CVMCorrelationDiscriminatoryPowerAnalysis9a.java

  1. package org.drip.sample.anfuso2017;

  3. import java.util.ArrayList;
  4. import java.util.List;

  6. import org.drip.numerical.common.FormatUtil;
  7. import org.drip.service.env.EnvManager;
  8. import org.drip.state.identifier.EntityEquityLabel;
  9. import org.drip.state.identifier.FXLabel;
  10. import org.drip.validation.distance.GapLossWeightFunction;
  11. import org.drip.validation.distance.GapTestOutcome;
  12. import org.drip.validation.distance.GapTestSetting;
  13. import org.drip.validation.evidence.Ensemble;
  14. import org.drip.validation.evidence.Sample;
  15. import org.drip.validation.evidence.TestStatisticEvaluator;
  16. import org.drip.validation.riskfactorjoint.NormalSampleCohort;
  17. import org.drip.validation.riskfactorsingle.DiscriminatoryPowerAnalyzer;

  19. /*
  20.  * -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
  21.  */

  23. /*!
  24.  * Copyright (C) 2020 Lakshmi Krishnamurthy
  25.  * Copyright (C) 2019 Lakshmi Krishnamurthy
  26.  * 
  27.  *  This file is part of DROP, an open-source library targeting analytics/risk, transaction cost analytics,
  28.  *      asset liability management analytics, capital, exposure, and margin analytics, valuation adjustment
  29.  *      analytics, and portfolio construction analytics within and across fixed income, credit, commodity,
  30.  *      equity, FX, and structured products. It also includes auxiliary libraries for algorithm support,
  31.  *      numerical analysis, numerical optimization, spline builder, model validation, statistical learning,
  32.  *      and computational support.
  33.  *  
  34.  *      https://lakshmidrip.github.io/DROP/
  35.  *  
  36.  *  DROP is composed of three modules:
  37.  *  
  38.  *  - DROP Product Core - https://lakshmidrip.github.io/DROP-Product-Core/
  39.  *  - DROP Portfolio Core - https://lakshmidrip.github.io/DROP-Portfolio-Core/
  40.  *  - DROP Computational Core - https://lakshmidrip.github.io/DROP-Computational-Core/
  41.  * 
  42.  *  DROP Product Core implements libraries for the following:
  43.  *  - Fixed Income Analytics
  44.  *  - Loan Analytics
  45.  *  - Transaction Cost Analytics
  46.  * 
  47.  *  DROP Portfolio Core implements libraries for the following:
  48.  *  - Asset Allocation Analytics
  49.  *  - Asset Liability Management Analytics
  50.  *  - Capital Estimation Analytics
  51.  *  - Exposure Analytics
  52.  *  - Margin Analytics
  53.  *  - XVA Analytics
  54.  * 
  55.  *  DROP Computational Core implements libraries for the following:
  56.  *  - Algorithm Support
  57.  *  - Computation Support
  58.  *  - Function Analysis
  59.  *  - Model Validation
  60.  *  - Numerical Analysis
  61.  *  - Numerical Optimizer
  62.  *  - Spline Builder
  63.  *  - Statistical Learning
  64.  * 
  65.  *  Documentation for DROP is Spread Over:
  66.  * 
  67.  *  - Main                     => https://lakshmidrip.github.io/DROP/
  68.  *  - Wiki                     => https://github.com/lakshmiDRIP/DROP/wiki
  69.  *  - GitHub                   => https://github.com/lakshmiDRIP/DROP
  70.  *  - Repo Layout Taxonomy     => https://github.com/lakshmiDRIP/DROP/blob/master/Taxonomy.md
  71.  *  - Javadoc                  => https://lakshmidrip.github.io/DROP/Javadoc/index.html
  72.  *  - Technical Specifications => https://github.com/lakshmiDRIP/DROP/tree/master/Docs/Internal
  73.  *  - Release Versions         => https://lakshmidrip.github.io/DROP/version.html
  74.  *  - Community Credits        => https://lakshmidrip.github.io/DROP/credits.html
  75.  *  - Issues Catalog           => https://github.com/lakshmiDRIP/DROP/issues
  76.  *  - JUnit                    => https://lakshmidrip.github.io/DROP/junit/index.html
  77.  *  - Jacoco                   => https://lakshmidrip.github.io/DROP/jacoco/index.html
  78.  * 
  79.  *  Licensed under the Apache License, Version 2.0 (the "License");
  80.  *      you may not use this file except in compliance with the License.
  81.  *   
  82.  *  You may obtain a copy of the License at
  83.  *      http://www.apache.org/licenses/LICENSE-2.0
  84.  *  
  85.  *  Unless required by applicable law or agreed to in writing, software
  86.  *      distributed under the License is distributed on an "AS IS" BASIS,
  87.  *      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  88.  *  
  89.  *  See the License for the specific language governing permissions and
  90.  *      limitations under the License.
  91.  */

  93. /**
  94.  * <i>CVMCorrelationDiscriminatoryPowerAnalysis9a</i> demonstrates the Correlation Discriminatory Power
  95.  * Analysis on an Ensemble of Hypothesis as seen in Table 9a of Anfuso, Karyampas, and Nawroth (2017).
  96.  *
  97.  *  <br><br>
  98.  *  <ul>
  99.  *      <li>
  100.  *          Anfuso, F., D. Karyampas, and A. Nawroth (2017): A Sound Basel III Compliant Framework for
  101.  *              Back-testing Credit Exposure Models
  102.  *              https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2264620 <b>eSSRN</b>
  103.  *      </li>
  104.  *      <li>
  105.  *          Diebold, F. X., T. A. Gunther, and A. S. Tay (1998): Evaluating Density Forecasts with
  106.  *              Applications to Financial Risk Management, International Economic Review 39 (4) 863-883
  107.  *      </li>
  108.  *      <li>
  109.  *          Kenyon, C., and R. Stamm (2012): Discounting, LIBOR, CVA, and Funding: Interest Rate and Credit
  110.  *              Pricing, Palgrave Macmillan
  111.  *      </li>
  112.  *      <li>
  113.  *          Wikipedia (2018): Probability Integral Transform
  114.  *              https://en.wikipedia.org/wiki/Probability_integral_transform
  115.  *      </li>
  116.  *      <li>
  117.  *          Wikipedia (2019): p-value https://en.wikipedia.org/wiki/P-value
  118.  *      </li>
  119.  *  </ul>
  120.  *
  121.  *  <br><br>
  122.  *  <ul>
  123.  *      <li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ComputationalCore.md">Computational Core Module</a></li>
  124.  *      <li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ModelValidationAnalyticsLibrary.md">Model Validation Analytics Library</a></li>
  125.  *      <li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/sample/README.md">DROP API Construction and Usage</a></li>
  126.  *      <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/sample/anfuso2017/README.md">Anfuso, Karyampas, and Nawroth (2013) Replications</a></li>
  127.  *  </ul>
  128.  * <br><br>
  129.  *
  130.  * @author Lakshmi Krishnamurthy
  131.  */

  133. public class CVMCorrelationDiscriminatoryPowerAnalysis9a
  134. {

  136.     private static final double[][] CorrelationMatrix (
  137.         final double correlation)
  138.     {
  139.         return new double[][]
  140.         {
  141.             {1.,          correlation},
  142.             {correlation, 1.         }
  143.         };
  144.     }

  146.     private static final Ensemble Hypothesis (
  147.         final List<String> labelList,
  148.         final double[] annualStateMeanArray,
  149.         final double[] annualStateVolatilityArray,
  150.         final double[][] correlationMatrix,
  151.         final int vertexCount,
  152.         final int sampleCount,
  153.         final double horizon,
  154.         final String label1,
  155.         final String label2)
  156.         throws Exception
  157.     {
  158.         Sample[] sampleArray = new Sample[sampleCount];

  160.         for (int sampleIndex = 0; sampleIndex < sampleCount; ++sampleIndex)
  161.         {
  162.             sampleArray[sampleIndex] = NormalSampleCohort.Correlated (
  163.                 labelList,
  164.                 annualStateMeanArray,
  165.                 annualStateVolatilityArray,
  166.                 correlationMatrix,
  167.                 vertexCount,
  168.                 horizon
  169.             ).reduce (
  170.                 label1,
  171.                 label2
  172.             );
  173.         }

  175.         return new Ensemble (
  176.             sampleArray,
  177.             new TestStatisticEvaluator[]
  178.             {
  179.                 new TestStatisticEvaluator()
  180.                 {
  181.                     public double evaluate (
  182.                         final double[] drawArray)
  183.                         throws Exception
  184.                     {
  185.                         return 1.;
  186.                     }
  187.                 }
  188.             }
  189.         );
  190.     }

  192.     private static final double DistanceTest (
  193.         final GapTestOutcome gapTestOutcome)
  194.         throws Exception
  195.     {
  196.         return gapTestOutcome.distance();
  197.     }

  199.     public static final void main (
  200.         final String[] argumentArray)
  201.         throws Exception
  202.     {
  203.         EnvManager.InitEnv ("");

  205.         int sampleCount = 26;
  206.         int vertexCount = 780;
  207.         String currency = "USD";
  208.         double horizon = 1. / 12.;
  209.         double sampleCorrelation = 0.50;
  210.         String equityEntity = "SNP500";
  211.         String fxCurrencyPair = "CHF/USD";
  212.         double[] annualStateMeanArray =
  213.         {
  214.             0.06,
  215.             0.01
  216.         };
  217.         double[] annualStateVolatilityArray =
  218.         {
  219.             0.1,
  220.             0.1
  221.         };
  222.         double[] hypothesisCorrelationArray =
  223.         {
  224.             -0.99,
  225.             -0.50,
  226.              0.00,
  227.              0.50,
  228.              0.99
  229.         };

  231.         List<String> labelList = new ArrayList<String>();

  233.         String snp500Label = EntityEquityLabel.Standard (
  234.             equityEntity,
  235.             currency
  236.         ).fullyQualifiedName();

  238.         String chfusdLabel = FXLabel.Standard (fxCurrencyPair).fullyQualifiedName();

  240.         labelList.add (snp500Label);

  242.         labelList.add (chfusdLabel);

  244.         Sample sample = NormalSampleCohort.Correlated (
  245.             labelList,
  246.             annualStateMeanArray,
  247.             annualStateVolatilityArray,
  248.             CorrelationMatrix (sampleCorrelation),
  249.             vertexCount,
  250.             horizon
  251.         ).reduce (
  252.             snp500Label,
  253.             chfusdLabel
  254.         );

  256.         DiscriminatoryPowerAnalyzer discriminatoryPowerAnalysis = DiscriminatoryPowerAnalyzer.FromSample (
  257.             sample,
  258.             GapTestSetting.RiskFactorLossTest (GapLossWeightFunction.CramersVonMises())
  259.         );

  261.         System.out.println ("\t|-----------------------||");

  263.         System.out.println ("\t|   CORRELATION SCAN    ||");

  265.         System.out.println ("\t|-----------------------||");

  267.         System.out.println ("\t|    L -> R:            ||");

  269.         System.out.println ("\t|        - Correlation  ||");

  271.         System.out.println ("\t|        - Distance     ||");

  273.         System.out.println ("\t|-----------------------||");

  275.         for (double hypothesisCorrelation : hypothesisCorrelationArray)
  276.         {
  277.             Ensemble hypothesis = Hypothesis (
  278.                 labelList,
  279.                 annualStateMeanArray,
  280.                 annualStateVolatilityArray,
  281.                 CorrelationMatrix (hypothesisCorrelation),
  282.                 vertexCount,
  283.                 sampleCount,
  284.                 horizon,
  285.                 snp500Label,
  286.                 chfusdLabel
  287.             );

  289.             GapTestOutcome gapTestOutcome = discriminatoryPowerAnalysis.gapTest (hypothesis);

  291.             System.out.println (
  292.                 "\t| " +
  293.                 FormatUtil.FormatDouble (hypothesisCorrelation, 1, 3, 1.) + " => " +
  294.                 FormatUtil.FormatDouble (DistanceTest (gapTestOutcome), 1, 8, 1.) + " ||"
  295.             );
  296.         }

  298.         System.out.println ("\t|-----------------------||");

  300.         EnvManager.TerminateEnv();
  301.     }
  302. }
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