net.finmath.montecarlo.interestrate.models.covariance.AbstractShortRateVolatilityModelParametric Maven / Gradle / Ivy
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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 20.05.2006
*/
package net.finmath.montecarlo.interestrate.models.covariance;
import java.text.DecimalFormat;
import java.text.Format;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Future;
import java.util.concurrent.FutureTask;
import java.util.logging.Level;
import java.util.logging.Logger;
import net.finmath.exception.CalculationException;
import net.finmath.montecarlo.BrownianMotion;
import net.finmath.montecarlo.BrownianMotionFromMersenneRandomNumbers;
import net.finmath.montecarlo.interestrate.CalibrationProduct;
import net.finmath.montecarlo.interestrate.LIBORMonteCarloSimulationFromLIBORModel;
import net.finmath.montecarlo.interestrate.ShortRateModel;
import net.finmath.montecarlo.interestrate.models.HullWhiteModel;
import net.finmath.montecarlo.process.EulerSchemeFromProcessModel;
import net.finmath.optimizer.Optimizer;
import net.finmath.optimizer.Optimizer.ObjectiveFunction;
import net.finmath.optimizer.OptimizerFactory;
import net.finmath.optimizer.OptimizerFactoryLevenbergMarquardt;
import net.finmath.optimizer.SolverException;
import net.finmath.stochastic.RandomVariable;
import net.finmath.time.TimeDiscretization;
/**
* Base class for parametric volatility models, see also {@link AbstractShortRateVolatilityModel}.
*
* Parametric models feature a parameter vector which can be inspected
* and modified for calibration purposes.
*
* The parameter vector may have zero length, which indicated that the model
* is not calibrateable.
*
* This class includes the implementation of a generic calibration algorithm.
* If you provide an arbitrary list of calibration products, the class can return
* a new instance where the parameters are chosen such that the (weighted) root-mean-square
* error of the difference of the value of the calibration products and given target
* values is minimized.
*
* @author Ruben Duarte
* @version 1.1
*/
public abstract class AbstractShortRateVolatilityModelParametric extends AbstractShortRateVolatilityModel implements ShortRateVolatilityModelParametric, ShortRateVolatilityModelCalibrateable {
private static final long serialVersionUID = 7015719361182945464L;
private static final Logger logger = Logger.getLogger("net.finmath");
/**
* Constructor consuming time discretization.
*
* @param timeDiscretization The vector of simulation time discretization points.
*/
public AbstractShortRateVolatilityModelParametric(final TimeDiscretization timeDiscretization) {
super(timeDiscretization);
}
/**
* Get the parameters of determining this parametric
* volatility model. The parameters are usually free parameters
* which may be used in calibration.
*
* @return Parameter vector.
*/
@Override
public abstract RandomVariable[] getParameter();
@Override
public abstract Object clone();
@Override
public double[] getParameterAsDouble() {
final RandomVariable[] parameters = getParameter();
final double[] parametersAsDouble = new double[parameters.length];
for(int i=0; iString s):
* -
*
brownianMotion: Under this key an object implementing {@link net.finmath.montecarlo.BrownianMotion} may be provided. If so, this Brownian motion is used to build the valuation model.
* maxIterations: Under this key an object of type Integer may be provided specifying the maximum number of iterations.
* accuracy: Under this key an object of type Double may be provided specifying the desired accuracy. Note that this is understood in the sense that the solver will stop if the iteration does not improve by more than this number.
*
this one, but with calibrated parameters.
* @throws CalculationException Thrown if calibration has failed.
*/
/* (non-Javadoc)
* @see net.finmath.montecarlo.interestrate.models.covariance.ShortRateVolatilityModelCalibrateable#getCloneCalibrated(net.finmath.montecarlo.interestrate.ShortRateModel, net.finmath.montecarlo.interestrate.CalibrationProduct[], java.util.Map)
*/
@Override
public AbstractShortRateVolatilityModelParametric getCloneCalibrated(final ShortRateModel calibrationModel, final CalibrationProduct[] calibrationProducts, final Map-
*
brownianMotion: Under this key an object implementing {@link net.finmath.montecarlo.BrownianMotion} may be provided. If so, this Brownian motion is used to build the valuation model.
* maxIterations: Under this key an object of type Integer may be provided specifying the maximum number of iterations.
* accuracy: Under this key an object of type Double may be provided specifying the desired accuracy. Note that this is understood in the sense that the solver will stop if the iteration does not improve by more than this number.
*
this one, but with calibrated parameters.
* @throws CalculationException Thrown if calibration has failed.
*/
public AbstractShortRateVolatilityModelParametric getCloneCalibratedLegazy(final ShortRateModel calibrationModel, final CalibrationProduct[] calibrationProducts, Map