Financial Modeling

Höfundar: Simon Benninga; Tal Mofkadi (Útgáfa: 5)
Financial Modeling

Kaup valmöguleikar

A substantially updated new edition of the essential text on financial modeling, with revised material, new data, and implementations shown in Excel, R, and Python. Financial Modeling has become the gold-standard text in its field, an essential guide for students, researchers, and practitioners that provides the computational tools needed for modeling finance fundamentals. This fifth edition has been substantially updated but maintains the straightforward, hands-on approach, with an optimal mix of explanation and implementation, that made the previous editions so popular.

Using detailed Excel spreadsheets, it explains basic and advanced models in the areas of corporate finance, portfolio management, options, and bonds. This new edition offers revised material on valuation, second-order and third-order Greeks for options, value at risk (VaR), Monte Carlo methods, and implementation in R. The examples and implementation use up-to-date and relevant data. Parts I to V cover corporate finance topics, bond and yield curve models, portfolio theory, options and derivatives, and Monte Carlo methods and their implementation in finance.

Parts VI and VII treat technical topics, with part VI covering Excel and R issues and part VII (now on the book’s auxiliary website) covering Excel’s programming language, Visual Basic for Applications (VBA), and Python implementations. Knowledge of technical chapters on VBA and R is not necessary for understanding the material in the first five parts. The book is suitable for use in advanced finance classes that emphasize the need to combine modeling skills with a deeper knowledge of the underlying financial models.

Nánar um bókina

Útgefandi
Random House Publishing Services
ISBN
9780262368247
Print ISBN
9780262046428
Format
ePub
Útgáfa
5
Höfundar
Simon Benninga; Tal Mofkadi
Tungumál
English
Útgefið
2022-02-08
Prent takmörkun á líftíma
100
Prent takmörkun
2
Afritunar takmörkun
2

Kaflar

  • Title Page
  • Copyright
  • Dedication
  • Table of Contents
  • Preface and Acknowledgments
  • Before All Else
  • 0.1. Data Tables
  • 0.2. What Is Getformula?
  • 0.3. How to Put Getformula into Your Excel Notebook
  • 0.4. Saving the Excel Workbook: Windows
  • 0.5. Saving the Excel Workbook: Mac
  • 0.6. Do You Have to Put Getformula into Each Excel Workbook?
  • 0.7. Using Formulatext() Instead of Getformula
  • 0.8. A Shortcut to Use Getformula and Formulatext
  • 0.9. Recording Getformula: The Windows Case
  • 0.10. Recording Getformula: The Mac Case
  • 0.11. Using R
  • I. Corporate Finance
  • 1. Basic Financial Analysis
  • 1.1. Overview
  • 1.2. Present Value and Net Present Value
  • 1.3. The Internal Rate of Return (IRR) and Loan Tables
  • 1.4. Multiple Internal Rates of Return
  • 1.5. Flat Payment Schedules
  • 1.6. Future Values and Applications
  • 1.7. A Pension Problem—Complicating the Future Value Problem
  • 1.8. Continuous Compounding
  • 1.9. Discounting Using Dated Cash Flows
  • Exercises
  • 2. Corporate Valuation Overview
  • 2.1. Overview
  • 2.2. Three Methods to Compute Enterprise Value (EV)
  • 2.3. Using Accounting Book Values to Value a Company: The Firm’s Accounting Enterprise Value
  • 2.4. The Efficient Markets Approach to Corporate Valuation
  • 2.5. Enterprise Value as the Present Value of the Free Cash Flows: DCF “Top Down” Valuation
  • 2.6. Free Cash Flows Based on Consolidated Statement of Cash Flows
  • 2.7. Free Cash Flows Based on Pro Forma Financial Statements
  • 2.8. Summary
  • Exercises
  • 3. Calculating the Weighted Average Cost of Capital (WACC)
  • 3.1. Overview
  • 3.2. Computing the Value of the Firm’s Equity, E
  • 3.3. Computing the Value of the Firm’s Debt, D
  • 3.4. Computing the Firm’s Tax Rate, TC
  • 3.5. Computing the Firm’s Cost of Debt, rD
  • 3.6. Two Approaches to Computing the Firm’s Cost of Equity, rE
  • 3.7. Three Approaches to Computing the Expected Return on the Market, E(rM)
  • 3.8. What’s the Risk-Free Rate rf in the CAPM?
  • 3.9. Computing the WACC
  • 3.10. When Don’t the Models Work?
  • 3.11. Summary
  • Exercises
  • 4. Pro Forma Analysis and Valuation Based on the Discounted Cash Flow Approach
  • 4.1. Overview
  • 4.2. Setting the Stage—Discounting the Free Cash Flow (FCF)
  • 4.3. Simplified Approach Based on Consolidated Statement of Cash Flows
  • 4.4. Pro Forma Financial Statement Modeling
  • 4.5. Using the FCF to Value the Firm and Its Equity
  • 4.6. Setting the Debt to Be the Absorbing Item and Incorporating Target Debt/Equity Ratio into the Pro Forma
  • 4.7. Calculating the Return on Invested Capital
  • 4.8. Project Finance: Debt Repayment Schedules
  • 4.9. Calculating the Return on Equity
  • 4.10. Tax Loss Carryforwards
  • 4.11. Conclusion
  • Exercises
  • 5. Building a Pro Forma Model: The Case of Merck
  • 5.1. Overview
  • 5.2. Merck’s Financial Statements, 2015–2018
  • 5.3. Analyzing the Financial Statements
  • 5.4. A Model for Merck
  • 5.5. Using the Model to Value Merck
  • 5.6. Valuation Model for Merck Using Multiples
  • 5.7. Summary
  • 6. Financial Analysis of Leasing
  • 6.1. Overview
  • 6.2. A Simple but Misleading Example
  • 6.3. Leasing and Firm Financing—the Equivalent-Loan Method
  • 6.4. The Lessor’s Problem: Calculating the Highest Acceptable Lease Rental
  • 6.5. Asset Residual Value and Other Considerations
  • 6.6. Mini-Case: When Is Leasing Profitable for Both the Lessor and the Lessee?
  • 6.7. Leveraged Leasing
  • 6.8. A Leveraged Lease Example
  • 6.9. Summary
  • Exercises
  • II. Bonds
  • 7. Bond’s Duration
  • 7.1. Overview
  • 7.2. Two Examples
  • 7.3. What Does Duration Mean?
  • 7.4. Duration Patterns
  • 7.5. The Duration of a Bond with Uneven Payments
  • 7.6. Convexity of a Bond
  • 7.7. Immunization Strategies
  • 7.8. Summary
  • Exercises
  • 8. Modeling the Term Structure
  • 8.1. Overview
  • 8.2. The Term Structure of Interest Rates
  • 8.3. Bond Pricing Using the Equivalent Single Bond Approach
  • 8.4. Pricing with Several Bonds at the Same Maturity
  • 8.5. The Nelson-Siegel Approach of Fitting a Functional Form to the Term Structure
  • 8.6. The Properties of the Nelson-Siegel Term Structure
  • 8.7. Term Structure for Treasury Notes
  • 8.8. Summary
  • Appendix: VBA Functions Used in This Chapter
  • 9. Calculating Default-Adjusted Expected Bond Returns
  • 9.1. Overview
  • 9.2. Calculating the Expected Return in a One-Period Framework
  • 9.3. Calculating the Bond Expected Return in a Multi-period Framework
  • 9.4. A Numerical Example
  • 9.5. Experimenting with the Example
  • 9.6. Computing the Bond Expected Return for an Actual Bond
  • 9.7. Semiannual Transition Matrices
  • 9.8. Computing Bond Beta
  • 9.9. Summary
  • Exercises
  • III. Portfolio Theory
  • 10. Portfolio Models—Introduction
  • 10.1. Overview
  • 10.2. Computing Descriptive Statistics for Stocks
  • 10.3. Calculating Portfolio Means and Variances
  • 10.4. Portfolio Mean and Variance—Case of N Assets
  • 10.5. Envelope Portfolios
  • 10.6. Summary
  • Exercises
  • Appendix 10.1: Continuously Compounded versus Geometric Returns
  • Appendix 10.2: Adjusting for Dividends
  • 11. Efficient Portfolios and the Efficient Frontier
  • 11.1. Overview
  • 11.2. Some Preliminary Definitions and Notation
  • 11.3. Five Propositions on Efficient Portfolios and the CAPM
  • 11.4. Calculating the Efficient Frontier: An Example
  • 11.5. Three Notes on the Optimization Procedure
  • 11.6. Finding the Market Portfolio: The Capital Market Line (CML)
  • 11.7. Computing the Global Minimum Variance Portfolio (GMVP)
  • 11.8. Testing the SML—Implementing Propositions 3–5
  • 11.9. Efficient Portfolios without Short Sales
  • 11.10. Summary
  • Exercises
  • Mathematical Appendix
  • 12. Calculating the Variance-Covariance Matrix
  • 12.1. Overview
  • 12.2. Computing the Sample Variance-Covariance Matrix
  • 12.3. The Correlation Matrix
  • 12.4. Four Alternatives to the Sample Variance-Covariance Matrix
  • 12.5. Alternatives to the Sample Variance-Covariance: The Single-Index Model
  • 12.6. Alternatives to the Sample Variance-Covariance: Constant Correlation
  • 12.7. Alternatives to the Sample Variance-Covariance: Shrinkage Methods
  • 12.8. Using Option Information to Compute the Variance Matrix
  • 12.9. Which Method to Compute the Variance-Covariance Matrix?
  • 12.10. Summing Up
  • Exercises
  • 13. Estimating Betas and the Security Market Line
  • 13.1. Overview
  • 13.2. Testing the SML
  • 13.3. Did We Learn Something?
  • 13.4. The Non-efficiency of the “Market Portfolio”
  • 13.5. So What’s the Real Market Portfolio? How Can We Test the CAPM?
  • 13.6. Conclusion: Does the CAPM Have Any Uses?
  • Exercises
  • 14. Event Studies
  • 14.1. Overview
  • 14.2. Outline of an Event Study
  • 14.3. An Initial Event Study: Procter & Gamble Buys Gillette
  • 14.4. A Fuller Event Study: Impact of Earnings Announcements on Stock Prices
  • 14.5. Using a Two-Factor Model of Returns for an Event Study
  • 14.6. Using Excel’s Offset Function to Locate a Regression in a Data Set
  • 14.7. Conclusion
  • 15. The Black-Litterman Approach to Portfolio Optimization
  • 15.1. Overview
  • 15.2. A Naive Problem
  • 15.3. Black and Litterman’s Solution to the Optimization Problem
  • 15.4. BL Step 1: What Does the Market Think?
  • 15.5. BL Step 2: Introducing Opinions—What Does Joanna Think?
  • 15.6. Using BL for International Asset Allocation
  • 15.7. Summary
  • Exercises
  • IV. Options
  • 16. Introduction to Options
  • 16.1. Overview
  • 16.2. Basic Option Definitions and Terminology
  • 16.3. Some Examples
  • 16.4. Option Payoff and Profit Patterns
  • 16.5. Option Strategies: Payoffs from Portfolios of Options and Stocks
  • 16.6. Option Arbitrage Propositions
  • 16.7. Summary
  • Exercises
  • 17. The Binomial Option Pricing Model
  • 17.1. Overview
  • 17.2. Two-Date Binomial Pricing
  • 17.3. The State Prices
  • 17.4. The Multi-period Binomial Model
  • 17.5. Pricing American Options Using the Binomial Pricing Model
  • 17.6. Programming the Binomial Option Pricing Model
  • 17.7. Convergence of Binomial Pricing to the Black-Scholes Price
  • 17.8. Using the Binomial Model to Price Employee Stock Options
  • 17.9. Using the Binomial Model to Price Nonstandard Options: An Example
  • 17.10. Summary
  • Exercises
  • 18. The Black-Scholes Model
  • 18.1. Overview
  • 18.2. The Black-Scholes Model
  • 18.3. Programming the Black-Scholes Option Pricing Model
  • 18.4. Calculating the Volatility
  • 18.5. Programming a Function to Find the Implied Volatility
  • 18.6. Dividend Adjustments to the Black-Scholes
  • 18.7. “Bang for the Buck” with Options
  • 18.8. The Black Model for Bond Option Valuation
  • 18.9. Using the Black-Scholes Model to Price Risky Debt
  • 18.10. Using the Black-Scholes Formula to Price Structured Securities
  • 18.11. Summary
  • Exercises
  • 19. Option Greeks
  • 19.1. Overview
  • 19.2. Defining and Computing the Greeks
  • 19.3. Delta Hedging a Call
  • 19.4. The Greeks of a Portfolio
  • 19.5. Greek-Neutral Portfolio
  • 19.6. The Relationship between Delta, Theta, and Gamma
  • 19.7. Summary
  • Exercises
  • Appendix 19.1: VBA for Greeks
  • Appendix 19.2: R Code for Greeks
  • 20. Real Options
  • 20.1. Overview
  • 20.2. A Simple Example of the Option to Expand
  • 20.3. The Abandonment Option
  • 20.4. Valuing the Abandonment Option as a Series of Puts
  • 20.5. Valuing a Biotechnology Project
  • 20.6. Summary
  • Exercises
  • V. Monte Carlo Methods
  • 21. Generating and Using Random Numbers
  • 21.1. Overview
  • 21.2. Rand() and Rnd: The Excel and VBA Random-Number Generators
  • 21.3. Scaling Uniformly Distributed Numbers
  • 21.4. Generating Normally Distributed Random Numbers
  • 21.5. Norm.Inv: Another Way to Generate Normal Deviates
  • 21.6. Scaling Normally Distributed Numbers
  • 21.7. Generating Correlated Random Numbers
  • 21.8. What’s Our Interest in Correlation? A Small Case
  • 21.9. Multiple Random Variables with Correlation: The Cholesky Decomposition
  • 21.10. Multivariate Uniform Simulations
  • 21.11. Summary
  • Exercises
  • 22. An Introduction to Monte Carlo Methods
  • 22.1. Overview
  • 22.2. Computing π Using Monte Carlo
  • 22.3. Programming the Monte Carlo Approach to Estimate π
  • 22.4. Another Monte Carlo Problem: Investment and Retirement
  • 22.5. A Monte Carlo Simulation of the Investment Problem
  • 22.6. Summary
  • Exercises
  • Appendix: Some Comments on the Value of π
  • 23. Simulating Stock Prices
  • 23.1. Overview
  • 23.2. What Do Stock Prices Look Like?
  • 23.3. Lognormal Price Distributions and Geometric Diffusions
  • 23.4. What Does the Lognormal Distribution Look Like?
  • 23.5. Simulating Lognormal Price Paths
  • 23.6. Technical Analysis
  • 23.7. Calculating the Parameters of the Lognormal Distribution from Stock Prices
  • 23.8. Summary
  • Exercises
  • Appendix: The Itô’s Lemma
  • 24. Monte Carlo Simulations for Investments
  • 24.1. Overview
  • 24.2. Simulating Price and Returns for a Single Stock
  • 24.3. Portfolio of Two Stocks
  • 24.4. Adding a Risk-Free Asset
  • 24.5. Multiple Stock Portfolios
  • 24.6. Simulating Savings for Pensions
  • 24.7. Beta and Return
  • 24.8. Summary
  • Exercises
  • 25. Value at Risk (VaR)
  • 25.1. Overview
  • 25.2. The Three Types of VaR Models
  • 25.3. VaR of an N-Asset Portfolio
  • 25.4. Backtesting
  • 26. Replicating Options and Option Strategies
  • 26.1. Overview
  • 26.2. Imperfect but Cashless Replication of a Call Option
  • 26.3. Simulating Portfolio Insurance
  • 26.4. Some Properties of Portfolio Insurance
  • 26.5. Digression: Insuring Total Portfolio Returns
  • 26.6. Simulating a Butterfly
  • 26.7. Summary
  • Exercises
  • 27. Using Monte Carlo Methods for Option Pricing
  • 27.1. Overview
  • 27.2. Pricing Plain-Vanilla Options Using Monte Carlo Methods
  • 27.3. State Prices, Probabilities, and Risk-Neutrality
  • 27.4. Pricing Plain-Vanilla Options—Monte Carlo Binomial Model Approach
  • 27.5. Pricing Asian Options
  • 27.6. Barrier Options
  • 27.7. Basket Options
  • 27.8. Rainbow Options
  • 27.9. Binary Options
  • 27.10. Chooser Options
  • 27.11. Lookback Options
  • 27.12. Summary
  • Exercises
  • VI. Technical
  • 28. Data Tables
  • 28.1. Overview
  • 28.2. An Example
  • 28.3. Creating a One-Dimensional Data Table
  • 28.4. Creating a Two-Dimensional Data Table
  • 28.5. An Aesthetic Note: Hiding the Formula Cells
  • 28.6. Excel Data Tables Are Arrays
  • 28.7. Data Tables on Blank Cells (Advanced)
  • 28.8. Data Tales Can Stop Your Computer
  • Exercises
  • 29. Matrices
  • 29.1. Overview
  • 29.2. Matrix Operations
  • 29.3. Matrix Inverses
  • 29.4. Solving Systems of Simultaneous Linear Equations
  • Exercises
  • 30. Excel Functions
  • 30.1. Overview
  • 30.2. Financial Functions
  • 30.3. Dates and Date Functions
  • 30.4. Statistical Functions
  • 30.5. Doing Regressions with Excel
  • 30.6. Conditional Functions
  • 30.7. Reference Functions
  • 30.8. Large, Rank, Percentile, and Percentrank
  • 30.9. Count, CountA, Countif, Countifs, Averageif, Averageifs
  • 31. Array Functions
  • 31.1. Overview
  • 31.2. Some Built-In Excel Array Functions
  • 31.3. Homemade Array Functions
  • 31.4. Array Formulas with Matrices
  • Exercises
  • 32. Some Excel Hints
  • 32.1. Overview
  • 32.2. Fast Copy: Filling in Data Next to Filled-In Column
  • 32.3. Filling Cells with a Series
  • 32.4. Multi-line Cells
  • 32.5. Multi-line Cells with Text Formulas
  • 32.6. Writing on Multiple Spreadsheets
  • 32.7. Moving Multiple Sheets of an Excel Notebook
  • 32.8. Text Functions in Excel
  • 32.9. Chart Titles That Update
  • 32.10. Putting Greek Symbols in Cells
  • 32.11. Superscripts and Subscripts
  • 32.12. Named Cells
  • 32.13. Hiding Cells (in Data Tables and Other Places)
  • 32.14. Formula Auditing
  • 32.15. Formatting Millions as Thousands
  • 32.16. Excel’s Personal Notebook: Automating Frequent Procedures
  • 32.17. Quick Number Formatting
  • 33. Essentials of R Programming
  • 33.1. Rule #1: Use the Provided Help for R Functions
  • 33.2. Installing a Package
  • 33.3. Setting a Default Folder (Working Directory)
  • 33.4. Understanding Data Types in R
  • 33.5. How to Read a Table from a CSV File
  • 33.6. How to Directly Import Stock Price Data to R
  • 33.7. Defining a Function
  • 33.8. Plotting Data in R
  • 33.9. The apply Function
  • 33.10. The lapply and sapply Functions
  • Selected References
  • Index