Module net.finmath.lib

Package net.finmath.montecarlo.assetderivativevaluation.models

Class MertonModel

java.lang.Object

net.finmath.montecarlo.model.AbstractProcessModel

net.finmath.montecarlo.assetderivativevaluation.models.MertonModel

All Implemented Interfaces:

ProcessModel

public class MertonModel
extends AbstractProcessModel

This class implements a Merton 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 \[ dS = \mu S dt + \sigma S dW + S dJ, \quad S(0) = S_{0}, \] \[ dN = r N dt, \quad N(0) = N_{0}, \] where \( W \) is Brownian motion and \( J \) is a jump process (compound Poisson process). The process \( J \) is given by \( J(t) = \sum_{i=1}^{N(t)} (Y_{i}-1) \), where \( \log(Y_{i}) \) are i.i.d. normals with mean \( a - \frac{1}{2} b^{2} \) and standard deviation \( b \). Here \( a \) is the jump size mean and \( b \) is the jump size std. dev. The model can be rewritten as \( S = \exp(X) \), where \[ dX = \mu dt + \sigma dW + dJ^{X}, \quad X(0) = \log(S_{0}), \] with \[ J^{X}(t) = \sum_{i=1}^{N(t)} \log(Y_{i}) \] with \( \mu = r - \frac{1}{2} \sigma^2 - (exp(a)-1) \lambda \). The class provides the model of S to an MonteCarloProcess via the specification of \( f = exp \), \( \mu = r - \frac{1}{2} \sigma^2 - (exp(a)-1) \lambda \), \( \lambda_{1,1} = \sigma, \lambda_{1,2} = a - \frac{1}{2} b^2, \lambda_{1,3} = b \), i.e., of the SDE \[ dX = \mu dt + \lambda_{1,1} dW + \lambda_{1,2} dN + \lambda_{1,3} Z dN, \quad X(0) = \log(S_{0}), \] with \( S = f(X) \). See MonteCarloProcess for the notation. For an example on the construction of the three factors \( dW \), \( dN \), and \( Z dN \) see MonteCarloMertonModel.

Version:

1.0

Author:

Christian Fries

See Also:

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

Constructor Summary

Constructors

Constructor	Description
MertonModel(double initialValue, double riskFreeRate, double volatility, double jumpIntensity, double jumpSizeMean, double jumpSizeStDev)	Create a Merton model.
MertonModel(double initialValue, double riskFreeRate, double volatility, double discountRate, double jumpIntensity, double jumpSizeMean, double jumpSizeStDev)	Create a Merton model.
MertonModel(double initialValue, double riskFreeRate, double volatility, double discountRate, double jumpIntensity, double jumpSizeMean, double jumpSizeStDev, RandomVariableFactory randomVariableFactory)	Create a Merton model.
MertonModel(double initialValue, DiscountCurve discountCurveForForwardRate, double volatility, DiscountCurve discountCurveForDiscountRate, double jumpIntensity, double jumpSizeMean, double jumpSizeStDev)	Create a Merton model.
MertonModel(double initialValue, DiscountCurve discountCurveForForwardRate, double volatility, DiscountCurve discountCurveForDiscountRate, double jumpIntensity, double jumpSizeMean, double jumpSizeStDev, RandomVariableFactory randomVariableFactory)	Create a Merton model.
MertonModel(MertonModelDescriptor descriptor)	Create the model from a descriptor.
MertonModel(RandomVariable initialValue, DiscountCurve discountCurveForForwardRate, RandomVariable volatility, DiscountCurve discountCurveForDiscountRate, RandomVariable jumpIntensity, RandomVariable jumpSizeMean, RandomVariable jumpSizeStDev, RandomVariableFactory randomVariableFactory)	Create a Merton model.
MertonModel(RandomVariable initialValue, RandomVariable riskFreeRate, RandomVariable volatility, RandomVariable discountRate, RandomVariable jumpIntensity, RandomVariable jumpSizeMean, RandomVariable jumpSizeStDev, RandomVariableFactory randomVariableFactory)	Create a Merton 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.
ProcessModel	getCloneWithModifiedData(Map<String,Object> dataModified)	Returns a clone of this model where the specified properties have been modified.
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 componentIndex, 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	getJumpIntensity()
RandomVariable	getJumpSizeMean()
RandomVariable	getJumpSizeStdDev()
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.
RandomVariable	getRandomVariableForConstant(double value)	Return a random variable initialized with a constant using the models random variable factory.
RandomVariable	getRiskFreeRate()
RandomVariable	getVolatility()

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, toString, wait, wait, wait

Constructor Detail

MertonModel

public MertonModel(RandomVariable initialValue,
                   DiscountCurve discountCurveForForwardRate,
                   RandomVariable volatility,
                   DiscountCurve discountCurveForDiscountRate,
                   RandomVariable jumpIntensity,
                   RandomVariable jumpSizeMean,
                   RandomVariable jumpSizeStDev,
                   RandomVariableFactory randomVariableFactory)

Create a Merton model.

Parameters:

initialValue - \( S_{0} \) - spot - initial value of S

discountCurveForForwardRate - The curve specifying \( t \mapsto exp(- r^{\text{c}}(t) \cdot t) \) - with \( r^{\text{c}}(t) \) the risk free rate

volatility - The log volatility.

discountCurveForDiscountRate - The curve specifying \( t \mapsto exp(- r^{\text{d}}(t) \cdot t) \) - with \( r^{\text{d}}(t) \) the discount rate

jumpIntensity - The intensity parameter lambda of the compound Poisson process.

jumpSizeMean - The mean jump size of the normal distributes jump sizes of the compound Poisson process.

jumpSizeStDev - The standard deviation of the normal distributes jump sizes of the compound Poisson process.

randomVariableFactory - The factory to be used to construct random variables.

MertonModel

public MertonModel(double initialValue,
                   DiscountCurve discountCurveForForwardRate,
                   double volatility,
                   DiscountCurve discountCurveForDiscountRate,
                   double jumpIntensity,
                   double jumpSizeMean,
                   double jumpSizeStDev,
                   RandomVariableFactory randomVariableFactory)

Create a Merton model.

Parameters:

initialValue - \( S_{0} \) - spot - initial value of S

discountCurveForForwardRate - The curve specifying \( t \mapsto exp(- r^{\text{c}}(t) \cdot t) \) - with \( r^{\text{c}}(t) \) the risk free rate

volatility - The log volatility.

discountCurveForDiscountRate - The curve specifying \( t \mapsto exp(- r^{\text{d}}(t) \cdot t) \) - with \( r^{\text{d}}(t) \) the discount rate

jumpIntensity - The intensity parameter lambda of the compound Poisson process.

jumpSizeMean - The mean jump size of the normal distributes jump sizes of the compound Poisson process.

jumpSizeStDev - The standard deviation of the normal distributes jump sizes of the compound Poisson process.

randomVariableFactory - The factory to be used to construct random variables.

MertonModel

public MertonModel(RandomVariable initialValue,
                   RandomVariable riskFreeRate,
                   RandomVariable volatility,
                   RandomVariable discountRate,
                   RandomVariable jumpIntensity,
                   RandomVariable jumpSizeMean,
                   RandomVariable jumpSizeStDev,
                   RandomVariableFactory randomVariableFactory)

Create a Merton model.

Parameters:

initialValue - Spot value.

riskFreeRate - The risk free rate.

volatility - The log volatility.

discountRate - The discount rate used in the numeraire.

jumpIntensity - The intensity parameter lambda of the compound Poisson process.

jumpSizeMean - The mean jump size of the normal distributes jump sizes of the compound Poisson process.

jumpSizeStDev - The standard deviation of the normal distributes jump sizes of the compound Poisson process.

randomVariableFactory - The factory to be used to construct random variables.

MertonModel

public MertonModel(double initialValue,
                   double riskFreeRate,
                   double volatility,
                   double discountRate,
                   double jumpIntensity,
                   double jumpSizeMean,
                   double jumpSizeStDev,
                   RandomVariableFactory randomVariableFactory)

Create a Merton model.

Parameters:

initialValue - Spot value.

riskFreeRate - The risk free rate.

volatility - The log volatility.

discountRate - The discount rate used in the numeraire.

jumpIntensity - The intensity parameter lambda of the compound Poisson process.

jumpSizeMean - The mean jump size of the normal distributes jump sizes of the compound Poisson process.

jumpSizeStDev - The standard deviation of the normal distributes jump sizes of the compound Poisson process.

randomVariableFactory - The factory to be used to construct random variables.

MertonModel

public MertonModel(MertonModelDescriptor descriptor)

Create the model from a descriptor.

Parameters:

descriptor - A descriptor of the model.

MertonModel

public MertonModel(double initialValue,
                   DiscountCurve discountCurveForForwardRate,
                   double volatility,
                   DiscountCurve discountCurveForDiscountRate,
                   double jumpIntensity,
                   double jumpSizeMean,
                   double jumpSizeStDev)

Create a Merton model.

Parameters:

initialValue - \( S_{0} \) - spot - initial value of S

discountCurveForForwardRate - The curve specifying \( t \mapsto exp(- r^{\text{c}}(t) \cdot t) \) - with \( r^{\text{c}}(t) \) the risk free rate

volatility - The log volatility.

discountCurveForDiscountRate - The curve specifying \( t \mapsto exp(- r^{\text{d}}(t) \cdot t) \) - with \( r^{\text{d}}(t) \) the discount rate

jumpIntensity - The intensity parameter lambda of the compound Poisson process.

jumpSizeMean - The mean jump size of the normal distributes jump sizes of the compound Poisson process.

jumpSizeStDev - The standard deviation of the normal distributes jump sizes of the compound Poisson process.

MertonModel

public MertonModel(double initialValue,
                   double riskFreeRate,
                   double volatility,
                   double discountRate,
                   double jumpIntensity,
                   double jumpSizeMean,
                   double jumpSizeStDev)

Create a Merton model.

Parameters:

initialValue - Spot value.

riskFreeRate - The risk free rate.

volatility - The log volatility.

discountRate - The discount rate used in the numeraire.

jumpIntensity - The intensity parameter lambda of the compound Poisson process.

jumpSizeMean - The mean jump size of the normal distributes jump sizes of the compound Poisson process.

jumpSizeStDev - The standard deviation of the normal distributes jump sizes of the compound Poisson process.

MertonModel

public MertonModel(double initialValue,
                   double riskFreeRate,
                   double volatility,
                   double jumpIntensity,
                   double jumpSizeMean,
                   double jumpSizeStDev)

Create a Merton model.

Parameters:

initialValue - Spot value.

riskFreeRate - The risk free rate.

volatility - The log volatility.

jumpIntensity - The intensity parameter lambda of the compound Poisson process.

jumpSizeMean - The mean jump size of the normal distributes jump sizes of the compound Poisson process.

jumpSizeStDev - The standard deviation of the normal distributes jump sizes of the compound Poisson process.

Method Detail

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.

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).

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

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 componentIndex,
                                         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).

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.

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 ProcessModel 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).

getRiskFreeRate

public RandomVariable getRiskFreeRate()

Returns:

the riskFreeRate

getVolatility

public RandomVariable getVolatility()

Returns:

the volatility

getJumpIntensity

public RandomVariable getJumpIntensity()

Returns:

the jumpIntensity

getJumpSizeMean

public RandomVariable getJumpSizeMean()

Returns:

the jumpSizeMean

getJumpSizeStdDev

public RandomVariable getJumpSizeStdDev()

Returns:

the jumpSizeStdDev