RdWienerDriver.java

  1. package org.drip.dynamics.ito;

  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>RdWienerDriver</i> exposes the R<sup>d</sup> Wiener Background Emission Function. The References are:
  79.  *  
  80.  *  <br><br>
  81.  *  <ul>
  82.  *      <li>
  83.  *          Doob, J. L. (1942): The Brownian Movement and Stochastic Equations <i>Annals of Mathematics</i>
  84.  *              <b>43 (2)</b> 351-369
  85.  *      </li>
  86.  *      <li>
  87.  *          Gardiner, C. W. (2009): <i>Stochastic Methods: A Handbook for the Natural and Social Sciences
  88.  *              4<sup>th</sup> Edition</i> <b>Springer-Verlag</b>
  89.  *      </li>
  90.  *      <li>
  91.  *          Kadanoff, L. P. (2000): <i>Statistical Physics: Statics, Dynamics, and Re-normalization</i>
  92.  *              <b>World Scientific</b>
  93.  *      </li>
  94.  *      <li>
  95.  *          Karatzas, I., and S. E. Shreve (1991): <i>Brownian Motion and Stochastic Calculus 2<sup>nd</sup>
  96.  *              Edition</i> <b>Springer-Verlag</b>
  97.  *      </li>
  98.  *      <li>
  99.  *          Risken, H., and F. Till (1996): <i>The Fokker-Planck Equation – Methods of Solution and
  100.  *              Applications</i> <b>Springer</b>
  101.  *      </li>
  102.  *  </ul>
  103.  *
  104.  *  <br><br>
  105.  *  <ul>
  106.  *      <li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ProductCore.md">Product Core Module</a></li>
  107.  *      <li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/FixedIncomeAnalyticsLibrary.md">Fixed Income Analytics</a></li>
  108.  *      <li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/dynamics/README.md">HJM, Hull White, LMM, and SABR Dynamic Evolution Models</a></li>
  109.  *      <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/dynamics/ito/README.md">Ito Stochastic Process Dynamics Foundation</a></li>
  110.  *  </ul>
  111.  *
  112.  * @author Lakshmi Krishnamurthy
  113.  */

  115. public class RdWienerDriver
  116.     extends org.drip.dynamics.ito.RdStochasticDriver
  117. {
  118.     private double _timeWidthSQRT = java.lang.Double.NaN;
  119.     private org.drip.measure.gaussian.Covariance _correlation = null;

  121.     /**
  122.      * RdWienerDriver Constructor
  123.      * 
  124.      * @param timeWidth The Wiener Time Width
  125.      * @param correlation The Correlation
  126.      * 
  127.      * @throws java.lang.Exception Thrown if the Inputs are Invalid
  128.      */

  130.     public RdWienerDriver (
  131.         final double timeWidth,
  132.         final org.drip.measure.gaussian.Covariance correlation)
  133.         throws java.lang.Exception
  134.     {
  135.         if (!org.drip.numerical.common.NumberUtil.IsValid (
  136.                 timeWidth
  137.             ) || 0. >= timeWidth ||
  138.             null == (_correlation = correlation)
  139.         )
  140.         {
  141.             throw new java.lang.Exception (
  142.                 "RdWienerDriver Constructor => Invalid Inputs"
  143.             );
  144.         }

  146.         _timeWidthSQRT = java.lang.Math.sqrt (
  147.             timeWidth
  148.         );
  149.     }

  151.     /**
  152.      * Retrieve the Square Root of the Time Width
  153.      * 
  154.      * @return Square Root of the Time Width
  155.      */

  157.     public double timeWidthSQRT()
  158.     {
  159.         return _timeWidthSQRT;
  160.     }

  162.     /**
  163.      * Retrieve the Correlation
  164.      * 
  165.      * @return The Correlation
  166.      */

  168.     public org.drip.measure.gaussian.Covariance correlation()
  169.     {
  170.         return _correlation;
  171.     }

  173.     @Override public double[] emitSingle()
  174.     {
  175.         try
  176.         {
  177.             double[] singleCorrelatedSuite = new org.drip.measure.discrete.CorrelatedPathVertexDimension (
  178.                 new org.drip.measure.crng.RandomNumberGenerator(),
  179.                 _correlation.correlationMatrix(),
  180.                 1,
  181.                 1,
  182.                 false,
  183.                 null
  184.             ).straightVertexRealization();

  186.             if (null == singleCorrelatedSuite)
  187.             {
  188.                 return null;
  189.             }

  191.             int dimension = _correlation.numVariate();

  193.             for (int dimensionIndex = 0;
  194.                 dimensionIndex < dimension;
  195.                 ++dimensionIndex)
  196.             {
  197.                 singleCorrelatedSuite[dimensionIndex] =
  198.                     _timeWidthSQRT * singleCorrelatedSuite[dimensionIndex];
  199.             }

  201.             return singleCorrelatedSuite;
  202.         }
  203.         catch (java.lang.Exception e)
  204.         {
  205.             e.printStackTrace();
  206.         }

  208.         return null;
  209.     }
  210. }
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