finMath lib documentation
net.finmath.montecarlo.assetderivativevaluation.models

## Class 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
MonteCarloMertonModel, The interface for numerical schemes., The interface for models provinding parameters to numerical schemes.
• ### Constructor Summary

Constructors
Constructor and Description
MertonModel(double initialValue, DiscountCurve discountCurveForForwardRate, double volatility, DiscountCurve discountCurveForDiscountRate, double jumpIntensity, double jumpSizeMean, double jumpSizeStDev)
Create a Merton model.
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(MertonModelDescriptor descriptor)
Create the model from a descriptor.
• ### Method Summary

All Methods
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.
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.
double getJumpIntensity()
double getJumpSizeMean()
double getJumpSizeStdDev()
int getNumberOfComponents()
Returns the number of components
RandomVariable getNumeraire(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.
double getRiskFreeRate()
double getVolatility()
• ### Methods inherited from class net.finmath.montecarlo.model.AbstractProcessModel

getInitialValue, getMonteCarloWeights, getNumberOfFactors, getProcess, getProcessValue, getReferenceDate, getTime, getTimeDiscretization, getTimeIndex, setProcess
• ### Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
• ### Constructor Detail

• #### 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

• #### getNumberOfComponents

public int getNumberOfComponents()
Description copied from interface: ProcessModel
Returns the number of components
Returns:
The number of components
• #### applyStateSpaceTransform

public RandomVariable applyStateSpaceTransform(int componentIndex,
RandomVariable randomVariable)
Description copied from interface: ProcessModel
Applies the state space transform fi to the given state random variable such that Yi → fi(Yi) =: Xi.
Parameters:
componentIndex - The component index i.
randomVariable - The state random variable Yi.
Returns:
New random variable holding the result of the state space transformation.
• #### applyStateSpaceTransformInverse

public RandomVariable applyStateSpaceTransformInverse(int componentIndex,
RandomVariable randomVariable)
• #### getInitialState

public RandomVariable[] getInitialState()
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.
Returns:
The initial value of the state variable of the process Y(t=0).
• #### getNumeraire

public RandomVariable getNumeraire(double time)
throws CalculationException
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:
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

public RandomVariable[] getDrift(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, ..., μn) such that X = f(Y) and
dYj = μj dt + λ1,j dW1 + ... + λm,j dWm
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).

public RandomVariable[] getFactorLoading(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
λj = (λ1,j, ..., λm,j) such that X = f(Y) and
dYj = μj dt + λ1,j dW1 + ... + λm,j dWm
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:
• #### 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 double getRiskFreeRate()
Returns:
the riskFreeRate
• #### getVolatility

public double getVolatility()
Returns:
the volatility
• #### getJumpIntensity

public double getJumpIntensity()
Returns:
the jumpIntensity
• #### getJumpSizeMean

public double getJumpSizeMean()
Returns:
the jumpSizeMean
• #### getJumpSizeStdDev

public double getJumpSizeStdDev()
Returns:
the jumpSizeStdDev