net.finmath.montecarlo.interestrate.models.covariance.LIBORCovarianceModelStochasticVolatility 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.
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/*
 * (c) Copyright Christian P. Fries, Germany. Contact: [email protected].
 *
 * Created on 15 Jan 2015
 */

package net.finmath.montecarlo.interestrate.models.covariance;

import java.time.LocalDateTime;
import java.util.Map;

import net.finmath.exception.CalculationException;
import net.finmath.montecarlo.BrownianMotion;
import net.finmath.montecarlo.BrownianMotionView;
import net.finmath.montecarlo.RandomVariableFactory;
import net.finmath.montecarlo.model.ProcessModel;
import net.finmath.montecarlo.process.EulerSchemeFromProcessModel;
import net.finmath.montecarlo.process.MonteCarloProcess;
import net.finmath.stochastic.RandomVariable;
import net.finmath.stochastic.Scalar;

/**
 * Simple stochastic volatility model, using a process
 * \[
 * 	d\lambda(t) = \nu \lambda(t) \left( \rho \mathrm{d} W_{1}(t) + \sqrt{1-\rho^{2}} \mathrm{d} W_{2}(t) \right) \text{,}
 * \]
 * where \( \lambda(0) = 1 \) to scale all factor loadings \( f_{i} \) returned by a given covariance model.
 *
 * The model constructed is \( \lambda(t) F(t) \) where \( \lambda(t) \) is
 * the (Euler discretization of the) above process and \( F = ( f_{1}, \ldots, f_{m} ) \) is the factor loading
 * from the given covariance model.
 *
 * The process uses the first two factors of the Brownian motion provided by an object implementing
 * {@link net.finmath.montecarlo.BrownianMotion}. This can be used to generate correlations to
 * other objects. If you like to reuse a factor of another Brownian motion use a
 * {@link net.finmath.montecarlo.BrownianMotionView}
 * to delegate \( ( \mathrm{d} W_{1}(t) , \mathrm{d} W_{2}(t) ) \) to a different object.
 *
 * The parameter of this model is a joint parameter vector, consisting
 * of the parameter vector of the given base covariance model and
 * appending the parameters ν and ρ at the end.
 *
 * If this model is not calibrateable, its parameter vector is that of the
 * covariance model, i.e., ν and ρ will be not
 * part of the calibration.
 *
 * For an illustration of its usage see the associated unit test.
 *
 * @author Christian Fries
 * @version 1.0
 */
public class LIBORCovarianceModelStochasticVolatility extends AbstractLIBORCovarianceModelParametric {

	private static final long serialVersionUID = -559341617850035368L;
	private AbstractLIBORCovarianceModelParametric covarianceModel;
	private final BrownianMotion brownianMotion;
	private	RandomVariable rho, nu;

	private boolean isCalibrateable = false;

	private MonteCarloProcess stochasticVolatilityScalings = null;

	/**
	 * Create a modification of a given {@link AbstractLIBORCovarianceModelParametric} with a stochastic volatility scaling.
	 *
	 * @param covarianceModel A given AbstractLIBORCovarianceModelParametric.
	 * @param brownianMotion An object implementing {@link BrownianMotion} with at least two factors. This class uses the first two factors, but you may use {@link BrownianMotionView} to change this.
	 * @param nu The initial value for ν, the volatility of the volatility.
	 * @param rho The initial value for ρ the correlation to the first factor.
	 * @param isCalibrateable If true, the parameters ν and ρ are parameters. Note that the covariance model (covarianceModel) may have its own parameter calibration settings.
	 */
	public LIBORCovarianceModelStochasticVolatility(final AbstractLIBORCovarianceModelParametric covarianceModel, final BrownianMotion brownianMotion, final RandomVariable nu, final RandomVariable rho, final boolean isCalibrateable) {
		super(covarianceModel.getTimeDiscretization(), covarianceModel.getLiborPeriodDiscretization(), covarianceModel.getNumberOfFactors());

		this.covarianceModel = covarianceModel;
		this.brownianMotion = brownianMotion;
		this.nu		= nu;
		this.rho		= rho;

		this.isCalibrateable = isCalibrateable;
	}

	/**
	 * Create a modification of a given {@link AbstractLIBORCovarianceModelParametric} with a stochastic volatility scaling.
	 *
	 * @param covarianceModel A given AbstractLIBORCovarianceModelParametric.
	 * @param brownianMotion An object implementing {@link BrownianMotion} with at least two factors. This class uses the first two factors, but you may use {@link BrownianMotionView} to change this.
	 * @param nu The initial value for ν, the volatility of the volatility.
	 * @param rho The initial value for ρ the correlation to the first factor.
	 * @param isCalibrateable If true, the parameters ν and ρ are parameters. Note that the covariance model (covarianceModel) may have its own parameter calibration settings.
	 */
	public LIBORCovarianceModelStochasticVolatility(final AbstractLIBORCovarianceModelParametric covarianceModel, final BrownianMotion brownianMotion, final double nu, final double rho, final boolean isCalibrateable) {
		super(covarianceModel.getTimeDiscretization(), covarianceModel.getLiborPeriodDiscretization(), covarianceModel.getNumberOfFactors());

		this.covarianceModel = covarianceModel;
		this.brownianMotion = brownianMotion;
		this.nu		= new Scalar(nu);
		this.rho	= new Scalar(rho);

		this.isCalibrateable = isCalibrateable;
	}

	@Override
	public RandomVariable[] getParameter() {
		if(!isCalibrateable) {
			return covarianceModel.getParameter();
		}

		final RandomVariable[] covarianceParameters = covarianceModel.getParameter();
		if(covarianceParameters == null) {
			return new RandomVariable[] { nu, rho };
		}

		// Append nu and rho to the end of covarianceParameters
		final RandomVariable[] jointParameters = new RandomVariable[covarianceParameters.length+2];
		System.arraycopy(covarianceParameters, 0, jointParameters, 0, covarianceParameters.length);
		jointParameters[covarianceParameters.length+0] = nu;
		jointParameters[covarianceParameters.length+1] = rho;

		return jointParameters;
	}

	//	@Override
	private void setParameter(final RandomVariable[] parameter) {
		if(parameter == null || parameter.length == 0) {
			return;
		}

		if(!isCalibrateable) {
			covarianceModel = covarianceModel.getCloneWithModifiedParameters(parameter);
			return;
		}

		final RandomVariable[] covarianceParameters = new RandomVariable[parameter.length-2];
		System.arraycopy(parameter, 0, covarianceParameters, 0, covarianceParameters.length);

		covarianceModel = covarianceModel.getCloneWithModifiedParameters(covarianceParameters);

		nu	= parameter[covarianceParameters.length + 0];
		rho	= parameter[covarianceParameters.length + 1];

		stochasticVolatilityScalings = null;
	}

	@Override
	public Object clone() {
		final LIBORCovarianceModelStochasticVolatility newModel = new LIBORCovarianceModelStochasticVolatility((AbstractLIBORCovarianceModelParametric) covarianceModel.clone(), brownianMotion, nu, rho, isCalibrateable);
		return newModel;
	}

	@Override
	public AbstractLIBORCovarianceModelParametric getCloneWithModifiedParameters(final RandomVariable[] parameters) {
		final LIBORCovarianceModelStochasticVolatility model = (LIBORCovarianceModelStochasticVolatility)this.clone();
		model.setParameter(parameters);
		return model;
	}

	@Override
	public AbstractLIBORCovarianceModelParametric getCloneWithModifiedParameters(final double[] parameters) {
		return getCloneWithModifiedParameters(Scalar.arrayOf(parameters));
	}

	@Override
	public double[] getParameterAsDouble() {
		final RandomVariable[] parameters = getParameter();
		final double[] parametersAsDouble = new double[parameters.length];
		for(int i=0; i dataModified) {
						throw new UnsupportedOperationException("Method not implemented");
					}
				};
				stochasticVolatilityScalings = new EulerSchemeFromProcessModel(model, brownianMotion);
			}
		}

		RandomVariable stochasticVolatilityScaling = null;
		try {
			stochasticVolatilityScaling = stochasticVolatilityScalings.getProcessValue(timeIndex,0);
		} catch (final CalculationException e) {
			// Exception is not handled explicitly, we just return null
		}

		RandomVariable[] factorLoading = null;

		if(stochasticVolatilityScaling != null) {
			factorLoading = covarianceModel.getFactorLoading(timeIndex, component, realizationAtTimeIndex);
			for(int i=0; i dataModified)
			throws CalculationException {
		BrownianMotion brownianMotion = this.brownianMotion;
		RandomVariable nu = this.nu;
		RandomVariable rho = this.rho;
		boolean isCalibrateable = this.isCalibrateable;
		AbstractLIBORCovarianceModelParametric covarianceModel = this.covarianceModel;
		RandomVariableFactory randomVariableFactory = null;

		if(dataModified != null) {
			if(dataModified.containsKey("randomVariableFactory")) {
				randomVariableFactory = (RandomVariableFactory)dataModified.get("randomVariableFactory");
				nu = randomVariableFactory.createRandomVariable(nu.doubleValue());
				rho = randomVariableFactory.createRandomVariable(rho.doubleValue());
			}
			if (!dataModified.containsKey("covarianceModel")) {
				covarianceModel = covarianceModel.getCloneWithModifiedData(dataModified);
			}

			// Explicitly passed covarianceModel has priority
			covarianceModel = (AbstractLIBORCovarianceModelParametric)dataModified.getOrDefault("covarianceModel", covarianceModel);
			isCalibrateable = (boolean)dataModified.getOrDefault("isCalibrateable", isCalibrateable);
			brownianMotion = (BrownianMotion)dataModified.getOrDefault("brownianMotion", brownianMotion);

			if(dataModified.getOrDefault("nu", nu) instanceof RandomVariable) {
				nu = (RandomVariable)dataModified.getOrDefault("nu", nu);
			}else if(randomVariableFactory == null){
				nu = new Scalar((double)dataModified.get("nu"));
			}else {
				nu = randomVariableFactory.createRandomVariable((double)dataModified.get("nu"));
			}
			if(dataModified.getOrDefault("rho", rho) instanceof RandomVariable) {
				rho = (RandomVariable)dataModified.getOrDefault("rho", rho);
			}else if(randomVariableFactory == null){
				rho = new Scalar((double)dataModified.get("rho"));
			}else {
				rho = randomVariableFactory.createRandomVariable((double)dataModified.get("rho"));
			}
		}

		final AbstractLIBORCovarianceModelParametric newModel = new LIBORCovarianceModelStochasticVolatility(covarianceModel, brownianMotion, nu, rho, isCalibrateable);
		return newModel;
	}
}