RiskFactorTenorSensitivity.java
package org.drip.simm.product;
/*
* -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
*/
/*!
* Copyright (C) 2020 Lakshmi Krishnamurthy
* Copyright (C) 2019 Lakshmi Krishnamurthy
* Copyright (C) 2018 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>RiskFactorTenorSensitivity</i> holds the ISDA SIMM 2.0 Risk Factor Tenor Bucket Sensitivities. 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/PortfolioCore.md">Portfolio Core Module</a></li>
* <li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/MarginAnalyticsLibrary.md">Initial and Variation Margin Analytics</a></li>
* <li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/simm/README.md">Initial Margin Analytics based on ISDA SIMM and its Variants</a></li>
* <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/simm/product/README.md">ISDA SIMM Risk Factor Sensitivities</a></li>
* </ul>
* <br><br>
*
* @author Lakshmi Krishnamurthy
*/
public class RiskFactorTenorSensitivity
{
private java.util.Map<java.lang.String, java.lang.Double> _sensitivityMap = null;
/**
* RiskFactorTenorSensitivity Constructor
*
* @param sensitivityMap The Tenor Sensitivity Map
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public RiskFactorTenorSensitivity (
java.util.Map<java.lang.String, java.lang.Double> sensitivityMap)
throws java.lang.Exception
{
if (null == (_sensitivityMap = sensitivityMap) || 0 == _sensitivityMap.size())
{
throw new java.lang.Exception ("RiskFactorTenorSensitivity Constructor => Invalid Inputs");
}
}
/**
* Retrieve the Set of Tenors
*
* @return The Set of Tenors
*/
public java.util.Set<java.lang.String> tenorSet()
{
return _sensitivityMap.keySet();
}
/**
* Add the Tenor Sensitivity
*
* @param tenor The Tenor
* @param sensitivity Sensitivity for the given Tenor
*
* @return TRUE - The Tenor Sensitivity successfully set
*/
public boolean addTenorDelta (
final java.lang.String tenor,
final double sensitivity)
{
if (null == tenor || !org.drip.numerical.common.NumberUtil.IsValid (sensitivity))
{
return false;
}
_sensitivityMap.put (
tenor,
sensitivity
);
return true;
}
/**
* Indicate of the Sensitivity exists for the specified Tenor
*
* @param tenor The Tenor
*
* @return TRUE - Sensitivity exists for the specified Tenor
*/
public boolean tenorExists (
final java.lang.String tenor)
{
return null != tenor && _sensitivityMap.containsKey (tenor);
}
/**
* Retrieve the Sensitivity for the Bucket Tenor
*
* @param tenor The Tenor
*
* @return The Sensitivity corresponding to the Tenor
*
* @throws java.lang.Exception Thrown if the Input is Invalid
*/
public double sensitivity (
final java.lang.String tenor)
throws java.lang.Exception
{
if (!tenorExists (tenor))
{
throw new java.lang.Exception ("RiskFactorTenorSensitivity::sensitivity => Invalid Inputs");
}
return _sensitivityMap.get (tenor);
}
/**
* Retrieve the Map of Tenor Sensitivities
*
* @return The Map of Tenor Sensitivities
*/
public java.util.Map<java.lang.String, java.lang.Double> sensitivityMap()
{
return _sensitivityMap;
}
/**
* Generate the Cumulative Tenor Sensitivity
*
* @return The Cumulative Tenor Sensitivity
*/
public double cumulative()
{
double cumulative = 0.;
for (java.util.Map.Entry<java.lang.String, java.lang.Double> sensitivityEntry : _sensitivityMap.entrySet())
{
cumulative = cumulative + sensitivityEntry.getValue();
}
return cumulative;
}
/**
* Generate the Tenor Sensitivity Margin Map
*
* @param sensitivityRiskWeightMap The Tenor Sensitivity Risk Weight Map
*
* @return The Tenor Sensitivity Margin Map
*/
public java.util.Map<java.lang.String, java.lang.Double> sensitivityMargin (
final java.util.Map<java.lang.String, java.lang.Double> sensitivityRiskWeightMap)
{
if (null == sensitivityRiskWeightMap || 0 == sensitivityRiskWeightMap.size())
{
return null;
}
java.util.Map<java.lang.String, java.lang.Double> sensitivityMargin = new
java.util.HashMap<java.lang.String, java.lang.Double>();
for (java.util.Map.Entry<java.lang.String, java.lang.Double> sensitivityEntry :
_sensitivityMap.entrySet())
{
java.lang.String tenor = sensitivityEntry.getKey();
if (!sensitivityRiskWeightMap.containsKey (tenor))
{
return null;
}
sensitivityMargin.put (
tenor,
sensitivityEntry.getValue() * sensitivityRiskWeightMap.get (tenor)
);
}
return sensitivityMargin;
}
}