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

  77. /**
  78.  * <i>LabelCovariance</i> holds the Covariance between any Stochastic Variates identified by their Labels, as
  79.  * well as their Means. The References are:
  80.  * 
  81.  * <br><br>
  82.  *  <ul>
  83.  *      <li>
  84.  *          Andersen, L. B. G., M. Pykhtin, and A. Sokol (2017): Credit Exposure in the Presence of Initial
  85.  *              Margin https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2806156 <b>eSSRN</b>
  86.  *      </li>
  87.  *      <li>
  88.  *          Albanese, C., S. Caenazzo, and O. Frankel (2017): Regression Sensitivities for Initial Margin
  89.  *              Calculations https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2763488 <b>eSSRN</b>
  90.  *      </li>
  91.  *      <li>
  92.  *          Anfuso, F., D. Aziz, P. Giltinan, and K. Loukopoulus (2017): A Sound Modeling and Back-testing
  93.  *              Framework for Forecasting Initial Margin Requirements
  94.  *              https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2716279 <b>eSSRN</b>
  95.  *      </li>
  96.  *      <li>
  97.  *          Caspers, P., P. Giltinan, R. Lichters, and N. Nowaczyk (2017): Forecasting Initial Margin
  98.  *              Requirements - A Model Evaluation https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2911167
  99.  *              <b>eSSRN</b>
  100.  *      </li>
  101.  *      <li>
  102.  *          International Swaps and Derivatives Association (2017): SIMM v2.0 Methodology
  103.  *              https://www.isda.org/a/oFiDE/isda-simm-v2.pdf
  104.  *      </li>
  105.  *  </ul>
  106.  *
  107.  *  <br><br>
  108.  *  <ul>
  109.  *      <li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ComputationalCore.md">Computational Core Module</a></li>
  110.  *      <li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/NumericalAnalysisLibrary.md">Numerical Analysis Library</a></li>
  111.  *      <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>
  112.  *      <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>
  113.  *  </ul>
  114.  * 
  115.  * @author Lakshmi Krishnamurthy
  116.  */

  118. public class LabelCovariance extends org.drip.measure.stochastic.LabelCorrelation
  119. {
  120.     private double[] _meanArray = null;
  121.     private double[] _volatilityArray = null;
  122.     private org.drip.measure.gaussian.Covariance _covariance = null;

  124.     /**
  125.      * LabelCovariance Constructor
  126.      * 
  127.      * @param labelList The List of Labels
  128.      * @param meanArray Array of Variate Means
  129.      * @param volatilityArray Array of Variate Volatilities
  130.      * @param correlationMatrix The Correlation Matrix
  131.      * 
  132.      * @throws java.lang.Exception Thrown if the Inputs are Invalid
  133.      */

  135.     public LabelCovariance (
  136.         final java.util.List<java.lang.String> labelList,
  137.         final double[] meanArray,
  138.         final double[] volatilityArray,
  139.         final double[][] correlationMatrix)
  140.         throws java.lang.Exception
  141.     {
  142.         super (
  143.             labelList,
  144.             correlationMatrix
  145.         );

  147.         if (null == (_meanArray = meanArray) ||
  148.             null == (_volatilityArray = volatilityArray))
  149.         {
  150.             throw new java.lang.Exception ("LabelCovariance Constructor => Invalid Inputs");
  151.         }

  153.         int variateCount = correlationMatrix.length;
  154.         double[][] covarianceMatrix = new double[variateCount][variateCount];

  156.         if (variateCount != _meanArray.length ||
  157.             variateCount != _volatilityArray.length)
  158.         {
  159.             throw new java.lang.Exception ("LabelCovariance Constructor => Invalid Inputs");
  160.         }

  162.         for (int variateIndexI = 0; variateIndexI < variateCount; ++variateIndexI)
  163.         {
  164.             if (!org.drip.numerical.common.NumberUtil.IsValid (_meanArray[variateIndexI]) ||
  165.                 !org.drip.numerical.common.NumberUtil.IsValid (_volatilityArray[variateIndexI]) ||
  166.                 0. > _volatilityArray[variateIndexI])
  167.             {
  168.                 throw new java.lang.Exception ("LabelCovariance Constructor => Invalid Inputs");
  169.             }

  171.             for (int variateIndexJ = 0; variateIndexJ < variateCount; ++variateIndexJ)
  172.             {
  173.                 covarianceMatrix[variateIndexI][variateIndexJ] =
  174.                     correlationMatrix[variateIndexI][variateIndexJ] * _volatilityArray[variateIndexI] *
  175.                     _volatilityArray[variateIndexJ];
  176.             }
  177.         }

  179.         _covariance = new org.drip.measure.gaussian.Covariance (covarianceMatrix);
  180.     }

  182.     /**
  183.      * Retrieve the Array of Variate Means
  184.      * 
  185.      * @return The Array of Variate Means
  186.      */

  188.     public double[] meanArray()
  189.     {
  190.         return _meanArray;
  191.     }

  193.     /**
  194.      * Retrieve the Array of Variate Volatilities
  195.      * 
  196.      * @return The Array of Variate Volatilities
  197.      */

  199.     public double[] volatilityArray()
  200.     {
  201.         return _volatilityArray;
  202.     }

  204.     /**
  205.      * Retrieve the Correlation Matrix
  206.      * 
  207.      * @return The Correlation Matrix
  208.      */

  210.     public double[][] correlationMatrix()
  211.     {
  212.         return _matrix;
  213.     }

  215.     /**
  216.      * Retrieve the Covariance Matrix
  217.      * 
  218.      * @return The Covariance Matrix
  219.      */

  221.     public double[][] covarianceMatrix()
  222.     {
  223.         return _covariance.covarianceMatrix();
  224.     }

  226.     /**
  227.      * Retrieve the Precision Matrix
  228.      * 
  229.      * @return The Precision Matrix
  230.      */

  232.     public double[][] precisionMatrix()
  233.     {
  234.         return _covariance.precisionMatrix();
  235.     }

  237.     /**
  238.      * Retrieve the Mean of the Latent State
  239.      * 
  240.      * @param label Latent State Label
  241.      * 
  242.      * @return Mean of the Latent State
  243.      * 
  244.      * @throws java.lang.Exception Thrown if the Inputs are Invalid
  245.      */

  247.     public double mean (
  248.         final java.lang.String label)
  249.         throws java.lang.Exception
  250.     {
  251.         if (null == label || !_labelList.contains (label))
  252.         {
  253.             throw new java.lang.Exception ("LabelCovariance::mean => Invalid Inputs");
  254.         }

  256.         return _meanArray[_labelIndexMap.get (label)];
  257.     }

  259.     /**
  260.      * Retrieve the Volatility of the Latent State
  261.      * 
  262.      * @param label Latent State Label
  263.      * 
  264.      * @return Volatility of the Latent State
  265.      * 
  266.      * @throws java.lang.Exception Thrown if the Inputs are Invalid
  267.      */

  269.     public double volatility (
  270.         final java.lang.String label)
  271.         throws java.lang.Exception
  272.     {
  273.         if (null == label || !_labelList.contains (label))
  274.         {
  275.             throw new java.lang.Exception ("LabelCovariance::volatility => Invalid Inputs");
  276.         }

  278.         return _volatilityArray[_labelIndexMap.get (label)];
  279.     }
  280. }
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