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finmath lib is a Mathematical Finance Library in Java. It provides algorithms and methodologies related to mathematical finance.

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/*
 * (c) Copyright Christian P. Fries, Germany. All rights reserved. Contact: [email protected].
 *
 * Created on 13.08.2004
 */
package net.finmath.montecarlo.conditionalexpectation;

import net.finmath.functions.LinearAlgebra;
import net.finmath.stochastic.RandomVariableInterface;

/**
 * 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
 */
public class MonteCarloConditionalExpectationRegression implements MonteCarloConditionalExpectation {

	private RandomVariableInterface[]    basisFunctionsEstimator		= null;
	private RandomVariableInterface[]    basisFunctionsPredictor		= null;
    
	/**
	 * Creates a class for conditional expectation estimation.
	 * 
	 * @param basisFunctions A vector of random variables to be used as basis functions.
	 */
	public MonteCarloConditionalExpectationRegression(RandomVariableInterface[] basisFunctions) {
		super();
		this.basisFunctionsEstimator = basisFunctions;
		this.basisFunctionsPredictor = basisFunctions;
	}

	/**
	 * Creates a class for conditional expectation estimation.
	 * 
	 * @param basisFunctionsEstimator A vector of random variables to be used as basis functions for estimation.
	 * @param basisFunctionsPredictor A vector of random variables to be used as basis functions for prediction.
	 */
	public MonteCarloConditionalExpectationRegression(RandomVariableInterface[] basisFunctionsEstimator, RandomVariableInterface[] basisFunctionsPredictor) {
		super();
		this.basisFunctionsEstimator = basisFunctionsEstimator;
		this.basisFunctionsPredictor = basisFunctionsPredictor;
	}

	@Override
	public RandomVariableInterface getConditionalExpectation(RandomVariableInterface randomVariable) {
		// Get regression parameters x as the solution of XTX x = XT y
		double[] linearRegressionParameters = getLinearRegressionParameters(randomVariable);

		// Calculate estimate, i.e. X x
		RandomVariableInterface[] basisFunctions = getNonZeroBasisFunctions(basisFunctionsPredictor);
		RandomVariableInterface conditionalExpectation = basisFunctions[0].mult(linearRegressionParameters[0]);
		for(int i=1; i