LabelCovariance.java
package org.drip.measure.stochastic;
/*
* -*- 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>LabelCovariance</i> holds the Covariance between any Stochastic Variates identified by their Labels, as
* well as their Means. The References are:
*
* <br><br>
* <ul>
* <li>
* Andersen, L. B. G., M. Pykhtin, and A. Sokol (2017): Credit Exposure in the Presence of Initial
* Margin https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2806156 <b>eSSRN</b>
* </li>
* <li>
* Albanese, C., S. Caenazzo, and O. Frankel (2017): Regression Sensitivities for Initial Margin
* Calculations https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2763488 <b>eSSRN</b>
* </li>
* <li>
* Anfuso, F., D. Aziz, P. Giltinan, and K. Loukopoulus (2017): A Sound Modeling and Back-testing
* Framework for Forecasting Initial Margin Requirements
* https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2716279 <b>eSSRN</b>
* </li>
* <li>
* Caspers, P., P. Giltinan, R. Lichters, and N. Nowaczyk (2017): Forecasting Initial Margin
* Requirements - A Model Evaluation https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2911167
* <b>eSSRN</b>
* </li>
* <li>
* International Swaps and Derivatives Association (2017): SIMM v2.0 Methodology
* https://www.isda.org/a/oFiDE/isda-simm-v2.pdf
* </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/NumericalAnalysisLibrary.md">Numerical Analysis Library</a></li>
* <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>
* <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>
* </ul>
*
* @author Lakshmi Krishnamurthy
*/
public class LabelCovariance extends org.drip.measure.stochastic.LabelCorrelation
{
private double[] _meanArray = null;
private double[] _volatilityArray = null;
private org.drip.measure.gaussian.Covariance _covariance = null;
/**
* LabelCovariance Constructor
*
* @param labelList The List of Labels
* @param meanArray Array of Variate Means
* @param volatilityArray Array of Variate Volatilities
* @param correlationMatrix The Correlation Matrix
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public LabelCovariance (
final java.util.List<java.lang.String> labelList,
final double[] meanArray,
final double[] volatilityArray,
final double[][] correlationMatrix)
throws java.lang.Exception
{
super (
labelList,
correlationMatrix
);
if (null == (_meanArray = meanArray) ||
null == (_volatilityArray = volatilityArray))
{
throw new java.lang.Exception ("LabelCovariance Constructor => Invalid Inputs");
}
int variateCount = correlationMatrix.length;
double[][] covarianceMatrix = new double[variateCount][variateCount];
if (variateCount != _meanArray.length ||
variateCount != _volatilityArray.length)
{
throw new java.lang.Exception ("LabelCovariance Constructor => Invalid Inputs");
}
for (int variateIndexI = 0; variateIndexI < variateCount; ++variateIndexI)
{
if (!org.drip.numerical.common.NumberUtil.IsValid (_meanArray[variateIndexI]) ||
!org.drip.numerical.common.NumberUtil.IsValid (_volatilityArray[variateIndexI]) ||
0. > _volatilityArray[variateIndexI])
{
throw new java.lang.Exception ("LabelCovariance Constructor => Invalid Inputs");
}
for (int variateIndexJ = 0; variateIndexJ < variateCount; ++variateIndexJ)
{
covarianceMatrix[variateIndexI][variateIndexJ] =
correlationMatrix[variateIndexI][variateIndexJ] * _volatilityArray[variateIndexI] *
_volatilityArray[variateIndexJ];
}
}
_covariance = new org.drip.measure.gaussian.Covariance (covarianceMatrix);
}
/**
* Retrieve the Array of Variate Means
*
* @return The Array of Variate Means
*/
public double[] meanArray()
{
return _meanArray;
}
/**
* Retrieve the Array of Variate Volatilities
*
* @return The Array of Variate Volatilities
*/
public double[] volatilityArray()
{
return _volatilityArray;
}
/**
* Retrieve the Correlation Matrix
*
* @return The Correlation Matrix
*/
public double[][] correlationMatrix()
{
return _matrix;
}
/**
* Retrieve the Covariance Matrix
*
* @return The Covariance Matrix
*/
public double[][] covarianceMatrix()
{
return _covariance.covarianceMatrix();
}
/**
* Retrieve the Precision Matrix
*
* @return The Precision Matrix
*/
public double[][] precisionMatrix()
{
return _covariance.precisionMatrix();
}
/**
* Retrieve the Mean of the Latent State
*
* @param label Latent State Label
*
* @return Mean of the Latent State
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public double mean (
final java.lang.String label)
throws java.lang.Exception
{
if (null == label || !_labelList.contains (label))
{
throw new java.lang.Exception ("LabelCovariance::mean => Invalid Inputs");
}
return _meanArray[_labelIndexMap.get (label)];
}
/**
* Retrieve the Volatility of the Latent State
*
* @param label Latent State Label
*
* @return Volatility of the Latent State
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public double volatility (
final java.lang.String label)
throws java.lang.Exception
{
if (null == label || !_labelList.contains (label))
{
throw new java.lang.Exception ("LabelCovariance::volatility => Invalid Inputs");
}
return _volatilityArray[_labelIndexMap.get (label)];
}
}