net.finmath.montecarlo.model

Interface ProcessModel

All Known Subinterfaces:

LIBORMarketModel, LIBORModel, TermStructureModel

All Known Implementing Classes:

AbstractProcessModel, BachelierModel, BlackScholesModel, BlackScholesModelWithCurves, DisplacedLognomalModelExperimental, HestonModel, HullWhiteModel, HullWhiteModelWithConstantCoeff, HullWhiteModelWithDirectSimulation, HullWhiteModelWithShiftExtension, InhomogeneousDisplacedLognomalModel, InhomogenousBachelierModel, LIBORMarketModelFromCovarianceModel, LIBORMarketModelStandard, LIBORMarketModelWithTenorRefinement, MertonModel, MonteCarloMultiAssetBlackScholesModel, VarianceGammaModel

public interface ProcessModel

The interface for a model of a stochastic process X where X = f(Y) and
\[ dY_{j} = \mu_{j} dt + \lambda_{1,j} dW_{1} + \ldots + \lambda_{m,j} dW_{m} \]

• The value of Y(0) is provided by the method getInitialState().
• The value of μ is provided by the method getDrift(int, net.finmath.stochastic.RandomVariable[], net.finmath.stochastic.RandomVariable[]).
• The value λ_j is provided by the method getFactorLoading(int, int, net.finmath.stochastic.RandomVariable[]).
• The function f is provided by the method applyStateSpaceTransform(int, net.finmath.stochastic.RandomVariable).

Here, μ and λ_j may depend on X, which allows to implement stochastic drifts (like in a LIBOR market model) of local volatility models.
Examples:

• The Black Scholes model can be modeled by S = X = Y (i.e. f is the identity) and μ₁ = r S and λ_1,1 = σ S.
• Alternatively, the Black Scholes model can be modeled by S = X = exp(Y) (i.e. f is exp) and μ₁ = r - 0.5 σ σ and λ_1,1 = σ.

Version:

1.0

Author:

Christian Fries

Method Summary

Modifier and Type	Method and Description
RandomVariable	applyStateSpaceTransform(int componentIndex, RandomVariable randomVariable) Applies the state space transform fi to the given state random variable such that Yi → fi(Yi) =: Xi.
default RandomVariable	applyStateSpaceTransformInverse(int componentIndex, RandomVariable randomVariable)
ProcessModel	getCloneWithModifiedData(Map<String,Object> dataModified) Returns a clone of this model where the specified properties have been modified.
RandomVariable[]	getDrift(int timeIndex, RandomVariable[] realizationAtTimeIndex, RandomVariable[] realizationPredictor) This method has to be implemented to return the drift, i.e.
RandomVariable[]	getFactorLoading(int timeIndex, int componentIndex, RandomVariable[] realizationAtTimeIndex) This method has to be implemented to return the factor loadings, i.e.
RandomVariable[]	getInitialState() 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.
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(double time) Return the numeraire at a given time index.
MonteCarloProcess	getProcess() Get the numerical scheme used to generate the stochastic process.
default RandomVariable	getRandomVariableForConstant(double value) Return a random variable initialized with a constant using the models random variable factory.
LocalDateTime	getReferenceDate() Returns the model's date corresponding to the time discretization's \( t = 0 \).
TimeDiscretization	getTimeDiscretization() Returns the time discretization of the model parameters.
void	setProcess(MonteCarloProcess process) Set the numerical scheme used to generate the stochastic process.

Method Detail

getReferenceDate

LocalDateTime getReferenceDate()

Returns the model's date corresponding to the time discretization's \( t = 0 \). Note: Currently not all models provide a reference date. This will change in future versions.

Returns:

The model's date corresponding to the time discretization's \( t = 0 \).

getTimeDiscretization

TimeDiscretization getTimeDiscretization()

Returns the time discretization of the model parameters. It is not necessary that this time discretization agrees with the discretization of the stochactic process used in Abstract Process implementation.

Returns:

The time discretization

getNumberOfComponents

int getNumberOfComponents()

Returns the number of components

Returns:

The number of components

applyStateSpaceTransform

RandomVariable applyStateSpaceTransform(int componentIndex,
                                        RandomVariable randomVariable)

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

Parameters:

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

default RandomVariable applyStateSpaceTransformInverse(int componentIndex,
                                                       RandomVariable randomVariable)

getInitialState

RandomVariable[] getInitialState()

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.

Returns:

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

getNumeraire

RandomVariable getNumeraire(double time)
                     throws CalculationException

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

Parameters:

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

Returns:

The numeraire at the specified time as RandomVariableFromDoubleArray

Throws:

CalculationException - Thrown if the valuation fails, specific cause may be available via the cause() method.

getDrift

RandomVariable[] getDrift(int timeIndex,
                          RandomVariable[] realizationAtTimeIndex,
                          RandomVariable[] realizationPredictor)

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:

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).

getNumberOfFactors

int getNumberOfFactors()

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

Returns:

The number of factors.

getFactorLoading

RandomVariable[] getFactorLoading(int timeIndex,
                                  int componentIndex,
                                  RandomVariable[] realizationAtTimeIndex)

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:

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

componentIndex - 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.

getRandomVariableForConstant

default RandomVariable getRandomVariableForConstant(double value)

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.

setProcess

void setProcess(MonteCarloProcess process)

Set the numerical scheme used to generate the stochastic process. The model needs the numerical scheme to calculate, e.g., the numeraire.

Parameters:

process - The process.

getProcess

MonteCarloProcess getProcess()

Get the numerical scheme used to generate the stochastic process. The model needs the numerical scheme to calculate, e.g., the numeraire.

Returns:

the process

getCloneWithModifiedData

ProcessModel getCloneWithModifiedData(Map<String,Object> dataModified)
                               throws CalculationException

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).

Throws:

CalculationException - Thrown when the model could not be created.