LabelCorrelation.java

package org.drip.measure.stochastic;

/*
 * -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
 */

/*!
 * Copyright (C) 2020 Lakshmi Krishnamurthy
 * Copyright (C) 2019 Lakshmi Krishnamurthy
 * Copyright (C) 2018 Lakshmi Krishnamurthy
 * 
 *  This file is part of DROP, an open-source library targeting analytics/risk, transaction cost analytics,
 *  	asset liability management analytics, capital, exposure, and margin analytics, valuation adjustment
 *  	analytics, and portfolio construction analytics within and across fixed income, credit, commodity,
 *  	equity, FX, and structured products. It also includes auxiliary libraries for algorithm support,
 *  	numerical analysis, numerical optimization, spline builder, model validation, statistical learning,
 *  	and computational support.
 *  
 *  	https://lakshmidrip.github.io/DROP/
 *  
 *  DROP is composed of three modules:
 *  
 *  - DROP Product Core - https://lakshmidrip.github.io/DROP-Product-Core/
 *  - DROP Portfolio Core - https://lakshmidrip.github.io/DROP-Portfolio-Core/
 *  - DROP Computational Core - https://lakshmidrip.github.io/DROP-Computational-Core/
 * 
 * 	DROP Product Core implements libraries for the following:
 * 	- Fixed Income Analytics
 * 	- Loan Analytics
 * 	- Transaction Cost Analytics
 * 
 * 	DROP Portfolio Core implements libraries for the following:
 * 	- Asset Allocation Analytics
 *  - Asset Liability Management Analytics
 * 	- Capital Estimation Analytics
 * 	- Exposure Analytics
 * 	- Margin Analytics
 * 	- XVA Analytics
 * 
 * 	DROP Computational Core implements libraries for the following:
 * 	- Algorithm Support
 * 	- Computation Support
 * 	- Function Analysis
 *  - Model Validation
 * 	- Numerical Analysis
 * 	- Numerical Optimizer
 * 	- Spline Builder
 *  - Statistical Learning
 * 
 * 	Documentation for DROP is Spread Over:
 * 
 * 	- Main                     => https://lakshmidrip.github.io/DROP/
 * 	- Wiki                     => https://github.com/lakshmiDRIP/DROP/wiki
 * 	- GitHub                   => https://github.com/lakshmiDRIP/DROP
 * 	- Repo Layout Taxonomy     => https://github.com/lakshmiDRIP/DROP/blob/master/Taxonomy.md
 * 	- Javadoc                  => https://lakshmidrip.github.io/DROP/Javadoc/index.html
 * 	- Technical Specifications => https://github.com/lakshmiDRIP/DROP/tree/master/Docs/Internal
 * 	- Release Versions         => https://lakshmidrip.github.io/DROP/version.html
 * 	- Community Credits        => https://lakshmidrip.github.io/DROP/credits.html
 * 	- Issues Catalog           => https://github.com/lakshmiDRIP/DROP/issues
 * 	- JUnit                    => https://lakshmidrip.github.io/DROP/junit/index.html
 * 	- Jacoco                   => https://lakshmidrip.github.io/DROP/jacoco/index.html
 * 
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *   	you may not use this file except in compliance with the License.
 *   
 *  You may obtain a copy of the License at
 *  	http://www.apache.org/licenses/LICENSE-2.0
 *  
 *  Unless required by applicable law or agreed to in writing, software
 *  	distributed under the License is distributed on an "AS IS" BASIS,
 *  	WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  
 *  See the License for the specific language governing permissions and
 *  	limitations under the License.
 */

/**
 * <i>LabelCorrelation</i> holds the Correlations between any Stochastic Variates identified by their Labels.
 * The References are:
 * 
 * <br><br>
 * 	<ul>
 * 		<li>
 *  		Andersen, L. B. G., M. Pykhtin, and A. Sokol (2017): Credit Exposure in the Presence of Initial
 *  			Margin https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2806156 <b>eSSRN</b>
 * 		</li>
 * 		<li>
 *  		Albanese, C., S. Caenazzo, and O. Frankel (2017): Regression Sensitivities for Initial Margin
 *  			Calculations https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2763488 <b>eSSRN</b>
 * 		</li>
 * 		<li>
 *  		Anfuso, F., D. Aziz, P. Giltinan, and K. Loukopoulus (2017): A Sound Modeling and Back-testing
 *  			Framework for Forecasting Initial Margin Requirements
 *  			https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2716279 <b>eSSRN</b>
 * 		</li>
 * 		<li>
 *  		Caspers, P., P. Giltinan, R. Lichters, and N. Nowaczyk (2017): Forecasting Initial Margin
 *  			Requirements - A Model Evaluation https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2911167
 *  			<b>eSSRN</b>
 * 		</li>
 * 		<li>
 *  		International Swaps and Derivatives Association (2017): SIMM v2.0 Methodology
 *  			https://www.isda.org/a/oFiDE/isda-simm-v2.pdf
 * 		</li>
 * 	</ul>
 *
 *	<br><br>
 *  <ul>
 *		<li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ComputationalCore.md">Computational Core Module</a></li>
 *		<li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/NumericalAnalysisLibrary.md">Numerical Analysis Library</a></li>
 *		<li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/measure/README.md">R<sup>d</sup> Continuous/Discrete Probability Measures</a></li>
 *		<li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/measure/stochastic/README.md">R<sup>1</sup> R<sup>1</sup> To R<sup>1</sup> Process</a></li>
 *  </ul>
 * 
 * @author Lakshmi Krishnamurthy
 */

public class LabelCorrelation extends org.drip.measure.stochastic.LabelBase
{
	protected double[][] _matrix = null;

	/**
	 * LabelCorrelation Constructor
	 * 
	 * @param labelList The List of Labels
	 * @param matrix The Correlation Matrix
	 * 
	 * @throws java.lang.Exception Thrown if the Inputs are Invalid
	 */

	public LabelCorrelation (
		final java.util.List<java.lang.String> labelList,
		final double[][] matrix)
		throws java.lang.Exception
	{
		super (labelList);

		if (null == (_matrix = matrix))
		{
			throw new java.lang.Exception ("LabelCorrelation Constructor => Invalid Inputs");
		}

		int labelCount = labelList.size();

		if (0 == labelCount || labelCount != _matrix.length)
		{
			throw new java.lang.Exception ("LabelCorrelation Constructor => Invalid Inputs");
		}

		for (int labelIndex = 0; labelIndex < labelCount; ++labelIndex)
		{
			if (null == _matrix[labelIndex] || labelCount != _matrix[labelIndex].length ||
				!org.drip.numerical.common.NumberUtil.IsValid (_matrix[labelIndex]))
			{
				throw new java.lang.Exception ("LabelCorrelation Constructor => Invalid Inputs");
			}
		}
	}

	/**
	 * Retrieve the Cross-Label Correlation Matrix
	 * 
	 * @return The Cross-Label Correlation Matrix
	 */

	public double[][] matrix()
	{
		return _matrix;
	}

	/**
	 * Retrieve the Correlation Entry for the Pair of Labels
	 * 
	 * @param label1 Label #1
	 * @param label2 Label #2
	 * 
	 * @return The Correlation Entry
	 * 
	 * @throws java.lang.Exception Thrown if the Inputs are Invalid
	 */

	public double entry (
		final java.lang.String label1,
		final java.lang.String label2)
		throws java.lang.Exception
	{
		if (null == label1 || !_labelList.contains (label1) ||
			null == label2 || !_labelList.contains (label2))
		{
			throw new java.lang.Exception ("LabelCorrelation::entry => Invalid Inputs");
		}

		return _matrix[_labelIndexMap.get (label1)][_labelIndexMap.get (label2)];
	}

	/**
	 * Generate the InterestRateTenorCorrelation Instance that corresponds to the Tenor sub-space
	 * 
	 * @param subTenorList The sub-Tenor List
	 * 
	 * @return The InterestRateTenorCorrelation Instance
	 */

	public LabelCorrelation subTenor (
		final java.util.List<java.lang.String> subTenorList)
	{
		if (null == subTenorList)
		{
			return null;
		}

		int subTenorSize = subTenorList.size();

		if (0 == subTenorSize)
		{
			return null;
		}

		double[][] subTenorMatrix = new double[subTenorSize][subTenorSize];

		for (int subTenorOuterIndex = 0; subTenorOuterIndex < subTenorSize; ++subTenorOuterIndex)
		{
			for (int subTenorInnerIndex = 0; subTenorInnerIndex < subTenorSize; ++subTenorInnerIndex)
			{
				try
				{
					subTenorMatrix[subTenorOuterIndex][subTenorInnerIndex] = entry (
						subTenorList.get (subTenorOuterIndex),
						subTenorList.get (subTenorInnerIndex)
					);
				}
				catch (java.lang.Exception e)
				{
					e.printStackTrace();

					return null;
				}
			}
		}

		try
		{
			return new LabelCorrelation (
				subTenorList,
				subTenorMatrix
			);
		}
		catch (java.lang.Exception e)
		{
			e.printStackTrace();
		}

		return null;
	}
}