LabelCorrelation.java

  1. package org.drip.measure.stochastic;

  3. /*
  4.  * -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
  5.  */

  7. /*!
  8.  * Copyright (C) 2020 Lakshmi Krishnamurthy
  9.  * Copyright (C) 2019 Lakshmi Krishnamurthy
  10.  * Copyright (C) 2018 Lakshmi Krishnamurthy
  11.  * 
  12.  *  This file is part of DROP, an open-source library targeting analytics/risk, transaction cost analytics,
  13.  *      asset liability management analytics, capital, exposure, and margin analytics, valuation adjustment
  14.  *      analytics, and portfolio construction analytics within and across fixed income, credit, commodity,
  15.  *      equity, FX, and structured products. It also includes auxiliary libraries for algorithm support,
  16.  *      numerical analysis, numerical optimization, spline builder, model validation, statistical learning,
  17.  *      and computational support.
  18.  *  
  19.  *      https://lakshmidrip.github.io/DROP/
  20.  *  
  21.  *  DROP is composed of three modules:
  22.  *  
  23.  *  - DROP Product Core - https://lakshmidrip.github.io/DROP-Product-Core/
  24.  *  - DROP Portfolio Core - https://lakshmidrip.github.io/DROP-Portfolio-Core/
  25.  *  - DROP Computational Core - https://lakshmidrip.github.io/DROP-Computational-Core/
  26.  * 
  27.  *  DROP Product Core implements libraries for the following:
  28.  *  - Fixed Income Analytics
  29.  *  - Loan Analytics
  30.  *  - Transaction Cost Analytics
  31.  * 
  32.  *  DROP Portfolio Core implements libraries for the following:
  33.  *  - Asset Allocation Analytics
  34.  *  - Asset Liability Management Analytics
  35.  *  - Capital Estimation Analytics
  36.  *  - Exposure Analytics
  37.  *  - Margin Analytics
  38.  *  - XVA Analytics
  39.  * 
  40.  *  DROP Computational Core implements libraries for the following:
  41.  *  - Algorithm Support
  42.  *  - Computation Support
  43.  *  - Function Analysis
  44.  *  - Model Validation
  45.  *  - Numerical Analysis
  46.  *  - Numerical Optimizer
  47.  *  - Spline Builder
  48.  *  - Statistical Learning
  49.  * 
  50.  *  Documentation for DROP is Spread Over:
  51.  * 
  52.  *  - Main                     => https://lakshmidrip.github.io/DROP/
  53.  *  - Wiki                     => https://github.com/lakshmiDRIP/DROP/wiki
  54.  *  - GitHub                   => https://github.com/lakshmiDRIP/DROP
  55.  *  - Repo Layout Taxonomy     => https://github.com/lakshmiDRIP/DROP/blob/master/Taxonomy.md
  56.  *  - Javadoc                  => https://lakshmidrip.github.io/DROP/Javadoc/index.html
  57.  *  - Technical Specifications => https://github.com/lakshmiDRIP/DROP/tree/master/Docs/Internal
  58.  *  - Release Versions         => https://lakshmidrip.github.io/DROP/version.html
  59.  *  - Community Credits        => https://lakshmidrip.github.io/DROP/credits.html
  60.  *  - Issues Catalog           => https://github.com/lakshmiDRIP/DROP/issues
  61.  *  - JUnit                    => https://lakshmidrip.github.io/DROP/junit/index.html
  62.  *  - Jacoco                   => https://lakshmidrip.github.io/DROP/jacoco/index.html
  63.  * 
  64.  *  Licensed under the Apache License, Version 2.0 (the "License");
  65.  *      you may not use this file except in compliance with the License.
  66.  *   
  67.  *  You may obtain a copy of the License at
  68.  *      http://www.apache.org/licenses/LICENSE-2.0
  69.  *  
  70.  *  Unless required by applicable law or agreed to in writing, software
  71.  *      distributed under the License is distributed on an "AS IS" BASIS,
  72.  *      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  73.  *  
  74.  *  See the License for the specific language governing permissions and
  75.  *      limitations under the License.
  76.  */

  78. /**
  79.  * <i>LabelCorrelation</i> holds the Correlations between any Stochastic Variates identified by their Labels.
  80.  * The References are:
  81.  * 
  82.  * <br><br>
  83.  *  <ul>
  84.  *      <li>
  85.  *          Andersen, L. B. G., M. Pykhtin, and A. Sokol (2017): Credit Exposure in the Presence of Initial
  86.  *              Margin https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2806156 <b>eSSRN</b>
  87.  *      </li>
  88.  *      <li>
  89.  *          Albanese, C., S. Caenazzo, and O. Frankel (2017): Regression Sensitivities for Initial Margin
  90.  *              Calculations https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2763488 <b>eSSRN</b>
  91.  *      </li>
  92.  *      <li>
  93.  *          Anfuso, F., D. Aziz, P. Giltinan, and K. Loukopoulus (2017): A Sound Modeling and Back-testing
  94.  *              Framework for Forecasting Initial Margin Requirements
  95.  *              https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2716279 <b>eSSRN</b>
  96.  *      </li>
  97.  *      <li>
  98.  *          Caspers, P., P. Giltinan, R. Lichters, and N. Nowaczyk (2017): Forecasting Initial Margin
  99.  *              Requirements - A Model Evaluation https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2911167
  100.  *              <b>eSSRN</b>
  101.  *      </li>
  102.  *      <li>
  103.  *          International Swaps and Derivatives Association (2017): SIMM v2.0 Methodology
  104.  *              https://www.isda.org/a/oFiDE/isda-simm-v2.pdf
  105.  *      </li>
  106.  *  </ul>
  107.  *
  108.  *  <br><br>
  109.  *  <ul>
  110.  *      <li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ComputationalCore.md">Computational Core Module</a></li>
  111.  *      <li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/NumericalAnalysisLibrary.md">Numerical Analysis Library</a></li>
  112.  *      <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>
  113.  *      <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>
  114.  *  </ul>
  115.  * 
  116.  * @author Lakshmi Krishnamurthy
  117.  */

  119. public class LabelCorrelation extends org.drip.measure.stochastic.LabelBase
  120. {
  121.     protected double[][] _matrix = null;

  123.     /**
  124.      * LabelCorrelation Constructor
  125.      * 
  126.      * @param labelList The List of Labels
  127.      * @param matrix The Correlation Matrix
  128.      * 
  129.      * @throws java.lang.Exception Thrown if the Inputs are Invalid
  130.      */

  132.     public LabelCorrelation (
  133.         final java.util.List<java.lang.String> labelList,
  134.         final double[][] matrix)
  135.         throws java.lang.Exception
  136.     {
  137.         super (labelList);

  139.         if (null == (_matrix = matrix))
  140.         {
  141.             throw new java.lang.Exception ("LabelCorrelation Constructor => Invalid Inputs");
  142.         }

  144.         int labelCount = labelList.size();

  146.         if (0 == labelCount || labelCount != _matrix.length)
  147.         {
  148.             throw new java.lang.Exception ("LabelCorrelation Constructor => Invalid Inputs");
  149.         }

  151.         for (int labelIndex = 0; labelIndex < labelCount; ++labelIndex)
  152.         {
  153.             if (null == _matrix[labelIndex] || labelCount != _matrix[labelIndex].length ||
  154.                 !org.drip.numerical.common.NumberUtil.IsValid (_matrix[labelIndex]))
  155.             {
  156.                 throw new java.lang.Exception ("LabelCorrelation Constructor => Invalid Inputs");
  157.             }
  158.         }
  159.     }

  161.     /**
  162.      * Retrieve the Cross-Label Correlation Matrix
  163.      * 
  164.      * @return The Cross-Label Correlation Matrix
  165.      */

  167.     public double[][] matrix()
  168.     {
  169.         return _matrix;
  170.     }

  172.     /**
  173.      * Retrieve the Correlation Entry for the Pair of Labels
  174.      * 
  175.      * @param label1 Label #1
  176.      * @param label2 Label #2
  177.      * 
  178.      * @return The Correlation Entry
  179.      * 
  180.      * @throws java.lang.Exception Thrown if the Inputs are Invalid
  181.      */

  183.     public double entry (
  184.         final java.lang.String label1,
  185.         final java.lang.String label2)
  186.         throws java.lang.Exception
  187.     {
  188.         if (null == label1 || !_labelList.contains (label1) ||
  189.             null == label2 || !_labelList.contains (label2))
  190.         {
  191.             throw new java.lang.Exception ("LabelCorrelation::entry => Invalid Inputs");
  192.         }

  194.         return _matrix[_labelIndexMap.get (label1)][_labelIndexMap.get (label2)];
  195.     }

  197.     /**
  198.      * Generate the InterestRateTenorCorrelation Instance that corresponds to the Tenor sub-space
  199.      * 
  200.      * @param subTenorList The sub-Tenor List
  201.      * 
  202.      * @return The InterestRateTenorCorrelation Instance
  203.      */

  205.     public LabelCorrelation subTenor (
  206.         final java.util.List<java.lang.String> subTenorList)
  207.     {
  208.         if (null == subTenorList)
  209.         {
  210.             return null;
  211.         }

  213.         int subTenorSize = subTenorList.size();

  215.         if (0 == subTenorSize)
  216.         {
  217.             return null;
  218.         }

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

  222.         for (int subTenorOuterIndex = 0; subTenorOuterIndex < subTenorSize; ++subTenorOuterIndex)
  223.         {
  224.             for (int subTenorInnerIndex = 0; subTenorInnerIndex < subTenorSize; ++subTenorInnerIndex)
  225.             {
  226.                 try
  227.                 {
  228.                     subTenorMatrix[subTenorOuterIndex][subTenorInnerIndex] = entry (
  229.                         subTenorList.get (subTenorOuterIndex),
  230.                         subTenorList.get (subTenorInnerIndex)
  231.                     );
  232.                 }
  233.                 catch (java.lang.Exception e)
  234.                 {
  235.                     e.printStackTrace();

  237.                     return null;
  238.                 }
  239.             }
  240.         }

  242.         try
  243.         {
  244.             return new LabelCorrelation (
  245.                 subTenorList,
  246.                 subTenorMatrix
  247.             );
  248.         }
  249.         catch (java.lang.Exception e)
  250.         {
  251.             e.printStackTrace();
  252.         }

  254.         return null;
  255.     }
  256. }
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