariable getShortRateConditionalVariance(final double time, final double maturity) {
int timeIndexStart = volatilityModel.getTimeDiscretization().getTimeIndex(time);
if(timeIndexStart < 0) {
timeIndexStart = -timeIndexStart-2; // Get timeIndex corresponding to next point
}
int timeIndexEnd =volatilityModel.getTimeDiscretization().getTimeIndex(maturity);
if(timeIndexEnd < 0) {
timeIndexEnd = -timeIndexEnd-2; // Get timeIndex corresponding to previous point
}
RandomVariable integral = new Scalar(0.0);
double timePrev = time;
double timeNext;
RandomVariable expMRTimePrev = getMRTime(timePrev,maturity).mult(-2).exp();
for(int timeIndex=timeIndexStart+1; timeIndex<=timeIndexEnd; timeIndex++) {
timeNext = volatilityModel.getTimeDiscretization().getTime(timeIndex);
final RandomVariable meanReversion = volatilityModel.getMeanReversion(timeIndex-1);
final RandomVariable volatility = volatilityModel.getVolatility(timeIndex-1);
final RandomVariable volatilitySquaredPerMeanReversion = volatility.squared().div(meanReversion);
final RandomVariable expMRTimeNext = getMRTime(timeNext,maturity).mult(-2).exp();
integral = integral.add(volatilitySquaredPerMeanReversion.mult(expMRTimeNext.sub(expMRTimePrev).div(2))
);
timePrev = timeNext;
expMRTimePrev = expMRTimeNext;
}
timeNext = maturity;
final RandomVariable meanReversion = volatilityModel.getMeanReversion(timeIndexEnd);
final RandomVariable volatility = volatilityModel.getVolatility(timeIndexEnd);
final RandomVariable volatilitySquaredPerMeanReversion = volatility.squared().div(meanReversion);
final RandomVariable expMRTimeNext = getMRTime(timeNext,maturity).mult(-2).exp();
integral = integral.add(volatilitySquaredPerMeanReversion.mult(expMRTimeNext.sub(expMRTimePrev).div(2))
);
return integral;
}
public RandomVariable getIntegratedBondSquaredVolatility(final double time, final double maturity) {
return getShortRateConditionalVariance(0, time).mult(getB(time,maturity).squared());
}
@Override
public HullWhiteModel getCloneWithModifiedVolatilityModel(final ShortRateVolatilityModel volatilityModel) {
return new HullWhiteModel(randomVariableFactory, liborPeriodDiscretization, analyticModel, forwardRateCurve, discountCurve, volatilityModel, properties);
}
@Override
public ShortRateVolatilityModel getVolatilityModel() {
return volatilityModel;
}
@Override
public Map getModelParameters() {
// TODO Will remember last used process as a chache.
final MonteCarloProcess process = null;
final Map modelParameters = new TreeMap<>();
final TimeDiscretization timeDiscretizationForCurves = isInterpolateDiscountFactorsOnLiborPeriodDiscretization ? liborPeriodDiscretization : process.getTimeDiscretization();
// Add initial values
for(int timeIndex=0; timeIndex= 0) {
return getDiscountFactor(process, timeIndex);
}
else {
// Interpolation
final int timeIndexPrev = Math.min(-timeIndex-2, getLiborPeriodDiscretization().getNumberOfTimes()-2);
final int timeIndexNext = timeIndexPrev+1;
final double timePrev = timeDiscretizationForCurves.getTime(timeIndexPrev);
final double timeNext = timeDiscretizationForCurves.getTime(timeIndexNext);
final RandomVariable discountFactorPrev = getDiscountFactor(process, timeIndexPrev);
final RandomVariable discountFactorNext = getDiscountFactor(process, timeIndexNext);
return discountFactorPrev.mult(discountFactorNext.div(discountFactorPrev).pow((time-timePrev)/(timeNext-timePrev)));
}
}
private RandomVariable getDiscountFactor(final MonteCarloProcess process, final int timeIndex) {
final TimeDiscretization timeDiscretizationForCurves = isInterpolateDiscountFactorsOnLiborPeriodDiscretization ? liborPeriodDiscretization : process.getTimeDiscretization();
final double time = timeDiscretizationForCurves.getTime(timeIndex);
synchronized(numeraireDiscountFactorForwardRates) {
if(numeraireDiscountFactors.size() == 0) {
final double dfInitial = discountCurve.getDiscountFactor(analyticModel, timeDiscretizationForCurves.getTime(0));
RandomVariable deterministicNumeraireAdjustment
= randomVariableFactory.createRandomVariable(dfInitial);
numeraireDiscountFactors.add(0, deterministicNumeraireAdjustment);
for(int i=0; i= 0) {
return getDiscountFactorFromForwardCurve(process, timeIndex);
}
else {
final int timeIndexPrev = Math.min(-timeIndex-2, getLiborPeriodDiscretization().getNumberOfTimes()-2);
final int timeIndexNext = timeIndexPrev+1;
final double timePrev = timeDiscretizationForCurves.getTime(timeIndexPrev);
final double timeNext = timeDiscretizationForCurves.getTime(timeIndexNext);
final RandomVariable discountFactorPrev = getDiscountFactorFromForwardCurve(process, timeIndexPrev);
final RandomVariable discountFactorNext = getDiscountFactorFromForwardCurve(process, timeIndexNext);
return discountFactorPrev.mult(discountFactorNext.div(discountFactorPrev).pow((time-timePrev)/(timeNext-timePrev)));
}
}
private RandomVariable getDiscountFactorFromForwardCurve(final MonteCarloProcess process, final int timeIndex) {
synchronized(discountFactorFromForwardCurveCache) {
if(discountFactorFromForwardCurveCache.size() <= timeIndex) {
// Initialize cache
final TimeDiscretization timeDiscretizationForCurves = isInterpolateDiscountFactorsOnLiborPeriodDiscretization ? liborPeriodDiscretization : process.getTimeDiscretization();
for(int i=discountFactorFromForwardCurveCache.size(); i<=timeIndex; i++) {
RandomVariable dfAsRandomVariable;
if(i == 0) {
final double df = discountCurveFromForwardCurve.getDiscountFactor(analyticModel, timeDiscretizationForCurves.getTime(i));
dfAsRandomVariable = randomVariableFactory.createRandomVariable(df);
}
else {
final RandomVariable dfPrevious = discountFactorFromForwardCurveCache.get(i-1);
final RandomVariable forwardRate = getForwardRateInitialValue(process, i-1);
dfAsRandomVariable = dfPrevious.div(forwardRate.mult(timeDiscretizationForCurves.getTimeStep(i-1)).add(1.0));
}
discountFactorFromForwardCurveCache.add(dfAsRandomVariable);
}
}
}
return discountFactorFromForwardCurveCache.get(timeIndex);
}
private RandomVariable getForwardRateInitialValue(final MonteCarloProcess process, final int timeIndex) {
synchronized(forwardRateCache) {
if(forwardRateCache.size() <= timeIndex) {
// Initialize cache
final TimeDiscretization timeDiscretizationForCurves = isInterpolateDiscountFactorsOnLiborPeriodDiscretization ? liborPeriodDiscretization : process.getTimeDiscretization();
for(int i=forwardRateCache.size(); i<=timeIndex; i++) {
final double dfPrev = discountCurveFromForwardCurve.getDiscountFactor(analyticModel, timeDiscretizationForCurves.getTime(i));
final double dfNext = discountCurveFromForwardCurve.getDiscountFactor(analyticModel, timeDiscretizationForCurves.getTime(i+1));
final RandomVariable forwardRate = randomVariableFactory.createRandomVariable((dfPrev / dfNext - 1.0) / timeDiscretizationForCurves.getTimeStep(i));
forwardRateCache.add(forwardRate);
}
}
}
return forwardRateCache.get(timeIndex);
}
private void readObject(final ObjectInputStream in) throws IOException, ClassNotFoundException {
in.defaultReadObject();
/*
* Init transient fields
*/
numeraireDiscountFactors = new ArrayList<>();
numeraireDiscountFactorForwardRates = new ArrayList<>();
discountFactorFromForwardCurveCache = new ArrayList<>();
forwardRateCache = new ArrayList<>();
}
@Override
public String toString() {
return "HullWhiteModel [liborPeriodDiscretization=" + liborPeriodDiscretization + ", forwardCurveName="
+ forwardCurveName + ", analyticModel=" + analyticModel + ", forwardRateCurve=" + forwardRateCurve
+ ", discountCurve=" + discountCurve + ", discountCurveFromForwardCurve="
+ discountCurveFromForwardCurve + ", randomVariableFactory=" + randomVariableFactory
+ ", volatilityModel=" + volatilityModel + ", properties=" + properties
+ ", isInterpolateDiscountFactorsOnLiborPeriodDiscretization="
+ isInterpolateDiscountFactorsOnLiborPeriodDiscretization + "]";
}
}