Financial Toolbox User's Guide

Some Fundamentals -- Instruments In Brief -- Notations: Conventions Used In This Book -- Concepts Of Yield -- Interest-payment Conventions And Day-count Fractions -- Discount Paper: Calculations -- Interest-bearing Paper: Calculations -- Discount Paper: Applications -- Interest-bearing Paper: Applications -- Bonds: Measures Of Yield And Other Basics -- Bond Equation -- Carry Calculations: Long And Short Positions -- Duration -- Convexity -- Uses Of Duration And Convexity -- Covered Interest Arbitrage -- Floating-rate Instruments -- Fixed-income Securities Worldwide: Calculations. Marcia Stigum And Franklin L. Robinson. Rev. Ed. Of: Money Market Calculations. C1981. Includes Index. Getting Started Financial Toolbox Product Description Expected Users Analyze Sets of Numbers Using Matrix Functions Introduction Key Definitions Referencing Matrix Elements Transposing Matrices Matrix Algebra Refresher Introduction Adding and Subtracting Matrices Multiplying Matrices Dividing Matrices Solving Simultaneous Linear Equations Operating Element by Element Using Input and Output Arguments with Functions Input Arguments Output Arguments Performing Common Financial Tasks Handle and Convert Dates Date Formats Date Conversions Current Date and Time Determining Specific Dates Determining Holidays Determining Cash-Flow Dates Charting Financial Data Introduction Bollinger Chart Analyzing and Computing Cash Flows Introduction Interest Rates/Rates of Return Present or Future Values Depreciation Annuities Pricing and Computing Yields for Fixed-Income Securities Introduction Fixed-Income Terminology Framework Default Parameter Values Coupon Date Calculations Yield Conventions Pricing Functions Yield Functions Fixed-Income Sensitivities Treasury Bills Defined Computing Treasury Bill Price and Yield Introduction Treasury Bill Repurchase Agreements Treasury Bill Yields Term Structure of Interest Rates Introduction Deriving an Implied Zero Curve Returns with Negative Prices Negative Price Conversion Analysis of Negative Price Returns Visualization of Complex Returns Conclusion Pricing and Analyzing Equity Derivatives Introduction Sensitivity Measures Analysis Models About Life Tables Life Tables Theory Case Study for Life Tables Analysis Machine Learning for Statistical Arbitrage: Introduction Machine Learning for Statistical Arbitrage I: Data Management and Visualization Machine Learning for Statistical Arbitrage II: Feature Engineering and Model Development Machine Learning for Statistical Arbitrage III: Training, Tuning, and Prediction Portfolio Analysis Analyzing Portfolios Portfolio Optimization Functions Portfolio Construction Examples Introduction Efficient Frontier Example Portfolio Selection and Risk Aversion Introduction Optimal Risky Portfolio portopt Migration to Portfolio Object Migrate portopt Without Output Arguments Migrate portopt with Output Arguments Migrate portopt for Target Returns Within Range of Efficient Portfolio Returns Migrate portopt for Target Return Outside Range of Efficient Portfolio Returns Migrate portopt Using portcons Output for ConSet Integrate Output from portcons, pcalims, pcglims, and pcgcomp with a Portfolio Object frontcon Migration to Portfolio Object Migrate frontcon Without Output Arguments Migrate frontcon with Output Arguments Migrate frontcon for Target Returns Within Range of Efficient Portfolio Returns Migrate frontcon for Target Returns Outside Range of Efficient Portfolio Returns Migrate frontcon Syntax When Using Bounds Migrate frontcon Syntax When Using Groups Constraint Specification Using a Portfolio Object Constraints for Efficient Frontier Linear Constraint Equations Specifying Group Constraints Active Returns and Tracking Error Efficient Frontier Mean-Variance Portfolio Optimization Tools Portfolio Optimization Theory Portfolio Optimization Problems Portfolio Problem Specification Return Proxy Risk Proxy Portfolio Set for Optimization Using Portfolio Objects Linear Inequality Constraints Linear Equality Constraints 'Simple' Bound Constraints 'Conditional' Bound Constraints Budget Constraints Group Constraints Group Ratio Constraints Average Turnover Constraints One-Way Turnover Constraints Tracking Error Constraints Cardinality Constraints Default Portfolio Problem Portfolio Object Workflow Portfolio Object Portfolio Object Properties and Functions Working with Portfolio Objects Setting and Getting Properties Displaying Portfolio Objects Saving and Loading Portfolio Objects Estimating Efficient Portfolios and Frontiers Arrays of Portfolio Objects Subclassing Portfolio Objects Conventions for Representation of Data Creating the Portfolio Object Syntax Portfolio Problem Sufficiency Portfolio Function Examples Common Operations on the Portfolio Object Naming a Portfolio Object Configuring the Assets in the Asset Universe Setting Up a List of Asset Identifiers Truncating and Padding Asset Lists Setting Up an Initial or Current Portfolio Setting Up a Tracking Portfolio Asset Returns and Moments of Asset Returns Using Portfolio Object Assignment Using the Portfolio Function Assignment Using the setAssetMoments Function Scalar Expansion of Arguments Estimating Asset Moments from Prices or Returns Estimating Asset Moments with Missing Data Estimating Asset Moments from Time Series Data Working with a Riskless Asset Working with Transaction Costs Setting Transaction Costs Using the Portfolio Function Setting Transaction Costs Using the setCosts Function Setting Transaction Costs with Scalar Expansion Working with Portfolio Constraints Using Defaults Setting Default Constraints for Portfolio Weights Using Portfolio Object Working with 'Simple' Bound Constraints Using Portfolio Object Setting 'Simple' Bounds Using the Portfolio Function Setting 'Simple' Bounds Using the setBounds Function Setting 'Simple' Bounds Using the Portfolio Function or setBounds Function Working with Budget Constraints Using Portfolio Object Setting Budget Constraints Using the Portfolio Function Setting Budget Constraints Using the setBudget Function Working with Group Constraints Using Portfolio Object Setting Group Constraints Using the Portfolio Function Setting Group Constraints Using the setGroups and addGroups Functions Working with Group Ratio Constraints Using Portfolio Object Setting Group Ratio Constraints Using the Portfolio Function Setting Group Ratio Constraints Using the setGroupRatio and addGroupRatio Functions Working with Linear Equality Constraints Using Portfolio Object Setting Linear Equality Constraints Using the Portfolio Function Setting Linear Equality Constraints Using the setEquality and addEquality Functions Working with Linear Inequality Constraints Using Portfolio Object Setting Linear Inequality Constraints Using the Portfolio Function Setting Linear Inequality Constraints Using the setInequality and addInequality Functions Working with 'Conditional' BoundType, MinNumAssets, and MaxNumAssets Constraints Using Portfolio Objects Setting 'Conditional' BoundType Constraints Using the setBounds Function Setting the Limits on the Number of Assets Invested Using the setMinMaxNumAssets Function Working with Average Turnover Constraints Using Portfolio Object Setting Average Turnover Constraints Using the Portfolio Function Setting Average Turnover Constraints Using the setTurnover Function Working with One-Way Turnover Constraints Using Portfolio Object Setting One-Way Turnover Constraints Using the Portfolio Function Setting Turnover Constraints Using the setOneWayTurnover Function Working with Tracking Error Constraints Using Portfolio Object Setting Tracking Error Constraints Using the Portfolio Function Setting Tracking Error Constraints Using the setTrackingError Function Validate the Portfolio Problem for Portfolio Object Validating a Portfolio Set Validating Portfolios Estimate Efficient Portfolios for Entire Efficient Frontier for Portfolio Object Obtaining Portfolios Along the Entire Efficient Frontier Obtaining Endpoints of the Efficient Frontier Obtaining Efficient Portfolios for Target Returns Obtaining Efficient Portfolios for Target Risks Efficient Portfolio That Maximizes Sharpe Ratio Choosing and Controlling the Solver for Mean-Variance Portfolio Optimization Using 'lcprog' and 'quadprog' Using the Mixed Integer Nonlinear Programming (MINLP) Solver Solver Guidelines for Portfolio Objects Solver Guidelines for Custom Objective Problems Using Portfolio Objects Estimate Efficient Frontiers for Portfolio Object Obtaining Portfolio Risks and Returns Plotting the Efficient Frontier for a Portfolio Object Plotting Existing Efficient Portfolios Plotting Existing Efficient Portfolio Risks and Returns Postprocessing Results to Set Up Tradable Portfolios Setting Up Tradable Portfolios When to Use Portfolio Objects Over Optimization Toolbox Always Use Portfolio, PortfolioCVaR, or PortfolioMAD Object Preferred Use of Portfolio, PortfolioCVaR, or PortfolioMAD Object Use Optimization Toolbox Troubleshooting Portfolio Optimization Results Portfolio Object Destroyed When Modifying Optimization Fails with “Bad Pivot” Message Speed of Optimization Matrix Incompatibility and "Non-Conformable" Errors Missing Data Estimation Fails mv_optim_transform Errors solveContinuousCustomObjProb or solveMICustomObjProb Errors Efficient Portfolios Do Not Make Sense Efficient Frontiers Do Not Make Sense Troubleshooting estimateCustomObjectivePortfolio Troubleshooting for Setting 'Conditional' BoundType, MinNumAssets, and MaxNumAssets Constraints Role of Convexity in Portfolio Problems Examples of Convex Functions Examples of Concave Functions Examples of Nonconvex Functions Portfolio Optimization Examples Using Financial ToolboxTM Asset Allocation Case Study Portfolio Optimization with Semicontinuous and Cardinality Constraints Portfolio Optimization Against a Benchmark Portfolio Analysis with Turnover Constraints Leverage in Portfolio Optimization with a Risk-Free Asset Black-Litterman Portfolio Optimization Using Financial ToolboxTM Portfolio Optimization Using Factor Models Backtest Investment Strategies Using Financial ToolboxTM Backtest Investment Strategies with Trading Signals Portfolio Optimization Using Social Performance Measure Diversify ESG Portfolios Risk Budgeting Portfolio Backtest Using Risk-Based Equity Indexation Create Hierarchical Risk Parity Portfolio Backtest Strategies Using Deep Learning Analyze Performance Attribution Using Brinson Model Diversify Portfolios Using Custom Objective Solve Problem for Minimum Variance Portfolio with Tracking Error Penalty Solve Problem for Minimum Tracking Error with Net Return Constraint Solve Robust Portfolio Maximum Return Problem with Ellipsoidal Uncertainty Risk Parity or Budgeting with Constraints CVaR Portfolio Optimization Tools Portfolio Optimization Theory Portfolio Optimization Problems Portfolio Problem Specification Return Proxy Risk Proxy Portfolio Set for Optimization Using PortfolioCVaR Object Linear Inequality Constraints Linear Equality Constraints 'Simple' Bound Constraints 'Conditional' Bound Constraints Budget Constraints Group Constraints Group Ratio Constraints Average Turnover Constraints One-way Turnover Constraints Cardinality Constraints Default Portfolio Problem PortfolioCVaR Object Workflow PortfolioCVaR Object PortfolioCVaR Object Properties and Functions Working with PortfolioCVaR Objects Setting and Getting Properties Displaying PortfolioCVaR Objects Saving and Loading PortfolioCVaR Objects Estimating Efficient Portfolios and Frontiers Arrays of PortfolioCVaR Objects Subclassing PortfolioCVaR Objects Conventions for Representation of Data Creating the PortfolioCVaR Object Syntax PortfolioCVaR Problem Sufficiency PortfolioCVaR Function Examples Common Operations on the PortfolioCVaR Object Naming a PortfolioCVaR Object Configuring the Assets in the Asset Universe Setting Up a List of Asset Identifiers Truncating and Padding Asset Lists Setting Up an Initial or Current Portfolio Asset Returns and Scenarios Using PortfolioCVaR Object How Stochastic Optimization Works What Are Scenarios? Setting Scenarios Using the PortfolioCVaR Function Setting Scenarios Using the setScenarios Function Estimating the Mean and Covariance of Scenarios Simulating Normal Scenarios Simulating Normal Scenarios from Returns or Prices Simulating Normal Scenarios with Missing Data Simulating Normal Scenarios from Time Series Data Simulating Normal Scenarios with Mean and Covariance Working with a Riskless Asset Working with Transaction Costs Setting Transaction Costs Using the PortfolioCVaR Function Setting Transaction Costs Using the setCosts Function Setting Transaction Costs with Scalar Expansion Working with CVaR Portfolio Constraints Using Defaults Setting Default Constraints for Portfolio Weights Using PortfolioCVaR Object Working with 'Simple' Bound Constraints Using PortfolioCVaR Object Setting 'Simple' Bounds Using the PortfolioCVaR Function Setting 'Simple' Bounds Using the setBounds Function Setting 'Simple' Bounds Using the PortfolioCVaR Function or setBounds Function Working with Budget Constraints Using PortfolioCVaR Object Setting Budget Constraints Using the PortfolioCVaR Function Setting Budget Constraints Using the setBudget Function Working with Group Constraints Using PortfolioCVaR Object Setting Group Constraints Using the PortfolioCVaR Function Setting Group Constraints Using the setGroups and addGroups Functions Working with Group Ratio Constraints Using PortfolioCVaR Object Setting Group Ratio Constraints Using the PortfolioCVaR Function Setting Group Ratio Constraints Using the setGroupRatio and addGroupRatio Functions Working with Linear Equality Constraints Using PortfolioCVaR Object Setting Linear Equality Constraints Using the PortfolioCVaR Function Setting Linear Equality Constraints Using the setEquality and addEquality Functions Working with Linear Inequality Constraints Using PortfolioCVaR Object Setting Linear Inequality Constraints Using the PortfolioCVaR Function Setting Linear Inequality Constraints Using the setInequality and addInequality Functions Working with 'Conditional' BoundType, MinNumAssets, and MaxNumAssets Constraints Using PortfolioCVaR Objects Setting 'Conditional' BoundType Constraints Using the setBounds Function Setting the Limits on the Number of Assets Invested Using the setMinMaxNumAssets Function Working with Average Turnover Constraints Using PortfolioCVaR Object Setting Average Turnover Constraints Using the PortfolioCVaR Function Setting Average Turnover Constraints Using the setTurnover Function Working with One-Way Turnover Constraints Using PortfolioCVaR Object Setting One-Way Turnover Constraints Using the PortfolioCVaR Function Setting Turnover Constraints Using the setOneWayTurnover Function Validate the CVaR Portfolio Problem Validating a CVaR Portfolio Set Validating CVaR Portfolios Estimate Efficient Portfolios for Entire Frontier for PortfolioCVaR Object Obtaining Portfolios Along the Entire Efficient Frontier Obtaining Endpoints of the Efficient Frontier Obtaining Efficient Portfolios for Target Returns Obtaining Efficient Portfolios for Target Risks Choosing and Controlling the Solver for PortfolioCVaR Optimizations Using 'TrustRegionCP', 'ExtendedCP', and 'cuttingplane' SolverTypes Using 'fmincon' SolverType Using the Mixed Integer Nonlinear Programming (MINLP) Solver Solver Guidelines for PortfolioCVaR Objects Estimate Efficient Frontiers for PortfolioCVaR Object Obtaining CVaR Portfolio Risks and Returns Obtaining Portfolio Standard Deviation and VaR Plotting the Efficient Frontier for a PortfolioCVaR Object Plotting Existing Efficient Portfolios Plotting Existing Efficient Portfolio Risks and Returns Postprocessing Results to Set Up Tradable Portfolios Setting Up Tradable Portfolios Working with Other Portfolio Objects Troubleshooting CVaR Portfolio Optimization Results PortfolioCVaR Object Destroyed When Modifying Matrix Incompatibility and "Non-Conformable" Errors CVaR Portfolio Optimization Warns About “Max Iterations” CVaR Portfolio Optimization Errors with “Could Not Solve” Message Missing Data Estimation Fails cvar_optim_transform Errors Efficient Portfolios Do Not Make Sense Hedging Using CVaR Portfolio Optimization Compute Maximum Reward-to-Risk Ratio for CVaR Portfolio MAD Portfolio Optimization Tools Portfolio Optimization Theory Portfolio Optimization Problems Portfolio Problem Specification Return Proxy Risk Proxy Portfolio Set for Optimization Using PortfolioMAD Object Linear Inequality Constraints Linear Equality Constraints 'Simple' Bound Constraints 'Conditional' Bound Constraints Budget Constraints Group Constraints Group Ratio Constraints Average Turnover Constraints One-way Turnover Constraints Cardinality Constraints Default Portfolio Problem PortfolioMAD Object Workflow PortfolioMAD Object PortfolioMAD Object Properties and Functions Working with PortfolioMAD Objects Setting and Getting Properties Displaying PortfolioMAD Objects Saving and Loading PortfolioMAD Objects Estimating Efficient Portfolios and Frontiers Arrays of PortfolioMAD Objects Subclassing PortfolioMAD Objects Conventions for Representation of Data Creating the PortfolioMAD Object Syntax PortfolioMAD Problem Sufficiency PortfolioMAD Function Examples Common Operations on the PortfolioMAD Object Naming a PortfolioMAD Object Configuring the Assets in the Asset Universe Setting Up a List of Asset Identifiers Truncating and Padding Asset Lists Setting Up an Initial or Current Portfolio Asset Returns and Scenarios Using PortfolioMAD Object How Stochastic Optimization Works What Are Scenarios? Setting Scenarios Using the PortfolioMAD Function Setting Scenarios Using the setScenarios Function Estimating the Mean and Covariance of Scenarios Simulating Normal Scenarios Simulating Normal Scenarios from Returns or Prices Simulating Normal Scenarios with Missing Data Simulating Normal Scenarios from Time Series Data Simulating Normal Scenarios for Mean and Covariance Working with a Riskless Asset Working with Transaction Costs Setting Transaction Costs Using the PortfolioMAD Function Setting Transaction Costs Using the setCosts Function Setting Transaction Costs with Scalar Expansion Working with MAD Portfolio Constraints Using Defaults Setting Default Constraints for Portfolio Weights Using PortfolioMAD Object Working with 'Simple' Bound Constraints Using PortfolioMAD Object Setting 'Simple' Bounds Using the PortfolioMAD Function Setting 'Simple' Bounds Using the setBounds Function Setting 'Simple' Bounds Using the PortfolioMAD Function or setBounds Function Working with Budget Constraints Using PortfolioMAD Object Setting Budget Constraints Using the PortfolioMAD Function Setting Budget Constraints Using the setBudget Function Working with Group Constraints Using PortfolioMAD Object Setting Group Constraints Using the PortfolioMAD Function Setting Group Constraints Using the setGroups and addGroups Functions Working with Group Ratio Constraints Using PortfolioMAD Object Setting Group Ratio Constraints Using the PortfolioMAD Function Setting Group Ratio Constraints Using the setGroupRatio and addGroupRatio Functions Working with Linear Equality Constraints Using PortfolioMAD Object Setting Linear Equality Constraints Using the PortfolioMAD Function Setting Linear Equality Constraints Using the setEquality and addEquality Functions Working with Linear Inequality Constraints Using PortfolioMAD Object Setting Linear Inequality Constraints Using the PortfolioMAD Function Setting Linear Inequality Constraints Using the setInequality and addInequality Functions Working with 'Conditional' BoundType, MinNumAssets, and MaxNumAssets Constraints Using PortfolioMAD Objects Setting 'Conditional' BoundType Constraints Using the setBounds Function Setting the Limits on the Number of Assets Invested Using the setMinMaxNumAssets Function Working with Average Turnover Constraints Using PortfolioMAD Object Setting Average Turnover Constraints Using the PortfolioMAD Function Setting Average Turnover Constraints Using the setTurnover Function Working with One-Way Turnover Constraints Using PortfolioMAD Object Setting One-Way Turnover Constraints Using the PortfolioMAD Function Setting Turnover Constraints Using the setOneWayTurnover Function Validate the MAD Portfolio Problem Validating a MAD Portfolio Set Validating MAD Portfolios Estimate Efficient Portfolios Along the Entire Frontier for PortfolioMAD Object Obtaining Portfolios Along the Entire Efficient Frontier Obtaining Endpoints of the Efficient Frontier Obtaining Efficient Portfolios for Target Returns Obtaining Efficient Portfolios for Target Risks Choosing and Controlling the Solver for PortfolioMAD Optimizations Using 'TrustRegionCP' and 'ExtendedCP' SolverTypes Using 'fmincon' SolverType Using the Mixed Integer Nonlinear Programming (MINLP) Solver Solver Guidelines for PortfolioMAD Objects Estimate Efficient Frontiers for PortfolioMAD Object Obtaining MAD Portfolio Risks and Returns Obtaining the PortfolioMAD Standard Deviation Plotting the Efficient Frontier for a PortfolioMAD Object Plotting Existing Efficient Portfolios Plotting Existing Efficient Portfolio Risks and Returns Postprocessing Results to Set Up Tradable Portfolios Setting Up Tradable Portfolios Working with Other Portfolio Objects Troubleshooting MAD Portfolio Optimization Results PortfolioMAD Object Destroyed When Modifying Matrix Incompatibility and "Non-Conformable" Errors Missing Data Estimation Fails mad_optim_transform Errors Efficient Portfolios Do Not Make Sense Investment Performance Metrics Performance Metrics Overview Performance Metrics Types Performance Metrics Illustration Using the Sharpe Ratio Introduction Sharpe Ratio Using the Information Ratio Introduction Information Ratio Using Tracking Error Introduction Tracking Error Using Risk-Adjusted Return Introduction Risk-Adjusted Return Using Sample and Expected Lower Partial Moments Introduction Sample Lower Partial Moments Expected Lower Partial Moments Using Maximum and Expected Maximum Drawdown Introduction Maximum Drawdown Expected Maximum Drawdown Credit Risk Analysis Estimation of Transition Probabilities Introduction Estimate Transition Probabilities Estimate Transition Probabilities for Different Rating Scales Working with a Transition Matrix Containing NR Rating Estimate Point-in-Time and Through-the-Cycle Probabilities Estimate t-Year Default Probabilities Estimate Bootstrap Confidence Intervals Group Credit Ratings Work with Nonsquare Matrices Remove Outliers Estimate Probabilities for Different Segments Work with Large Datasets Forecasting Corporate Default Rates Credit Quality Thresholds Introduction Compute Credit Quality Thresholds Visualize Credit Quality Thresholds About Credit Scorecards What Is a Credit Scorecard? Credit Scorecard Development Process Credit Scorecard Modeling Workflow Credit Scorecard Modeling Using Observation Weights Credit Scorecard Modeling with Missing Values Troubleshooting Credit Scorecard Results Predictor Name Is Unspecified and the Parser Returns an Error Using bininfo or plotbins Before Binning If Categorical Data Is Given as Numeric NaNs Returned When Scoring a “Test” Dataset Case Study for Credit Scorecard Analysis Credit Scorecards with Constrained Logistic Regression Coefficients Credit Default Swap (CDS) Bootstrapping a Default Probability Curve Finding Breakeven Spread for New CDS Contract Valuing an Existing CDS Contract Converting from Running to Upfront Bootstrapping from Inverted Market Curves Visualize Transitions Data for transprob Impute Missing Data in the Credit Scorecard Workflow Using the k-Nearest Neighbors Algorithm Impute Missing Data in the Credit Scorecard Workflow Using the Random Forest Algorithm Treat Missing Data in a Credit Scorecard Workflow Using MATLAB® fillmissing Regression with Missing Data Multivariate Normal Regression Introduction Multivariate Normal Linear Regression Maximum Likelihood Estimation Special Case of Multiple Linear Regression Model Least-Squares Regression Mean and Covariance Estimation Convergence Fisher Information Statistical Tests Maximum Likelihood Estimation with Missing Data Introduction ECM Algorithm Standard Errors Data Augmentation Multivariate Normal Regression Functions Multivariate Normal Regression Without Missing Data Multivariate Normal Regression With Missing Data Least-Squares Regression With Missing Data Multivariate Normal Parameter Estimation With Missing Data Support Functions Multivariate Normal Regression Types Regressions Multivariate Normal Regression Multivariate Normal Regression Without Missing Data Multivariate Normal Regression With Missing Data Least-Squares Regression Least-Squares Regression Without Missing Data Least-Squares Regression With Missing Data Covariance-Weighted Least Squares Covariance-Weighted Least Squares Without Missing Data Covariance-Weighted Least Squares With Missing Data Feasible Generalized Least Squares Feasible Generalized Least Squares Without Missing Data Feasible Generalized Least Squares With Missing Data Seemingly Unrelated Regression Seemingly Unrelated Regression Without Missing Data Seemingly Unrelated Regression With Missing Data Mean and Covariance Parameter Estimation Troubleshooting Multivariate Normal Regression Biased Estimates Requirements Slow Convergence Nonrandom Residuals Nonconvergence Portfolios with Missing Data Valuation with Missing Data Introduction Capital Asset Pricing Model Estimation of the CAPM Estimation with Missing Data Estimation of Some Technology Stock Betas Grouped Estimation of Some Technology Stock Betas References Capital Asset Pricing Model with Missing Data Solving Sample Problems Sensitivity of Bond Prices to Interest Rates Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Step 7 Bond Portfolio for Hedging Duration and Convexity Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Bond Prices and Yield Curve Parallel Shifts Bond Prices and Yield Curve Nonparallel Shifts Greek-Neutral Portfolios of European Stock Options Step 1 Step 2 Step 3 Step 4 Term Structure Analysis and Interest-Rate Swaps Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Plotting an Efficient Frontier Using portopt Plotting Sensitivities of an Option Plotting Sensitivities of a Portfolio of Options Bond Portfolio Optimization Using Portfolio Object Hedge Options Using Reinforcement Learning ToolboxTM Financial Time Series Analysis Creating Financial Time Series Objects Introduction Using the Constructor Transforming a Text File Visualizing Financial Time Series Objects Introduction Using chartfts Zoom Tool Combine Axes Tool Using Financial Timetables Convert Financial Time Series Objects fints to Timetables Create Time Series Index an Object Transform Time Series Convert Time Series Merge Time Series Analyze Time Series Data Extraction Use Timetables in Finance Using Financial Time Series Working with Financial Time Series Objects Introduction Financial Time Series Object Structure Data Extraction Object-to-Matrix Conversion Financial Time Series Operations Basic Arithmetic Operations with Objects and Matrices Arithmetic Operations with Differing Data Series Names Other Arithmetic Operations Data Transformation and Frequency Conversion Indexing a Financial Time Series Object Indexing with Date Character Vectors Indexing with Date Character Vector Range Indexing with Integers Indexing When Time-of-Day Data Is Present Trading Date Utilities Trading Calendars User Interface UICalendar User Interface Using UICalendar in Standalone Mode Using UICalendar with an Application Technical Analysis Technical Indicators Technical Analysis Examples Overview Moving Average Convergence/Divergence (MACD) Williams %R Relative Strength Index (RSI) On-Balance Volume (OBV) Stochastic Differential Equations SDEs SDE Modeling Trials vs. Paths NTrials, NPeriods, and NSteps SDE Class Hierarchy SDE Models Introduction Creating SDE Objects Drift and Diffusion Available Models SDE Simulation and Interpolation Methods Base SDE Models Overview Example: Base SDE Models Drift and Diffusion Models Overview Example: Drift and Diffusion Rates Example: SDEDDO Models Linear Drift Models Overview Example: SDELD Models Parametric Models Creating Brownian Motion (BM) Models Example: BM Models Creating Constant Elasticity of Variance (CEV) Models Creating Geometric Brownian Motion (GBM) Models Creating Stochastic Differential Equations from Mean-Reverting Drift (SDEMRD) Models Creating Cox-Ingersoll-Ross (CIR) Square Root Diffusion Models Creating Hull-White/Vasicek (HWV) Gaussian Diffusion Models Creating Heston Stochastic Volatility Models Simulating Equity Prices Simulating Multidimensional Market Models Inducing Dependence and Correlation Dynamic Behavior of Market Parameters Pricing Equity Options Simulating Interest Rates Simulating Interest Rates Using Interpolation Ensuring Positive Interest Rates Stratified Sampling Quasi-Monte Carlo Simulation Performance Considerations Managing Memory Enhancing Performance Optimizing Accuracy: About Solution Precision and Error Price American Basket Options Using Standard Monte Carlo and Quasi-Monte Carlo Simulation Improving Performance of Monte Carlo Simulation with Parallel Computing Functions brinsonAttribution categoryAttribution categoryReturns categoryWeights totalAttribution summary bm cev cir diffusion drift gbm simBySolution simBySolution heston hwv interpolate sde sdeddo sdeld sdemrd bates merton backtestStrategy backtestEngine runBacktest summary equityCurve simByEuler simByEuler simBySolution simByQuadExp simByEuler simByTransition simulate ts2func abs2active accrfrac acrubond acrudisc active2abs addEquality addGroupRatio addGroups addInequality adline adosc amortize annurate annuterm arith2geom ascii2fts bar, barh bar3, bar3h beytbill binprice blkimpv blkprice blsdelta blsgamma blsimpv blslambda blsprice blsrho blstheta blsvega bondDefaultBootstrap bndconvp bndconvy bnddurp bnddury bndkrdur bndprice bndspread bndtotalreturn bndyield bolling bollinger boxcox busdate busdays candle candle (fts) cdai cdprice cdsbootstrap cdsprice cdsspread cdsrpv01 creditexposures exposureprofiles cdyield cfamounts cfconv cfdates cfdatesq cfdur cfplot cfport cfprice cfspread cfyield cftimes chaikosc chaikvolat chartfts checkFeasibility chfield convert2sur convertto corrcoef corr2cov cov cov2corr cpncount cpndaten cpndatenq cpndatepq cpndatep cpndaysn cpndaysp cpnpersz createholidays cumsum cur2frac cur2str date2time dateaxis datedisp datefind datemnth datewrkdy days252bus days360 days360e days360isda days360psa days365 daysact daysadd daysdif dec2thirtytwo depfixdb depgendb Written as a companion to Marcia Stigum's classic, The Money Market (3rd Ed., 1990), Money Market and Bond Calculations provides precise and thorough explanations for valuing fixed-income instruments throughout the world. The authors present an economical, consistent and easy-to-remember notation, one designed to make their equations simple to grasp and to manipulate. (For the reader's convenience, this notation is reproduced on the front and back papers of the book.) The authors also discuss interest-payment conventions and day-count fractions, topics that are crucial to understanding the pricing of various instruments covered in this book. Filled with examples that assume yields ranging from low to high, Money Market and Bond Calculations addresses: important price/yield relationships for discount and interest-bearing money market paper and examples of how these relationships can be used in practical and common situations to derive breakeven and other key numbers; commonly used concepts of yield and the standard bond equation, the equation most commonly used to make price/yield calculations for notes and bonds; more advanced topics regarding bonds: carry, various measures of duration, convexity and the ways in which the latter two measures of risk can be applied in putting on arbitrages and in portfolio management; and fixed-income securities worldwide, including covered interest arbitrage, floating-rate notes (FRNs), payment-in-kind bonds (PIKs) and descriptions of and calculations for the sovereign debt issued of major countries. Money and bond markets offer enormous opportunities for all market players - Money Market and Bond Calculations will help the reader profit from these opportunities