Module net.finmath.lib

Package net.finmath.montecarlo.conditionalexpectation

Class MonteCarloConditionalExpectationRegression

java.lang.Object

net.finmath.montecarlo.conditionalexpectation.MonteCarloConditionalExpectationRegression

All Implemented Interfaces:

ConditionalExpectationEstimator

Direct Known Subclasses:

MonteCarloConditionalExpectationRegressionLocalizedOnDependents

public class MonteCarloConditionalExpectationRegression
extends Object
implements ConditionalExpectationEstimator

A service that allows to estimate conditional expectation via regression. 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

Modifier and Type	Class	Description
static interface	MonteCarloConditionalExpectationRegression.RegressionBasisFunctions	Interface for objects specifying regression basis functions (a vector of random variables).
static class	MonteCarloConditionalExpectationRegression.RegressionBasisFunctionsGiven	Wrapper to an array of RandomVariable[] implementing RegressionBasisFunctions

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method	Description
MonteCarloConditionalExpectationRegression.RegressionBasisFunctions	getBasisFunctionsEstimator()
MonteCarloConditionalExpectationRegression.RegressionBasisFunctions	getBasisFunctionsPredictor()
RandomVariable	getConditionalExpectation(RandomVariable randomVariable)	Return the conditional expectation of a given random variable.
double[]	getLinearRegressionParameters(RandomVariable dependents)	Return the solution x of XTX x = XT y for a given y.

Methods inherited from class java.lang.Object

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

Constructor Detail

MonteCarloConditionalExpectationRegression

public MonteCarloConditionalExpectationRegression()

MonteCarloConditionalExpectationRegression

public MonteCarloConditionalExpectationRegression(RandomVariable[] basisFunctions)

Creates a class for conditional expectation estimation.

Parameters:

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

MonteCarloConditionalExpectationRegression

public MonteCarloConditionalExpectationRegression(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 Detail

getConditionalExpectation

public RandomVariable getConditionalExpectation(RandomVariable randomVariable)

Description copied from interface: ConditionalExpectationEstimator

Return the conditional expectation of a given random variable. The definition of the filtration time is part of the object implementing this interface.

Specified by:

getConditionalExpectation in interface ConditionalExpectationEstimator

Parameters:

randomVariable - Given random variable.

Returns:

The conditional expectation of randomVariable.

getLinearRegressionParameters

public double[] getLinearRegressionParameters(RandomVariable dependents)

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

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.

getBasisFunctionsEstimator

public MonteCarloConditionalExpectationRegression.RegressionBasisFunctions getBasisFunctionsEstimator()

getBasisFunctionsPredictor

public MonteCarloConditionalExpectationRegression.RegressionBasisFunctions getBasisFunctionsPredictor()