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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 20.01.2004
*/
package net.finmath.montecarlo.assetderivativevaluation;
import java.time.LocalDateTime;
import java.util.HashMap;
import java.util.Map;
import net.finmath.exception.CalculationException;
import net.finmath.montecarlo.IndependentIncrements;
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.time.TimeDiscretization;
/**
* This class glues together an AbstractProcessModel
and a Monte-Carlo implementation of a MonteCarloProcessFromProcessModel
* and implements AssetModelMonteCarloSimulationModel
.
*
* The model is specified via the object implementing ProcessModel
.
*
* @author Christian Fries
* @see net.finmath.montecarlo.process.MonteCarloProcess The interface for numerical schemes.
* @see net.finmath.montecarlo.model.ProcessModel The interface for models provinding parameters to numerical schemes.
* @version 1.0
*/
public class MonteCarloAssetModel implements AssetModelMonteCarloSimulationModel {
private final ProcessModel model;
private final MonteCarloProcess process;
/**
* Create a Monte-Carlo simulation using given process discretization scheme.
*
* @param process The numerical scheme to be used.
*/
public MonteCarloAssetModel(final MonteCarloProcess process) {
super();
this.model = process.getModel();
this.process = process;
}
/**
* Convenient constructor being the same as this(new EulerSchemeFromProcessModel(model, stochasticDriver))
*
* @param model The model to use for the EulerSchemeFromProcessModel.
* @param stochasticDriver The stochatic driver to use.
*/
public MonteCarloAssetModel(ProcessModel model, IndependentIncrements stochasticDriver) {
this(new EulerSchemeFromProcessModel(model, stochasticDriver));
}
/**
* Create a Monte-Carlo simulation using given process discretization scheme.
*
* @param model The model to be used.
* @param process The numerical scheme to be used.
* @deprecated May be made private in future releases.
*/
@Deprecated
public MonteCarloAssetModel(
final ProcessModel model,
final MonteCarloProcess process) {
super();
this.model = model;
this.process = process;
}
@Override
public RandomVariable getAssetValue(final double time, final int assetIndex) throws CalculationException {
final int timeIndex = getTimeIndex(time);
if(timeIndex < 0) {
throw new IllegalArgumentException("The model does not provide an interpolation of simulation time (time given was " + time + ").");
}
return getAssetValue(timeIndex, assetIndex);
}
@Override
public RandomVariable getAssetValue(final int timeIndex, final int assetIndex) throws CalculationException {
return process.getProcessValue(timeIndex, assetIndex);
}
@Override
public RandomVariable getNumeraire(final int timeIndex) throws CalculationException {
final double time = getTime(timeIndex);
// TODO Add caching of the numerare here!
return model.getNumeraire(process, time);
}
@Override
public RandomVariable getNumeraire(final double time) throws CalculationException {
// TODO Add caching of the numerare here!
return model.getNumeraire(process, time);
}
@Override
public RandomVariable getMonteCarloWeights(final double time) throws CalculationException {
return getMonteCarloWeights(getTimeIndex(time));
}
@Override
public int getNumberOfAssets() {
return 1;
}
@Override
public MonteCarloAssetModel getCloneWithModifiedData(final Map dataModified) throws CalculationException {
final ProcessModel newModel = model.getCloneWithModifiedData(dataModified);
MonteCarloProcess newProcess;
try {
final Map dataModifiedForProcess = new HashMap();
dataModifiedForProcess.putAll(dataModified);
if(!dataModifiedForProcess.containsKey("model")) {
dataModifiedForProcess.put("model", newModel);
}
newProcess = process.getCloneWithModifiedData(dataModifiedForProcess);
}
catch(final UnsupportedOperationException e) {
newProcess = process;
}
// In the case where the model has changed we need a new process anyway
if(newModel != model && newProcess == process) {
newProcess = process.getCloneWithModifiedModel(newModel);
}
return new MonteCarloAssetModel(newProcess);
}
/**
* The method is not implemented. Instead call getCloneWithModifiedData on the model
* an create a new process from it.
*
* @param seed The new seed.
*/
@Override
@Deprecated
public AssetModelMonteCarloSimulationModel getCloneWithModifiedSeed(final int seed) {
throw new UnsupportedOperationException("Method not implemented");
}
@Override
public int getNumberOfPaths() {
return process.getNumberOfPaths();
}
@Override
public LocalDateTime getReferenceDate() {
return model.getReferenceDate();
}
@Override
public TimeDiscretization getTimeDiscretization() {
return process.getTimeDiscretization();
}
@Override
public double getTime(final int timeIndex) {
return process.getTime(timeIndex);
}
@Override
public int getTimeIndex(final double time) {
return process.getTimeIndex(time);
}
@Override
public RandomVariable getRandomVariableForConstant(final double value) {
return model.getRandomVariableForConstant(value);
}
@Override
public RandomVariable getMonteCarloWeights(final int timeIndex) throws CalculationException {
return process.getMonteCarloWeights(timeIndex);
}
/**
* Returns the {@link ProcessModel} used for this Monte-Carlo simulation.
*
* @return the model
*/
public ProcessModel getModel() {
return model;
}
/**
* Returns the {@link MonteCarloProcess} used for this Monte-Carlo simulation.
*
* @return the process
*/
public MonteCarloProcess getProcess() {
return process;
}
@Override
public String toString() {
return this.getClass().getSimpleName() + " [model=" + model + "]";
}
}