CVMDiscriminatoryPowerAnalysis3b.java
package org.drip.sample.anfuso2017;
import org.drip.measure.gaussian.R1UnivariateNormal;
import org.drip.numerical.common.FormatUtil;
import org.drip.service.env.EnvManager;
import org.drip.validation.distance.GapLossWeightFunction;
import org.drip.validation.distance.GapTestOutcome;
import org.drip.validation.distance.GapTestSetting;
import org.drip.validation.evidence.Ensemble;
import org.drip.validation.evidence.Sample;
import org.drip.validation.evidence.TestStatisticEvaluator;
import org.drip.validation.riskfactorsingle.DiscriminatoryPowerAnalyzer;
/*
* -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
*/
/*!
* Copyright (C) 2020 Lakshmi Krishnamurthy
* Copyright (C) 2019 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>CVMDiscriminatoryPowerAnalysis3b</i> demonstrates the Discriminatory Power Analysis illustrated in
* Table 3b of Anfuso, Karyampas, and Nawroth (2013).
*
* <br><br>
* <ul>
* <li>
* Anfuso, F., D. Karyampas, and A. Nawroth (2017): A Sound Basel III Compliant Framework for
* Back-testing Credit Exposure Models
* https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2264620 <b>eSSRN</b>
* </li>
* <li>
* Diebold, F. X., T. A. Gunther, and A. S. Tay (1998): Evaluating Density Forecasts with
* Applications to Financial Risk Management, International Economic Review 39 (4) 863-883
* </li>
* <li>
* Kenyon, C., and R. Stamm (2012): Discounting, LIBOR, CVA, and Funding: Interest Rate and Credit
* Pricing, Palgrave Macmillan
* </li>
* <li>
* Wikipedia (2018): Probability Integral Transform
* https://en.wikipedia.org/wiki/Probability_integral_transform
* </li>
* <li>
* Wikipedia (2019): p-value https://en.wikipedia.org/wiki/P-value
* </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/ModelValidationAnalyticsLibrary.md">Model Validation Analytics Library</a></li>
* <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>
* <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>
* </ul>
* <br><br>
*
* @author Lakshmi Krishnamurthy
*/
public class CVMDiscriminatoryPowerAnalysis3b
{
private static final double UnivariateRandom (
final double mean,
final double sigma)
throws Exception
{
return new R1UnivariateNormal (
mean,
sigma
).random();
}
private static final Sample GenerateSample (
final double mean,
final double sigma,
final int drawCount)
throws Exception
{
double[] univariateRandomArray = new double[drawCount];
for (int drawIndex = 0; drawIndex < drawCount; ++drawIndex)
{
univariateRandomArray[drawIndex] = UnivariateRandom (
mean,
sigma
);
}
return new Sample (univariateRandomArray);
}
private static final Sample[] GenerateSampleArray (
final double mean,
final double sigma,
final int drawCount,
final int sampleCount)
throws Exception
{
Sample[] sampleArray = new Sample[sampleCount];
for (int sampleIndex = 0; sampleIndex < sampleCount; ++sampleIndex)
{
sampleArray[sampleIndex] = GenerateSample (
mean,
sigma,
drawCount
);
}
return sampleArray;
}
private static final Ensemble GenerateEnsemble (
final double mean,
final double sigma,
final int drawCount,
final int sampleCount)
throws Exception
{
return new Ensemble (
GenerateSampleArray (
mean,
sigma,
drawCount,
sampleCount
),
new TestStatisticEvaluator[]
{
new TestStatisticEvaluator()
{
public double evaluate (
final double[] drawArray)
throws Exception
{
return 1.;
}
}
}
);
}
private static final double DistanceTest (
final GapTestOutcome gapTestOutcome)
throws Exception
{
return gapTestOutcome.distance();
}
public static final void main (
final String[] argumentArray)
throws Exception
{
EnvManager.InitEnv ("");
int drawCount = 3780;
int sampleCount = 100;
double horizon = 3. / 12;
double sampleAnnualMean = 0.;
double sampleAnnualVolatility = 0.1;
double[] hypothesisAnnualMeanArray = {
-0.050,
-0.025,
0.000,
0.025,
0.050
};
double[] hypothesisAnnualVolatilityArray = {
0.050,
0.075,
0.100,
0.125,
0.150
};
double hypothesisHorizonSQRT = Math.sqrt (horizon);
Sample sample = GenerateSample (
sampleAnnualMean,
sampleAnnualVolatility * hypothesisHorizonSQRT,
drawCount
);
DiscriminatoryPowerAnalyzer discriminatoryPowerAnalysis = DiscriminatoryPowerAnalyzer.FromSample (
sample,
GapTestSetting.RiskFactorLossTest (
GapLossWeightFunction.AndersonDarling()
)
);
System.out.println ("\t|--------------------------------||");
System.out.println ("\t| DISCRIMINANT GRID SCAN ||");
System.out.println ("\t|--------------------------------||");
System.out.println ("\t| L -> R: ||");
System.out.println ("\t| - Hypothesis Mean ||");
System.out.println ("\t| - Hypothesis Sigma ||");
System.out.println ("\t| - Distance Metric ||");
System.out.println ("\t|--------------------------------||");
for (double hypothesisAnnualMean : hypothesisAnnualMeanArray)
{
for (double hypothesisAnnualVolatility : hypothesisAnnualVolatilityArray)
{
Ensemble hypothesis = GenerateEnsemble (
hypothesisAnnualMean * horizon,
hypothesisAnnualVolatility * hypothesisHorizonSQRT,
drawCount,
sampleCount
);
GapTestOutcome gapTestOutcome = discriminatoryPowerAnalysis.gapTest (hypothesis);
System.out.println (
"\t| " +
FormatUtil.FormatDouble (hypothesisAnnualMean, 1, 3, 1.) + " | " +
FormatUtil.FormatDouble (hypothesisAnnualVolatility, 1, 3, 1.) + " => " +
FormatUtil.FormatDouble (DistanceTest (gapTestOutcome), 1, 8, 1.) + " ||"
);
}
}
System.out.println ("\t|------------------------------||");
EnvManager.TerminateEnv();
}
}