net.finmath.montecarlo.conditionalexpectation

Class MonteCarloConditionalExpectationRegression

java.lang.Object

net.finmath.montecarlo.conditionalexpectation.MonteCarloConditionalExpectationRegression

All Implemented Interfaces:

MonteCarloConditionalExpectation

public class MonteCarloConditionalExpectationRegression
extends Object
implements MonteCarloConditionalExpectation

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).

Author:

Christian Fries

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method and Description
RandomVariableInterface	getConditionalExpectation(RandomVariableInterface randomVariable) Return the conditional expectation of a given random variable.
double[]	getLinearRegressionParameters(RandomVariableInterface 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(RandomVariableInterface[] basisFunctions)

Creates a class for conditional expectation estimation.

Parameters:

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

MonteCarloConditionalExpectationRegression

public MonteCarloConditionalExpectationRegression(RandomVariableInterface[] basisFunctionsEstimator,
                                                  RandomVariableInterface[] 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 RandomVariableInterface getConditionalExpectation(RandomVariableInterface randomVariable)

Description copied from interface: MonteCarloConditionalExpectation

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 MonteCarloConditionalExpectation

Parameters:

randomVariable - Given random variable.

Returns:

The conditional expectation of randomVariable.

getLinearRegressionParameters

public double[] getLinearRegressionParameters(RandomVariableInterface 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.