Module net.finmath.lib
Class DisplacedLognormalGARCH
java.lang.Object
net.finmath.timeseries.models.parametric.DisplacedLognormalGARCH
- All Implemented Interfaces:
HistoricalSimulationModel
Displaced log-normal process with GARCH(1,1) volatility.
This class estimate the process
\[
\mathrm{d} \log(X + a) = \frac{\sigma}{b + a} \mathrm{d}W(t)
\]
where \( a > -min(X(t_{i}) \) and thus \( X+a > 0 \) and \( b = 1 - -min(X(t_{i}) \) \) and
\( \sigma \) is given by a GARCH(1,1) process.
The choice of b ensures that b+a ≥ 1.
For a=0 we have a log-normal process with volatility σ/(b + a).
For a=infinity we have a normal process with volatility σ.
- Version:
- 1.0
- Author:
- Christian Fries
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Constructor Summary
ConstructorsConstructorDescriptionDisplacedLognormalGARCH(double[] values)
DisplacedLognormalGARCH(double[] values, double lowerBoundDisplacement)
DisplacedLognormalGARCH(double[] values, double lowerBoundDisplacement, int windowIndexStart, int windowIndexEnd)
DisplacedLognormalGARCH(double[] values, int windowIndexStart, int windowIndexEnd)
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Method Summary
Modifier and TypeMethodDescriptionReturns the parameters estimated for the given time series.getBestParameters(Map<String,Object> guess)
Returns the parameters estimated for the given time series, using a parameter guess.getCloneWithWindow(double lowerBoundDisplacement, int windowIndexStart, int windowIndexEnd)
getCloneWithWindow(int windowIndexStart, int windowIndexEnd)
Create a new model, using only a window of the times series.double
getLastResidualForParameters(double omega, double alpha, double beta, double displacement)
double
getLogLikelihoodForParameters(double omega, double alpha, double beta, double displacement)
double[]
getQuantilPredictionsForParameters(double omega, double alpha, double beta, double displacement, double[] quantiles)
double[]
getSzenarios(double omega, double alpha, double beta, double displacement)
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Constructor Details
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DisplacedLognormalGARCH
public DisplacedLognormalGARCH(double[] values) -
DisplacedLognormalGARCH
public DisplacedLognormalGARCH(double[] values, double lowerBoundDisplacement) -
DisplacedLognormalGARCH
public DisplacedLognormalGARCH(double[] values, int windowIndexStart, int windowIndexEnd) -
DisplacedLognormalGARCH
public DisplacedLognormalGARCH(double[] values, double lowerBoundDisplacement, int windowIndexStart, int windowIndexEnd)
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Method Details
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getCloneWithWindow
Description copied from interface:HistoricalSimulationModel
Create a new model, using only a window of the times series.- Specified by:
getCloneWithWindow
in interfaceHistoricalSimulationModel
- Parameters:
windowIndexStart
- Index of the first element to be part of the new time series.windowIndexEnd
- Index of the last element to be part of the new time series.- Returns:
- A new historical simulation using a different data window.
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getCloneWithWindow
public HistoricalSimulationModel getCloneWithWindow(double lowerBoundDisplacement, int windowIndexStart, int windowIndexEnd) -
getLogLikelihoodForParameters
public double getLogLikelihoodForParameters(double omega, double alpha, double beta, double displacement) -
getLastResidualForParameters
public double getLastResidualForParameters(double omega, double alpha, double beta, double displacement) -
getSzenarios
public double[] getSzenarios(double omega, double alpha, double beta, double displacement) -
getQuantilPredictionsForParameters
public double[] getQuantilPredictionsForParameters(double omega, double alpha, double beta, double displacement, double[] quantiles) -
getBestParameters
Description copied from interface:HistoricalSimulationModel
Returns the parameters estimated for the given time series.- Specified by:
getBestParameters
in interfaceHistoricalSimulationModel
- Returns:
- The parameters estimated for the given time series.
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getBestParameters
Description copied from interface:HistoricalSimulationModel
Returns the parameters estimated for the given time series, using a parameter guess.- Specified by:
getBestParameters
in interfaceHistoricalSimulationModel
- Parameters:
guess
- A parameter guess.- Returns:
- The parameters estimated for the given time series.
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