Module net.finmath.lib

Package net.finmath.montecarlo.assetderivativevaluation.models

Class InhomogeneousDisplacedLognomalModel

java.lang.Object

net.finmath.montecarlo.model.AbstractProcessModel

net.finmath.montecarlo.assetderivativevaluation.models.InhomogeneousDisplacedLognomalModel

All Implemented Interfaces:

ProcessModel

public class InhomogeneousDisplacedLognomalModel
extends AbstractProcessModel

This class implements an inhomogeneous displaced log-normal 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 \[ \mathrm{d}S = r S dt + \sigma (S + d) \mathrm{d}W, \quad S(0) = S_{0}, \] \[ \mathrm{d}N = r N \mathrm{d}t, \quad N(0) = N_{0}, \] Note that \[ \mathrm{d}(S/N) = \sigma (S/N + d/N) \mathrm{d}W \] i.e. \[ \mathrm{d}(S/N + d/N) = -r d/N dt + \sigma (S/N + d/N) \mathrm{d}W \] The class provides the model of S to an MonteCarloProcess via the specification of (via X = S/N+d/N) \( S = f(X) = N X - d \), \( \mu = d \frac{exp(- r t_2) - exp(- r t_1)}{t_2-t_1} \), \( \lambda_{1,1} = \sigma X \), of the SDE \[ dX = \mu dt + \lambda_{1,1} dW, \quad X(0) = S_{0} + e, \] with \( N(0) = 1 \). See MonteCarloProcess for the notation.

Version:

1.0

Author:

Christian Fries

See Also:

The interface for numerical schemes., The interface for models provinding parameters to numerical schemes.

Constructor Summary

Constructors

Constructor	Description
InhomogeneousDisplacedLognomalModel(double initialValue, double riskFreeRate, double displacement, double volatility)	Create a blended normal/lognormal model.
InhomogeneousDisplacedLognomalModel(double initialValue, double riskFreeRate, double displacement, double volatility, boolean isUseMilsteinCorrection)	Create a blended normal/lognormal model.
InhomogeneousDisplacedLognomalModel(RandomVariableFactory randomVariableFactory, double initialValue, double riskFreeRate, double displacement, double volatility, boolean isUseMilsteinCorrection)	Create a blended normal/lognormal model.
InhomogeneousDisplacedLognomalModel(RandomVariableFactory randomVariableFactory, RandomVariable initialValue, RandomVariable riskFreeRate, RandomVariable displacement, RandomVariable volatility, boolean isUseMilsteinCorrection)	Create a blended normal/lognormal model.

Method Summary

Modifier and Type	Method	Description
RandomVariable	applyStateSpaceTransform(MonteCarloProcess process, int timeIndex, int componentIndex, RandomVariable randomVariable)	Applies the state space transform fi to the given state random variable such that Yi → fi(Yi) =: Xi.
RandomVariable	applyStateSpaceTransformInverse(MonteCarloProcess process, int timeIndex, int componentIndex, RandomVariable randomVariable)	Applies the inverse state space transform f-1i to the given random variable such that Xi → f-1i(Xi) =: Yi.
InhomogeneousDisplacedLognomalModel	getCloneWithModifiedData(Map<String,Object> dataModified)	Returns a clone of this model where the specified properties have been modified.
RandomVariable	getDisplacement()
RandomVariable[]	getDrift(MonteCarloProcess process, int timeIndex, RandomVariable[] realizationAtTimeIndex, RandomVariable[] realizationPredictor)	This method has to be implemented to return the drift, i.e.
RandomVariable[]	getFactorLoading(MonteCarloProcess process, int timeIndex, int component, RandomVariable[] realizationAtTimeIndex)	This method has to be implemented to return the factor loadings, i.e.
RandomVariable[]	getInitialState(MonteCarloProcess process)	Returns the initial value of the state variable of the process Y, not to be confused with the initial value of the model X (which is the state space transform applied to this state value.
RandomVariable	getInitialValue()
int	getNumberOfComponents()	Returns the number of components
int	getNumberOfFactors()	Returns the number of factors m, i.e., the number of independent Brownian drivers.
RandomVariable	getNumeraire(MonteCarloProcess process, double time)	Return the numeraire at a given time index.
RandomVariableFactory	getRandomVariableFactory()
RandomVariable	getRandomVariableForConstant(double value)	Return a random variable initialized with a constant using the models random variable factory.
RandomVariable	getRiskFreeRate()	Returns the risk free rate parameter of this model.
RandomVariable	getVolatility()	Returns the volatility parameter of this model.
String	toString()

Methods inherited from class net.finmath.montecarlo.model.AbstractProcessModel

getInitialValue, getReferenceDate

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Details

InhomogeneousDisplacedLognomalModel

public InhomogeneousDisplacedLognomalModel(RandomVariableFactory randomVariableFactory,
 RandomVariable initialValue,
 RandomVariable riskFreeRate,
 RandomVariable displacement,
 RandomVariable volatility,
 boolean isUseMilsteinCorrection)

Create a blended normal/lognormal model.

Parameters:

randomVariableFactory - The RandomVariableFactory used to generate random variables from constants.

initialValue - Spot value.

riskFreeRate - The risk free rate.

displacement - The displacement parameter d.

volatility - The volatility.

isUseMilsteinCorrection - If true, a Milstein scheme correction is applied in the drift.

InhomogeneousDisplacedLognomalModel

public InhomogeneousDisplacedLognomalModel(RandomVariableFactory randomVariableFactory,
 double initialValue,
 double riskFreeRate,
 double displacement,
 double volatility,
 boolean isUseMilsteinCorrection)

Create a blended normal/lognormal model.

Parameters:

randomVariableFactory - The RandomVariableFactory used to generate random variables from constants.

initialValue - Spot value.

riskFreeRate - The risk free rate.

displacement - The displacement parameter d.

volatility - The volatility.

isUseMilsteinCorrection - If true, the drift will include the Milstein correction (making an Euler scheme a Milstein scheme).

InhomogeneousDisplacedLognomalModel

public InhomogeneousDisplacedLognomalModel(double initialValue,
 double riskFreeRate,
 double displacement,
 double volatility,
 boolean isUseMilsteinCorrection)

Create a blended normal/lognormal model.

Parameters:

initialValue - Spot value.

riskFreeRate - The risk free rate.

displacement - The displacement parameter d.

volatility - The volatility.

isUseMilsteinCorrection - If true, the drift will include the Milstein correction (making an Euler scheme a Milstein scheme).

InhomogeneousDisplacedLognomalModel

public InhomogeneousDisplacedLognomalModel(double initialValue,
 double riskFreeRate,
 double displacement,
 double volatility)

Create a blended normal/lognormal model.

Parameters:

initialValue - Spot value.

riskFreeRate - The risk free rate.

displacement - The displacement parameter d.

volatility - The volatility.

Method Details

getInitialState

public RandomVariable[] getInitialState(MonteCarloProcess process)

Description copied from interface: ProcessModel

Returns the initial value of the state variable of the process Y, not to be confused with the initial value of the model X (which is the state space transform applied to this state value.

Parameters:

process - The discretization process generating this model. The process provides call backs for TimeDiscretization and allows calls to getProcessValue for timeIndices less or equal the given one.

Returns:

The initial value of the state variable of the process Y(t=0).

getDrift

public RandomVariable[] getDrift(MonteCarloProcess process,
 int timeIndex,
 RandomVariable[] realizationAtTimeIndex,
 RandomVariable[] realizationPredictor)

Description copied from interface: ProcessModel

This method has to be implemented to return the drift, i.e. the coefficient vector
μ = (μ₁, ..., μ_n) such that X = f(Y) and
dY_j = μ_j dt + λ_1,j dW₁ + ... + λ_m,j dW_m
in an m-factor model. Here j denotes index of the component of the resulting process. Since the model is provided only on a time discretization, the method may also (should try to) return the drift as \( \frac{1}{t_{i+1}-t_{i}} \int_{t_{i}}^{t_{i+1}} \mu(\tau) \mathrm{d}\tau \).

Parameters:

process - The discretization process generating this model. The process provides call backs for TimeDiscretization and allows calls to getProcessValue for timeIndices less or equal the given one.

timeIndex - The time index (related to the model times discretization).

realizationAtTimeIndex - The given realization at timeIndex

realizationPredictor - The given realization at timeIndex+1 or null if no predictor is available.

Returns:

The drift or average drift from timeIndex to timeIndex+1, i.e. \( \frac{1}{t_{i+1}-t_{i}} \int_{t_{i}}^{t_{i+1}} \mu(\tau) \mathrm{d}\tau \) (or a suitable approximation).

getFactorLoading

public RandomVariable[] getFactorLoading(MonteCarloProcess process,
 int timeIndex,
 int component,
 RandomVariable[] realizationAtTimeIndex)

Description copied from interface: ProcessModel

This method has to be implemented to return the factor loadings, i.e. the coefficient vector
λ_j = (λ_1,j, ..., λ_m,j) such that X = f(Y) and
dY_j = μ_j dt + λ_1,j dW₁ + ... + λ_m,j dW_m
in an m-factor model. Here j denotes index of the component of the resulting process.

Parameters:

process - The discretization process generating this model. The process provides call backs for TimeDiscretization and allows calls to getProcessValue for timeIndices less or equal the given one.

timeIndex - The time index (related to the model times discretization).

component - The index j of the driven component.

realizationAtTimeIndex - The realization of X at the time corresponding to timeIndex (in order to implement local and stochastic volatlity models).

Returns:

The factor loading for given factor and component.

applyStateSpaceTransform

public RandomVariable applyStateSpaceTransform(MonteCarloProcess process,
 int timeIndex,
 int componentIndex,
 RandomVariable randomVariable)

Description copied from interface: ProcessModel

Applies the state space transform f_i to the given state random variable such that Y_i → f_i(Y_i) =: X_i.

Parameters:

process - The discretization process generating this model. The process provides call backs for TimeDiscretization and allows calls to getProcessValue for timeIndices less or equal the given one.

timeIndex - The time index (related to the model times discretization).

componentIndex - The component index i.

randomVariable - The state random variable Y_i.

Returns:

New random variable holding the result of the state space transformation.

applyStateSpaceTransformInverse

public RandomVariable applyStateSpaceTransformInverse(MonteCarloProcess process,
 int timeIndex,
 int componentIndex,
 RandomVariable randomVariable)

Description copied from interface: ProcessModel

Applies the inverse state space transform f^-1_i to the given random variable such that X_i → f^-1_i(X_i) =: Y_i.

Parameters:

process - The discretization process generating this model. The process provides call backs for TimeDiscretization and allows calls to getProcessValue for timeIndices less or equal the given one.

timeIndex - The time index (related to the model times discretization).

componentIndex - The component index i.

randomVariable - The state random variable X_i.

Returns:

New random variable holding the result of the state space transformation.

getNumeraire

public RandomVariable getNumeraire(MonteCarloProcess process,
 double time)

Description copied from interface: ProcessModel

Return the numeraire at a given time index. Note: The random variable returned is a defensive copy and may be modified.

Parameters:

process - The discretization process generating this model. The process provides call backs for TimeDiscretization and allows calls to getProcessValue for timeIndices less or equal the given one.

time - The time t for which the numeraire N(t) should be returned.

Returns:

The numeraire at the specified time as RandomVariable

getNumberOfComponents

public int getNumberOfComponents()

Description copied from interface: ProcessModel

Returns the number of components

Returns:

The number of components

getNumberOfFactors

public int getNumberOfFactors()

Description copied from interface: ProcessModel

Returns the number of factors m, i.e., the number of independent Brownian drivers.

Returns:

The number of factors.

getRandomVariableForConstant

public RandomVariable getRandomVariableForConstant(double value)

Description copied from interface: ProcessModel

Return a random variable initialized with a constant using the models random variable factory.

Parameters:

value - The constant value.

Returns:

A new random variable initialized with a constant value.

getCloneWithModifiedData

public InhomogeneousDisplacedLognomalModel getCloneWithModifiedData(Map<String,Object> dataModified)

Description copied from interface: ProcessModel

Returns a clone of this model where the specified properties have been modified. Note that there is no guarantee that a model reacts on a specification of a properties in the parameter map dataModified. If data is provided which is ignored by the model no exception may be thrown.

Parameters:

dataModified - Key-value-map of parameters to modify.

Returns:

A clone of this model (or this model if no parameter was modified).

toString

public String toString()

Overrides:

toString in class Object

getRandomVariableFactory

public RandomVariableFactory getRandomVariableFactory()

getInitialValue

public RandomVariable getInitialValue()

getRiskFreeRate

public RandomVariable getRiskFreeRate()

Returns the risk free rate parameter of this model.

Returns:

Returns the riskFreeRate.

getDisplacement

public RandomVariable getDisplacement()

getVolatility

public RandomVariable getVolatility()

Returns the volatility parameter of this model.

Returns:

Returns the volatility.