Module net.finmath.lib

Package net.finmath.montecarlo.conditionalexpectation

Class MonteCarloConditionalExpectationRegressionLocalizedOnDependents

java.lang.Object

net.finmath.montecarlo.conditionalexpectation.MonteCarloConditionalExpectationRegression

net.finmath.montecarlo.conditionalexpectation.MonteCarloConditionalExpectationRegressionLocalizedOnDependents

All Implemented Interfaces:

ConditionalExpectationEstimator

public class MonteCarloConditionalExpectationRegressionLocalizedOnDependents
extends MonteCarloConditionalExpectationRegression

A service that allows to estimate conditional expectation via regression. This implementation uses a localization weight derived from the dependent variable. The regression only considers sample paths where \( - M < y_{i} < M \) where M is a multiple of the standard deviation of y. In oder to estimate the conditional expectation, basis functions have to be specified. The class can either estimate and predict the conditional expectation within the same simulation (which will eventually introduce a small foresight bias) or use a different simulation for estimation (using basisFunctionsEstimator) to predict conditional expectation within another simulation (using basisFunctionsPredictor). In the latter case, the basis functions have to correspond to the same entities, however, generated in different simulations (number of path, etc., may be different).

Version:

1.0

Author:

Christian Fries

Nested Class Summary

Nested classes/interfaces inherited from class net.finmath.montecarlo.conditionalexpectation.MonteCarloConditionalExpectationRegression

MonteCarloConditionalExpectationRegression.RegressionBasisFunctions, MonteCarloConditionalExpectationRegression.RegressionBasisFunctionsGiven

Constructor Summary

Constructors

Constructor	Description
MonteCarloConditionalExpectationRegressionLocalizedOnDependents()
MonteCarloConditionalExpectationRegressionLocalizedOnDependents(RandomVariable[] basisFunctions)	Creates a class for conditional expectation estimation.
MonteCarloConditionalExpectationRegressionLocalizedOnDependents(RandomVariable[] basisFunctionsEstimator, double standardDeviations)	Creates a class for conditional expectation estimation.
MonteCarloConditionalExpectationRegressionLocalizedOnDependents(RandomVariable[] basisFunctionsEstimator, RandomVariable[] basisFunctionsPredictor)	Creates a class for conditional expectation estimation.
MonteCarloConditionalExpectationRegressionLocalizedOnDependents(RandomVariable[] basisFunctionsEstimator, RandomVariable[] basisFunctionsPredictor, double standardDeviations)	Creates a class for conditional expectation estimation.

Method Summary

Modifier and Type	Method	Description
double[]	getLinearRegressionParameters(RandomVariable dependents)	Return the solution x of XTX x = XT y for a given y.

Methods inherited from class net.finmath.montecarlo.conditionalexpectation.MonteCarloConditionalExpectationRegression

getBasisFunctionsEstimator, getBasisFunctionsPredictor, getConditionalExpectation

Methods inherited from class java.lang.Object

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

Constructor Details

MonteCarloConditionalExpectationRegressionLocalizedOnDependents

public MonteCarloConditionalExpectationRegressionLocalizedOnDependents(RandomVariable[] basisFunctionsEstimator, RandomVariable[] basisFunctionsPredictor, double standardDeviations)

Creates a class for conditional expectation estimation.

Parameters:

basisFunctionsEstimator - A vector of random variables to be used as basis functions for estimation.

basisFunctionsPredictor - A vector of random variables to be used as basis functions for prediction.

standardDeviations - A standard deviation parameter for the weight function.

MonteCarloConditionalExpectationRegressionLocalizedOnDependents

public MonteCarloConditionalExpectationRegressionLocalizedOnDependents(RandomVariable[] basisFunctionsEstimator, double standardDeviations)

Creates a class for conditional expectation estimation.

Parameters:

basisFunctionsEstimator - A vector of random variables to be used as basis functions for estimation.

standardDeviations - A standard deviation parameter for the weight function.

MonteCarloConditionalExpectationRegressionLocalizedOnDependents

public MonteCarloConditionalExpectationRegressionLocalizedOnDependents()

MonteCarloConditionalExpectationRegressionLocalizedOnDependents

public MonteCarloConditionalExpectationRegressionLocalizedOnDependents(RandomVariable[] basisFunctions)

Creates a class for conditional expectation estimation.

Parameters:

basisFunctions - A vector of random variables to be used as basis functions.

MonteCarloConditionalExpectationRegressionLocalizedOnDependents

public MonteCarloConditionalExpectationRegressionLocalizedOnDependents(RandomVariable[] basisFunctionsEstimator, RandomVariable[] basisFunctionsPredictor)

Creates a class for conditional expectation estimation.

Parameters:

basisFunctionsEstimator - A vector of random variables to be used as basis functions for estimation.

basisFunctionsPredictor - A vector of random variables to be used as basis functions for prediction.

Method Details

getLinearRegressionParameters

public double[] getLinearRegressionParameters(RandomVariable dependents)

Return the solution x of XTX x = XT y for a given y.

Overrides:

getLinearRegressionParameters in class MonteCarloConditionalExpectationRegression

Parameters:

dependents - The sample vector of the random variable y.

Returns:

The solution x of XTX x = XT y.

To dos:

Performance upon repeated call can be optimized by caching XTX.