Module net.finmath.lib

Package net.finmath.marketdata.model.curves.locallinearregression

Class CurveEstimation

java.lang.Object

net.finmath.marketdata.model.curves.locallinearregression.CurveEstimation

public class CurveEstimation
extends Object

This class implements the method of local linear regression with discrete kernel function, see see https://ssrn.com/abstract=3073942 In particular it represents the implementation of proposition 2 and 3 of the paper. This class allows choosing between three different kernel functions, i.e. a normal, a Laplace or a Cauchy kernel. For the kernel types provided see CurveEstimation.Distribution. The resulting curve is piecewise linear. That means, only the knot points of the curve are computed in this algorithm. The final curve is then provided with linear interpolation of the knot points, see CurveInterpolation.

Version:

1.0

Author:

Moritz Scherrmann, Christian Fries

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	CurveEstimation.Distribution	Possible kernel types.

Constructor Summary

Constructors

Constructor	Description
CurveEstimation(LocalDate referenceDate, double bandwidth, double[] independentValues, double[] dependentValues, double[] partitionValues, double weight)	Creates a curve estimation object with a normal kernel.
CurveEstimation(LocalDate referenceDate, double bandwidth, double[] independentValues, double[] dependentValues, double[] partitionValues, double weight, CurveEstimation.Distribution distribution)	Creates a curve estimation object.

Method Summary

Modifier and Type	Method	Description
Curve	getRegressionCurve()	Returns the curve resulting from the local linear regression with discrete kernel.

Methods inherited from class java.lang.Object

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

Constructor Details

CurveEstimation

public CurveEstimation(LocalDate referenceDate,
 double bandwidth,
 double[] independentValues,
 double[] dependentValues,
 double[] partitionValues,
 double weight,
 CurveEstimation.Distribution distribution)

Creates a curve estimation object.

Parameters:

referenceDate - The reference date for the resulting regression curve, i.e., the date which defined t=0.

bandwidth - The bandwidth parameter of the regression.

independentValues - The realization of a random variable X.

dependentValues - The realization of a random variable Y.

partitionValues - The values to create a partition. It is important that min(partition) ≤ min(X) and max(partition) ≥ max(X).

weight - The weight needed to create a partition.

distribution - The kernel type.

CurveEstimation

public CurveEstimation(LocalDate referenceDate,
 double bandwidth,
 double[] independentValues,
 double[] dependentValues,
 double[] partitionValues,
 double weight)

Creates a curve estimation object with a normal kernel.

Parameters:

referenceDate - The reference date for the resulting regression curve, i.e., the date which defined t=0.

bandwidth - The bandwidth parameter of the regression.

independentValues - The realization of a random variable X.

dependentValues - The realization of a random variable Y.

partitionValues - The values to create a partition. It is important that min(partition) ≤ min(X) and max(partition) ≥ max(X).

weight - The weight needed to create a partition.

Method Details

getRegressionCurve

public Curve getRegressionCurve()

Returns the curve resulting from the local linear regression with discrete kernel.

Returns:

The regression curve.