PathVertexExerciseMetrics.java

package org.drip.sample.govviemc;

import org.drip.analytics.date.*;
import org.drip.measure.crng.RandomNumberGenerator;
import org.drip.measure.discrete.CorrelatedPathVertexDimension;
import org.drip.measure.dynamics.DiffusionEvaluatorLogarithmic;
import org.drip.measure.process.DiffusionEvolver;
import org.drip.measure.statistics.UnivariateDiscreteThin;
import org.drip.numerical.common.FormatUtil;
import org.drip.param.creator.MarketParamsBuilder;
import org.drip.param.market.CurveSurfaceQuoteContainer;
import org.drip.param.valuation.ValuationParams;
import org.drip.product.creator.BondBuilder;
import org.drip.product.credit.BondComponent;
import org.drip.product.params.EmbeddedOptionSchedule;
import org.drip.service.env.EnvManager;
import org.drip.service.template.LatentMarketStateBuilder;
import org.drip.state.discount.MergedDiscountForwardCurve;
import org.drip.state.govvie.GovvieCurve;
import org.drip.state.sequence.*;

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

/*!
 * Copyright (C) 2019 Lakshmi Krishnamurthy
 * Copyright (C) 2018 Lakshmi Krishnamurthy
 * Copyright (C) 2017 Lakshmi Krishnamurthy
 * 
 *  This file is part of DROP, an open-source library targeting risk, transaction costs, exposure, margin
 *  	calculations, valuation adjustment, and portfolio construction within and across fixed income,
 *  	credit, commodity, equity, FX, and structured products.
 *  
 *  	https://lakshmidrip.github.io/DROP/
 *  
 *  DROP is composed of three modules:
 *  
 *  - DROP Analytics Core - https://lakshmidrip.github.io/DROP-Analytics-Core/
 *  - DROP Portfolio Core - https://lakshmidrip.github.io/DROP-Portfolio-Core/
 *  - DROP Numerical Core - https://lakshmidrip.github.io/DROP-Numerical-Core/
 * 
 * 	DROP Analytics Core implements libraries for the following:
 * 	- Fixed Income Analytics
 * 	- Asset Backed Analytics
 * 	- XVA Analytics
 * 	- Exposure and Margin Analytics
 * 
 * 	DROP Portfolio Core implements libraries for the following:
 * 	- Asset Allocation Analytics
 * 	- Transaction Cost Analytics
 * 
 * 	DROP Numerical Core implements libraries for the following:
 * 	- Statistical Learning
 * 	- Numerical Optimizer
 * 	- Spline Builder
 * 	- Algorithm Support
 * 
 * 	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>PathVertexExerciseMetrics</i> demonstrates the Simulations of the Per-Path Callable Bond OAS Based
 * Exercise Metrics.
 *  
 * <br><br>
 *  <ul>
 *		<li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/AnalyticsCore.md">Analytics Core Module</a></li>
 *		<li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/FixedIncomeAnalyticsLibrary.md">Fixed Income 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">Sample</a></li>
 *		<li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/sample/govviemc/README.md">Govvie Curve Monte Carlo Runs</a></li>
 *  </ul>
 * <br><br>
 * 
 * @author Lakshmi Krishnamurthy
 */

public class PathVertexExerciseMetrics {

	private static final MergedDiscountForwardCurve FundingCurve (
		final JulianDate dtSpot,
		final String strCurrency,
		final double dblBump)
		throws Exception
	{
		String[] astrDepositMaturityTenor = new String[] {
			"2D"
		};

		double[] adblDepositQuote = new double[] {
			0.0111956 + dblBump // 2D
		};

		double[] adblFuturesQuote = new double[] {
			0.011375 + dblBump,	// 98.8625
			0.013350 + dblBump,	// 98.6650
			0.014800 + dblBump,	// 98.5200
			0.016450 + dblBump,	// 98.3550
			0.017850 + dblBump,	// 98.2150
			0.019300 + dblBump	// 98.0700
		};

		String[] astrFixFloatMaturityTenor = new String[] {
			"02Y",
			"03Y",
			"04Y",
			"05Y",
			"06Y",
			"07Y",
			"08Y",
			"09Y",
			"10Y",
			"11Y",
			"12Y",
			"15Y",
			"20Y",
			"25Y",
			"30Y",
			"40Y",
			"50Y"
		};

		double[] adblFixFloatQuote = new double[] {
			0.017029 + dblBump, //  2Y
			0.019354 + dblBump, //  3Y
			0.021044 + dblBump, //  4Y
			0.022291 + dblBump, //  5Y
			0.023240 + dblBump, //  6Y
			0.024025 + dblBump, //  7Y
			0.024683 + dblBump, //  8Y
			0.025243 + dblBump, //  9Y
			0.025720 + dblBump, // 10Y
			0.026130 + dblBump, // 11Y
			0.026495 + dblBump, // 12Y
			0.027230 + dblBump, // 15Y
			0.027855 + dblBump, // 20Y
			0.028025 + dblBump, // 25Y
			0.028028 + dblBump, // 30Y
			0.027902 + dblBump, // 40Y
			0.027655 + dblBump  // 50Y
		};

		return LatentMarketStateBuilder.SmoothFundingCurve (
			dtSpot,
			strCurrency,
			astrDepositMaturityTenor,
			adblDepositQuote,
			"ForwardRate",
			adblFuturesQuote,
			"ForwardRate",
			astrFixFloatMaturityTenor,
			adblFixFloatQuote,
			"SwapRate"
		);
	}

	private static final PathVertexGovvie ScenarioGovvieCurves (
		final JulianDate dtSpot,
		final int iNumPath,
		final int iNumVertex)
		throws Exception
	{
		double dblVolatility = 0.10;
		String strTreasuryCode = "UST";

		String[] astrTenor = new String[] {
			"01Y",
			"02Y",
			"03Y",
			"05Y",
			"07Y",
			"10Y",
			"20Y",
			"30Y"
		};

		double[] adblTreasuryCoupon = new double[] {
			0.0100,
			0.0100,
			0.0125,
			0.0150,
			0.0200,
			0.0225,
			0.0250,
			0.0300
		};

		double[] adblTreasuryYield = new double[] {
			0.0083,	//  1Y
			0.0122, //  2Y
			0.0149, //  3Y
			0.0193, //  5Y
			0.0227, //  7Y
			0.0248, // 10Y
			0.0280, // 20Y
			0.0308  // 30Y
		};

		int iNumDimension = astrTenor.length;
		double[][] aadblCorrelation = new double[iNumDimension][iNumDimension];

		for (int i = 0; i < iNumDimension; ++i) {
			for (int j = 0; j < iNumDimension; ++j)
				aadblCorrelation[i][j] = i == j ? 1. : 0.;
		}

		GovvieBuilderSettings gbs = new GovvieBuilderSettings (
			dtSpot,
			strTreasuryCode,
			astrTenor,
			adblTreasuryCoupon,
			adblTreasuryYield
		);

		return PathVertexGovvie.Standard (
			gbs,
			new CorrelatedPathVertexDimension (
				new RandomNumberGenerator(),
				aadblCorrelation,
				iNumVertex,
				iNumPath,
				false,
				null
			),
			new DiffusionEvolver (
				DiffusionEvaluatorLogarithmic.Standard (
					0.,
					dblVolatility
				)
			)
		);
	}

	private static final BondComponent Callable (
		final EmbeddedOptionSchedule eos)
		throws Exception
	{
		JulianDate dtEffective = DateUtil.CreateFromYMD (
			2009,
			12,
			3
		);

		JulianDate dtMaturity  = DateUtil.CreateFromYMD (
			2039,
			12,
			1
		);

		double dblCoupon = 0.06558;
		int iFreq = 2;
		String strCUSIP = "033177XV3";
		String strDayCount = "30/360";

		BondComponent bond = BondBuilder.CreateSimpleFixed (
			strCUSIP,
			"USD",
			"",
			dblCoupon,
			iFreq,
			strDayCount,
			dtEffective,
			dtMaturity,
			null,
			null
		);

		bond.setEmbeddedCallSchedule (eos);

		return bond;
	}

	public static final void main (
		final String[] astrArgs)
		throws Exception
	{
		EnvManager.InitEnv ("");

		JulianDate dtSpot = DateUtil.CreateFromYMD (
			2017,
			DateUtil.MARCH,
			24
		);

		int iNumPath = 50;
		double dblCleanPrice = 1.08641;
		int[] aiExerciseDate = new int[] {
			DateUtil.CreateFromYMD (2019, 12,  1).julian(),
			DateUtil.CreateFromYMD (2020, 12,  1).julian(),
			DateUtil.CreateFromYMD (2021, 12,  1).julian(),
			DateUtil.CreateFromYMD (2022, 12,  1).julian(),
			DateUtil.CreateFromYMD (2023, 12,  1).julian(),
			DateUtil.CreateFromYMD (2024, 12,  1).julian(),
			DateUtil.CreateFromYMD (2025, 12,  1).julian(),
			DateUtil.CreateFromYMD (2026, 12,  1).julian(),
			DateUtil.CreateFromYMD (2027, 12,  1).julian(),
			DateUtil.CreateFromYMD (2028, 12,  1).julian(),
			DateUtil.CreateFromYMD (2029, 12,  1).julian(),
			DateUtil.CreateFromYMD (2030, 12,  1).julian(),
			DateUtil.CreateFromYMD (2031, 12,  1).julian(),
			DateUtil.CreateFromYMD (2032, 12,  1).julian(),
			DateUtil.CreateFromYMD (2033, 12,  1).julian(),
			DateUtil.CreateFromYMD (2034, 12,  1).julian(),
			DateUtil.CreateFromYMD (2035, 12,  1).julian(),
			DateUtil.CreateFromYMD (2036, 12,  1).julian(),
			DateUtil.CreateFromYMD (2037, 12,  1).julian(),
			DateUtil.CreateFromYMD (2038, 12,  1).julian(),
		};
		double[] adblExercisePrice = new double[] {
			1.,
			1.,
			1.,
			1.,
			1.,
			1.,
			1.,
			1.,
			1.,
			1.,
			1.,
			1.,
			1.,
			1.,
			1.,
			1.,
			1.,
			1.,
			1.,
			1.,
		};

		int iNumVertex = aiExerciseDate.length;
		double[] adblOptimalExercisePV = new double[iNumPath];
		int[] aiOptimalExerciseVertexIndex = new int[iNumPath];
		double[] adblOptimalExerciseOAS = new double[iNumPath];
		double[] adblOptimalExercisePrice = new double[iNumPath];
		double[] adblOptimalExerciseOASGap = new double[iNumPath];
		double[] adblOptimalExerciseDuration = new double[iNumPath];
		double[] adblOptimalExerciseConvexity = new double[iNumPath];
		JulianDate[] adtOptimalExerciseDate = new JulianDate[iNumPath];
		double[][] aadblForwardPrice = new double[iNumPath][iNumVertex];
		ValuationParams[] aValParamsEvent = new ValuationParams[iNumVertex];

		BondComponent bond = Callable (
			new EmbeddedOptionSchedule (
				aiExerciseDate,
				adblExercisePrice,
				false,
				30,
				false,
				Double.NaN,
				"",
				Double.NaN
			)
		);

		PathVertexGovvie mcrg = ScenarioGovvieCurves (
			dtSpot,
			iNumPath,
			iNumVertex
		);

		GovvieCurve[][] aaGCPathEvent = mcrg.pathVertex (aiExerciseDate);

		MergedDiscountForwardCurve mdfc = FundingCurve (
			dtSpot,
			"USD",
			0.
		);

		CurveSurfaceQuoteContainer csqcSpot = MarketParamsBuilder.Create (
			mdfc,
			mcrg.govvieBuilderSettings().groundState(),
			null,
			null,
			null,
			null,
			null
		);

		ValuationParams valParamsSpot = ValuationParams.Spot (dtSpot.julian());

		double dblOASSpot = bond.oasFromPrice (
			valParamsSpot,
			csqcSpot,
			null,
			dblCleanPrice
		);

		for (int iVertex = 0; iVertex < iNumVertex; ++iVertex)
			aValParamsEvent[iVertex] = ValuationParams.Spot (aiExerciseDate[iVertex]);

		for (int iPath = 0; iPath < iNumPath; ++iPath) {
			for (int iVertex = 0; iVertex < iNumVertex; ++iVertex) {
				CurveSurfaceQuoteContainer csqcEvent = MarketParamsBuilder.Create (
					mdfc,
					aaGCPathEvent[iPath][iVertex],
					null,
					null,
					null,
					null,
					null
				);

				aadblForwardPrice[iPath][iVertex] = bond.priceFromOAS (
					aValParamsEvent[iVertex],
					csqcEvent,
					null,
					dblOASSpot
				);
			}
		}

		for (int iPath = 0; iPath < iNumPath; ++iPath) {
			adblOptimalExercisePV[iPath] = 0.;
			adblOptimalExercisePrice[iPath] = 1.;
			aiOptimalExerciseVertexIndex[0] = iNumVertex - 1;

			adtOptimalExerciseDate[iPath] = bond.maturityDate();

			for (int iVertex = 0; iVertex < iNumVertex; ++iVertex) {
				double dblExercisePV = (aadblForwardPrice[iPath][iVertex] - adblExercisePrice[iVertex])
					* mdfc.df (aiExerciseDate[iVertex]);

				if (dblExercisePV > adblOptimalExercisePV[iPath]) {
					adtOptimalExerciseDate[iPath] = new JulianDate (aiExerciseDate[iVertex]);

					adblOptimalExercisePrice[iPath] = adblExercisePrice[iVertex];
					aiOptimalExerciseVertexIndex[iPath] = iVertex;
					adblOptimalExercisePV[iPath] = dblExercisePV;
				}
			}
		}

		System.out.println ("\n");

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

		System.out.println ("\t||                        PATH-WISE EXERCISE METRICS                         ||");

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

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

		System.out.println ("\t||        Path Number                                                        ||");

		System.out.println ("\t||        Optimal Exercise Index                                             ||");

		System.out.println ("\t||        Optimal Exercise Date                                              ||");

		System.out.println ("\t||        Optimal Exercise Price                                             ||");

		System.out.println ("\t||        Optimal Exercise Value                                             ||");

		System.out.println ("\t||        Optimal Exercise OAS                                               ||");

		System.out.println ("\t||        Optimal Exercise OAS Gap                                           ||");

		System.out.println ("\t||        Optimal Exercise Duration                                          ||");

		System.out.println ("\t||        Optimal Exercise Convexity                                         ||");

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

		for (int iPath = 0; iPath < iNumPath; ++iPath) {
			int iOptimalExerciseDate = adtOptimalExerciseDate[iPath].julian();

			adblOptimalExerciseOAS[iPath] = bond.oasFromPrice (
				valParamsSpot,
				csqcSpot,
				null,
				iOptimalExerciseDate,
				adblOptimalExercisePrice[iPath],
				dblCleanPrice
			);

			adblOptimalExerciseDuration[iPath] = bond.modifiedDurationFromPrice (
				valParamsSpot,
				csqcSpot,
				null,
				iOptimalExerciseDate,
				adblOptimalExercisePrice[iPath],
				dblCleanPrice
			);

			adblOptimalExerciseConvexity[iPath] = bond.convexityFromPrice (
				valParamsSpot,
				csqcSpot,
				null,
				iOptimalExerciseDate,
				adblOptimalExercisePrice[iPath],
				dblCleanPrice
			);

			adblOptimalExerciseOASGap[iPath] = adblOptimalExerciseOAS[iPath] - dblOASSpot;

			System.out.println (
				"\t|| " +
				FormatUtil.FormatDouble (iPath, 2, 0, 1.) + " => " +
				FormatUtil.FormatDouble (aiOptimalExerciseVertexIndex[iPath], 2, 0, 1.) + " | " +
				adtOptimalExerciseDate[iPath] + " | " +
				FormatUtil.FormatDouble (adblOptimalExercisePrice[iPath], 3, 2, 100.) + " | " +
				FormatUtil.FormatDouble (adblOptimalExercisePV[iPath], 2, 1, 100.) + " | " +
				FormatUtil.FormatDouble (adblOptimalExerciseOAS[iPath], 3, 0, 10000.) + " | " +
				FormatUtil.FormatDouble (adblOptimalExerciseOASGap[iPath], 3, 0, 10000.) + " | " +
				FormatUtil.FormatDouble (adblOptimalExerciseDuration[iPath], 2, 2, 10000.)  + " | " +
				FormatUtil.FormatDouble (adblOptimalExerciseConvexity[iPath], 1, 2, 1000000.) + " ||"
			);
		}

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

		System.out.println();

		UnivariateDiscreteThin udtOptimalExercisePrice = new UnivariateDiscreteThin (adblOptimalExercisePrice);

		UnivariateDiscreteThin udtOptimalExercisePV = new UnivariateDiscreteThin (adblOptimalExercisePV);

		UnivariateDiscreteThin udtOptimalExerciseOAS = new UnivariateDiscreteThin (adblOptimalExerciseOAS);

		UnivariateDiscreteThin udtOptimalExerciseOASGap = new UnivariateDiscreteThin (adblOptimalExerciseOASGap);

		UnivariateDiscreteThin udtOptimalExerciseDuration = new UnivariateDiscreteThin (adblOptimalExerciseDuration);

		UnivariateDiscreteThin udtOptimalExerciseConvexity = new UnivariateDiscreteThin (adblOptimalExerciseConvexity);

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

		System.out.println ("\t||        Optimal Exercise Price                               ||");

		System.out.println ("\t||        Optimal Exercise Value                               ||");

		System.out.println ("\t||        Optimal Exercise OAS                                 ||");

		System.out.println ("\t||        Optimal Exercise OAS Gap                             ||");

		System.out.println ("\t||        Optimal Exercise Duration                            ||");

		System.out.println ("\t||        Optimal Exercise Convexity                           ||");

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

		System.out.println ("\t|| AVERAGE => " +
			FormatUtil.FormatDouble (udtOptimalExercisePrice.average(), 3, 2, 100.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExercisePV.average(), 2, 1, 100.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExerciseOAS.average(), 3, 1, 10000.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExerciseOASGap.average(), 3, 0, 10000.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExerciseDuration.average(), 2, 2, 10000.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExerciseConvexity.average(), 1, 2, 1000000.) + " ||"
		);

		System.out.println ("\t||  ERROR  => " +
			FormatUtil.FormatDouble (udtOptimalExercisePrice.error(), 3, 2, 100.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExercisePV.error(), 2, 1, 100.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExerciseOAS.error(), 3, 1, 10000.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExerciseOASGap.error(), 3, 0, 10000.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExerciseDuration.error(), 2, 2, 10000.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExerciseConvexity.error(), 1, 2, 1000000.) + " ||"
		);

		System.out.println ("\t|| MAXIMUM => " +
			FormatUtil.FormatDouble (udtOptimalExercisePrice.maximum(), 3, 2, 100.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExercisePV.maximum(), 2, 1, 100.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExerciseOAS.maximum(), 3, 1, 10000.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExerciseOASGap.maximum(), 3, 0, 10000.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExerciseDuration.maximum(), 2, 2, 10000.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExerciseConvexity.maximum(), 1, 2, 1000000.) + " ||"
		);

		System.out.println ("\t|| MINIMUM => " +
			FormatUtil.FormatDouble (udtOptimalExercisePrice.minimum(), 3, 2, 100.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExercisePV.minimum(), 2, 1, 100.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExerciseOAS.minimum(), 3, 1, 10000.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExerciseOASGap.minimum(), 3, 0, 10000.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExerciseDuration.minimum(), 2, 2, 10000.) + " | " +
			FormatUtil.FormatDouble (udtOptimalExerciseConvexity.minimum(), 1, 2, 1000000.) + " ||"
		);

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

		System.out.println();

		EnvManager.TerminateEnv();
	}
}