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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.models;
import java.util.Map;
import net.finmath.montecarlo.RandomVariableFactory;
import net.finmath.montecarlo.RandomVariableFromArrayFactory;
import net.finmath.montecarlo.model.AbstractProcessModel;
import net.finmath.montecarlo.process.MonteCarloProcess;
import net.finmath.stochastic.RandomVariable;
import net.finmath.stochastic.Scalar;
/**
* This class implements a (variant of the) Bachelier model, that is,
* it provides the drift and volatility specification
* and performs the calculation of the numeraire (consistent with the dynamics, i.e. the drift).
*
* The model is
* \[
* d(S/N) = \sigma dW, \quad S(0) = S_{0},
* \]
* \[
* dN = r N dt, \quad N(0) = N_{0},
* \]
*
* Note: This implies the dynamic
* \[
* dS = r S dt + \sigma exp(r t) dW, \quad S(0) = S_{0},
* \]
* for \( S \). For The model
* \[
* dS = r S dt + \sigma dW, \quad S(0) = S_{0},
* \]
* see {@link net.finmath.montecarlo.assetderivativevaluation.models.InhomogenousBachelierModel}.
*
* The model's implied Bachelier volatility for a given maturity T is
* volatility * Math.exp(riskFreeRate * optionMaturity)
*
* The class provides the model of S to an {@link net.finmath.montecarlo.process.MonteCarloProcess} via the specification of
* \( f = \text{identity} \), \( \mu = \frac{exp(r \Delta t_{i}) - 1}{\Delta t_{i}} S(t_{i}) \), \( \lambda_{1,1} = \sigma \), i.e.,
* of the SDE
* \[
* dX = \mu dt + \lambda_{1,1} dW, \quad X(0) = \log(S_{0}),
* \]
* with \( S = X \). See {@link net.finmath.montecarlo.process.MonteCarloProcess} for the notation.
*
* @author Christian Fries
* @version 1.0
* @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.
*/
public class BachelierModel extends AbstractProcessModel {
private final RandomVariableFactory randomVariableFactory;
private final RandomVariable initialValue;
private final RandomVariable riskFreeRate; // Actually the same as the drift (which is not stochastic)
private final RandomVariable volatility;
/**
* Create a Monte-Carlo simulation using given time discretization.
*
* @param randomVariableFactory The RandomVariableFactory used to generate random variables from constants.
* @param initialValue Spot value.
* @param riskFreeRate The risk free rate.
* @param volatility The volatility.
*/
public BachelierModel(RandomVariableFactory randomVariableFactory, RandomVariable initialValue, RandomVariable riskFreeRate, RandomVariable volatility) {
super();
this.randomVariableFactory = randomVariableFactory;
this.initialValue = initialValue;
this.riskFreeRate = riskFreeRate;
this.volatility = volatility;
}
/**
* Create a Monte-Carlo simulation using given time discretization.
*
* @param initialValue Spot value.
* @param riskFreeRate The risk free rate.
* @param volatility The volatility.
*/
public BachelierModel(
final double initialValue,
final double riskFreeRate,
final double volatility) {
super();
this.randomVariableFactory = new RandomVariableFromArrayFactory();
this.initialValue = randomVariableFactory.createRandomVariable(initialValue);
this.riskFreeRate = randomVariableFactory.createRandomVariable(riskFreeRate);
this.volatility = randomVariableFactory.createRandomVariable(volatility);
}
@Override
public RandomVariable[] getInitialState(MonteCarloProcess process) {
return new RandomVariable[] { initialValue };
}
@Override
public RandomVariable[] getDrift(final MonteCarloProcess process, final int timeIndex, final RandomVariable[] realizationAtTimeIndex, final RandomVariable[] realizationPredictor) {
final RandomVariable[] drift = new RandomVariable[realizationAtTimeIndex.length];
for(int componentIndex = 0; componentIndex dataModified) {
/*
* Determine the new model parameters from the provided parameter map.
*/
final RandomVariableFactory newRandomVariableFactory = (RandomVariableFactory)dataModified.getOrDefault("randomVariableFactory", randomVariableFactory);
final RandomVariable newInitialValue = RandomVariableFactory.getRandomVariableOrDefault(newRandomVariableFactory, dataModified.get("initialValue"), initialValue);
final RandomVariable newRiskFreeRate = RandomVariableFactory.getRandomVariableOrDefault(newRandomVariableFactory, dataModified.get("riskFreeRate"), riskFreeRate);
final RandomVariable newVolatility = RandomVariableFactory.getRandomVariableOrDefault(newRandomVariableFactory, dataModified.get("volatility"), volatility);
return new BachelierModel(newRandomVariableFactory, newInitialValue, newRiskFreeRate, newVolatility);
}
@Override
public String toString() {
return super.toString() + "\n" +
"BachelierModel:\n" +
" initial value...:" + initialValue + "\n" +
" risk free rate..:" + riskFreeRate + "\n" +
" volatiliy.......:" + volatility;
}
/**
* Returns the initial value parameter of this model.
*
* @return Returns the initialValue
*/
public RandomVariable getInitialValue() {
return initialValue;
}
/**
* Returns the risk free rate parameter of this model.
*
* @return Returns the riskFreeRate.
*/
public RandomVariable getRiskFreeRate() {
return riskFreeRate;
}
/**
* Returns the volatility parameter of this model.
*
* @return Returns the volatility.
*/
public RandomVariable getVolatility() {
return volatility;
}
public RandomVariable getImpliedBachelierVolatility(final double maturity) {
return volatility.mult(riskFreeRate.mult(maturity).exp());
}
}