ProjectionImpliedConfidenceOutput.java
package org.drip.portfolioconstruction.bayesian;
/*
* -*- 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
* Copyright (C) 2017 Lakshmi Krishnamurthy
* Copyright (C) 2016 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>ProjectionImpliedConfidenceOutput</i> holds the Results of the Idzorek 2005 Black Litterman Intuitive
* Projection Confidence Level Estimation Run. The References are:
*
* <br><br>
* <ul>
* <li>
* He. G., and R. Litterman (1999): <i>The Intuition behind the Black-Litterman Model
* Portfolios</i> <b>Goldman Sachs Asset Management</b>
* </li>
* <li>
* Idzorek, T. (2005): <i>A Step-by-Step Guide to the Black-Litterman Model: Incorporating
* User-Specified Confidence Levels</i> <b>Ibbotson Associates</b> Chicago, IL
* </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/AssetAllocationAnalyticsLibrary.md">Asset Allocation Analytics</a></li>
* <li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/portfolioconstruction/README.md">Portfolio Construction under Allocation Constraints</a></li>
* <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/portfolioconstruction/bayesian/README.md">Black Litterman Bayesian Portfolio Construction</a></li>
* </ul>
*
* @author Lakshmi Krishnamurthy
*/
public class ProjectionImpliedConfidenceOutput
{
private double[] _unadjustedWeightArray = null;
private org.drip.portfolioconstruction.bayesian.BlackLittermanOutput _fullConfidenceOutput = null;
private org.drip.portfolioconstruction.bayesian.BlackLittermanCustomConfidenceOutput
_customConfidenceOutput = null;
/**
* ProjectionImpliedConfidenceOutput Constructor
*
* @param unadjustedWeightArray Array of the Unadjusted Weights
* @param customConfidenceOutput The Custom Confidence Black Litterman Run Output
* @param fullConfidenceOutput The Full Confidence Black Litterman Run Output
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public ProjectionImpliedConfidenceOutput (
final double[] unadjustedWeightArray,
final org.drip.portfolioconstruction.bayesian.BlackLittermanCustomConfidenceOutput
customConfidenceOutput,
final org.drip.portfolioconstruction.bayesian.BlackLittermanOutput fullConfidenceOutput)
throws java.lang.Exception
{
if (null == (_unadjustedWeightArray = unadjustedWeightArray) || 0 == _unadjustedWeightArray.length ||
null == (_customConfidenceOutput = customConfidenceOutput) ||
null == (_fullConfidenceOutput = fullConfidenceOutput))
{
throw new java.lang.Exception
("ProjectionImpliedConfidenceOutput Constructor => Invalid Inputs");
}
}
/**
* Retrieve the Array of the Unadjusted Equilibrium Weights
*
* @return The Array of the Unadjusted Equilibrium Weights
*/
public double[] unadjustedWeightArray()
{
return _unadjustedWeightArray;
}
/**
* Retrieve the Custom Projection Confidence Black Litterman Run Output
*
* @return The Custom Projection Confidence Black Litterman Run Output
*/
public org.drip.portfolioconstruction.bayesian.BlackLittermanCustomConfidenceOutput
customConfidenceOutput()
{
return _customConfidenceOutput;
}
/**
* Retrieve the Full Projection Confidence Black Litterman Run Output
*
* @return The Full Projection Confidence Black Litterman Run Output
*/
public org.drip.portfolioconstruction.bayesian.BlackLittermanOutput fullConfidenceOutput()
{
return _fullConfidenceOutput;
}
/**
* Retrieve the Custom Projection Induced Equilibrium Asset Deviation Array
*
* @return The Custom Projection Induced Equilibrium Asset Deviation Array
*/
public double[] customProjectionConfidenceDeviation()
{
return _customConfidenceOutput.allocationAdjustmentTiltArray();
}
/**
* Retrieve the Custom Projection Induced Equilibrium Asset Weight Array
*
* @return The Custom Projection Induced Equilibrium Asset Weight Array
*/
public double[] customProjectionConfidenceWeight()
{
return _customConfidenceOutput.adjustedOptimizationOutput().optimalPortfolio().weightArray();
}
/**
* Retrieve the Full Projection Induced Equilibrium Asset Deviation Array
*
* @return The Full Projection Induced Equilibrium Asset Deviation Array
*/
public double[] fullProjectionConfidenceDeviation()
{
return _fullConfidenceOutput.allocationAdjustmentTiltArray();
}
/**
* Retrieve the Full Projection Induced Equilibrium Asset Weight Array
*
* @return The Full Projection Induced Equilibrium Asset Weight Array
*/
public double[] fullProjectionConfidenceWeight()
{
return _fullConfidenceOutput.adjustedOptimizationOutput().optimalPortfolio().weightArray();
}
/**
* Compute the Array of the Custom Projection Induced Confidence Level
*
* @return The Array of the Custom Projection Induced Confidence Level
*/
public double[] impliedConfidenceLevelArray()
{
int assetCount = _unadjustedWeightArray.length;
double[] impliedConfidenceLevelArray = new double[assetCount];
double[] fullProjectionConfidenceDeviationArray =
_fullConfidenceOutput.allocationAdjustmentTiltArray();
double[] customProjectionConfidenceDeviationArray =
_customConfidenceOutput.allocationAdjustmentTiltArray();
for (int assetIndex = 0; assetIndex < assetCount; ++assetIndex)
{
impliedConfidenceLevelArray[assetIndex] = customProjectionConfidenceDeviationArray[assetIndex] /
fullProjectionConfidenceDeviationArray[assetIndex];
}
return impliedConfidenceLevelArray;
}
}