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Financial Modeling 5th Revised edition [Hardback]

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  • Formāts: Hardback, 992 pages, height x width: 229x178 mm, 925 figures
  • Izdošanas datums: 01-Feb-2022
  • Izdevniecība: MIT Press
  • ISBN-10: 0262046423
  • ISBN-13: 9780262046428
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Financial Modeling 5th Revised editionPalielināt
  • Formāts: Hardback, 992 pages, height x width: 229x178 mm, 925 figures
  • Izdošanas datums: 01-Feb-2022
  • Izdevniecība: MIT Press
  • ISBN-10: 0262046423
  • ISBN-13: 9780262046428
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780262046428.html
Keywords:
"A revision of a leading finance textbook for the advanced undergraduate/Master's market"--

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.
Preface and Acknowledgments xix
Before All Else 1(12)
0.1 Data Tables
1(1)
0.2 What Is Getformula?
1(1)
0.3 How to Put Getformula into Your Excel Notebook
1(3)
0.4 Saving the Excel Workbook: Windows
4(1)
0.5 Saving the Excel Workbook: Mac
5(1)
0.6 Do You Have to Put Getformula into Each Excel Workbook?
5(1)
0.7 Using Formulatext() ) Instead of Getformula
6(1)
0.8 A Shortcut to Use Getformula and Formulatext
7(1)
0.9 Recording Getformula: The Windows Case
7(3)
0.10 Recording Getformula: The Mac Case
10(1)
0.11 Using R
11(2)
I Corporate Finance 13(164)
1 Basic Financial Analysis
15(38)
1.1 Overview
15(1)
1.2 Present Value and Net Present Value
16(6)
1.3 The Internal Rate of Return (IRR) and Loan Tables
22(5)
1.4 Multiple Internal Rates of Return
27(2)
1.5 Flat Payment Schedules
29(1)
1.6 Future Values and Applications
30(3)
1.7 A Pension Problem-Complicating the Future Value Problem
33(5)
1.8 Continuous Compounding
38(4)
1.9 Discounting Using Dated Cash Flows
42(3)
Exercises
45(8)
2 Corporate Valuation Overview
53(20)
2.1 Overview
53(1)
2.2 Three Methods to Compute Enterprise Value (EV)
53(1)
2.3 Using Accounting Book Values to Value a Company: The Firm's Accounting Enterprise Value
54(5)
2.4 The Efficient Markets Approach to Corporate Valuation
59(4)
2.5 Enterprise Value as the Present Value of the Free Cash Flows: DCF "Top Down" Valuation
63(4)
2.6 Free Cash Flows Based on Consolidated Statement of Cash Flows
67(1)
2.7 Free Cash Flows Based on Pro Forma Financial Statements
68(2)
2.8 Summary
70(1)
Exercises
71(2)
3 Calculating the Weighted Average Cost of Capital (WACC)
73(38)
3.1 Overview
73(2)
3.2 Computing the Value of the Firm's Equity, E
75(1)
3.3 Computing the Value of the Firm's Debt, D
75(2)
3.4 Computing the Firm's Tax Rate, Tc
77(1)
3.5 Computing the Firm's Cost of Debt, rD
78(3)
3.6 Two Approaches to Computing the Firm's Cost of Equity, rE
81(17)
3.7 Three Approaches to Computing the Expected Return on the Market, E(rM)
98(4)
3.8 What's the Risk-Free Rate rf in the CAPM?
102(1)
3.9 Computing the WACC
103(1)
3.10 When Don't the Models Work?
103(3)
3.11 Summary
106(1)
Exercises
106(5)
4 Pro Forma Analysis and Valuation Based on the Discounted Cash Flow Approach
111(34)
4.1 Overview
111(2)
4.2 Setting the Stage-Discounting the Free Cash Flow (FCF)
113(2)
4.3 Simplified Approach Based on Consolidated Statement of Cash Flows
115(5)
4.4 Pro Forma Financial Statement Modeling
120(11)
4.5 Using the FCF to Value the Firm and Its Equity
131(4)
4.6 Setting the Debt to Be the Absorbing Item and Incorporating Target Debt/Equity Ratio into the Pro Forma
135(1)
4.7 Calculating the Return on Invested Capital
136(1)
4.8 Project Finance: Debt Repayment Schedules
137(2)
4.9 Calculating the Return on Equity
139(1)
4.10 Tax Loss Carryforwards
140(2)
4.11 Conclusion
142(1)
Exercises
142(3)
5 Building a Pro Forma Model: The Case of Merck
145(16)
5.1 Overview
145(1)
5.2 Merck's Financial Statements, 2015-2018
146(3)
5.3 Analyzing the Financial Statements
149(8)
5.4 A Model for Merck
157(1)
5.5 Using the Model to Value Merck
158(2)
5.6 Valuation Model for Merck Using Multiples
160(1)
5.7 Summary
160(1)
6 Financial Analysis of Leasing
161(16)
6.1 Overview
161(1)
6.2 A Simple but Misleading Example
161(2)
6.3 Leasing and Firm Financing-the Equivalent-Loan Method
163(3)
6.4 The Lessor's Problem: Calculating the Highest Acceptable Lease Rental
166(3)
6.5 Asset Residual Value and Other Considerations
169(1)
6.6 Mini-Case: When Is Leasing Profitable for Both the Lessor and the Lessee?
170(2)
6.7 Leveraged Leasing
172(1)
6.8 A Leveraged Lease Example
173(2)
6.9 Summary
175(1)
Exercises
175(2)
II Bonds 177(76)
7 Bond's Duration
179(28)
7.1 Overview
179(1)
7.2 Two Examples
180(3)
7.3 What Does Duration Mean?
183(4)
7.4 Duration Patterns
187(2)
7.5 The Duration of a Bond with Uneven Payments
189(6)
7.6 Convexity of a Bond
195(2)
7.7 Immunization Strategies
197(8)
7.8 Summary
205(1)
Exercises
205(2)
8 Modeling the Term Structure
207(24)
8.1 Overview
207(1)
8.2 The Term Structure of Interest Rates
207(4)
8.3 Bond Pricing Using the Equivalent Single Bond Approach
211(4)
8.4 Pricing with Several Bonds at the Same Maturity
215(4)
8.5 The Nelson-Siegel Approach of Fitting a Functional Form to the Term Structure
219(3)
8.6 The Properties of the Nelson-Siegel Term Structure
222(2)
8.7 Term Structure for Treasury Notes
224(3)
8.8 Summary
227(1)
Appendix: VBA Functions Used in This
Chapter
227(4)
9 Calculating Default-Adjusted Expected Bond Returns
231(22)
9.1 Overview
231(2)
9.2 Calculating the Expected Return in a One-Period Framework
233(1)
9.3 Calculating the Bond Expected Return in a Multi-period Framework
234(4)
9.4 A Numerical Example
238(2)
9.5 Experimenting with the Example
240(2)
9.6 Computing the Bond Expected Return for an Actual Bond
242(4)
9.7 Semiannual Transition Matrices
246(3)
9.8 Computing Bond Beta
249(1)
9.9 Summary
250(1)
Exercises
251(2)
III Portfolio Theory 253(182)
10 Portfolio Models-Introduction
255(32)
10.1 Overview
255(1)
10.2 Computing Descriptive Statistics for Stocks
255(8)
10.3 Calculating Portfolio Means and Variances
263(5)
10.4 Portfolio Mean and Variance-Case of N Assets
268(8)
10.5 Envelope Portfolios
276(3)
10.6 Summary
279(1)
Exercises
279(2)
Appendix 10.1: Continuously Compounded versus Geometric Returns
281(2)
Appendix 10.2: Adjusting for Dividends
283(4)
11 Efficient Portfolios and the Efficient Frontier
287(50)
11.1 Overview
287(1)
11.2 Some Preliminary Definitions and Notation
287(2)
11.3 Five Propositions on Efficient Portfolios and the CAPM
289(5)
11.4 Calculating the Efficient Frontier: An Example
294(10)
11.5 Three Notes on the Optimization Procedure
304(3)
11.6 Finding the Market Portfolio: The Capital Market Line (CML)
307(2)
11.7 Computing the Global Minimum Variance Portfolio (GMVP)
309(3)
11.8 Testing the SML-Implementing Propositions 3-5
312(3)
11.9 Efficient Portfolios without Short Sales
315(16)
11.10 Summary
331(1)
Exercises
331(3)
Mathematical Appendix
334(3)
12 Calculating the Variance-Covariance Matrix
337(20)
12.1 Overview
337(1)
12.2 Computing the Sample Variance-Covariance Matrix
337(5)
12.3 The Correlation Matrix
342(3)
12.4 Four Alternatives to the Sample Variance-Covariance Matrix
345(1)
12.5 Alternatives to the Sample Variance-Covariance: The Single-Index Model
346(2)
12.6 Alternatives to the Sample Variance-Covariance: Constant Correlation
348(2)
12.7 Alternatives to the Sample Variance-Covariance: Shrinkage Methods
350(2)
12.8 Using Option Information to Compute the Variance Matrix
352(2)
12.9 Which Method to Compute the Variance-Covariance Matrix?
354(1)
12.10 Summing Up
355(1)
Exercises
355(2)
13 Estimating Betas and the Security Market Line
357(20)
13.1 Overview
357(3)
13.2 Testing the SML
360(6)
13.3 Did We Learn Something?
366(2)
13.4 The Non-efficiency of the "Market Portfolio"
368(4)
13.5 So What's the Real Market Portfolio? How Can We Test the CAPM?
372(1)
13.6 Conclusion: Does the CAPM Have Any Uses?
373(1)
Exercises
374(3)
14 Event Studies
377(28)
14.1 Overview
377(1)
14.2 Outline of an Event Study
377(4)
14.3 An Initial Event Study: Procter & Gamble Buys Gillette
381(8)
14.4 A Fuller Event Study: Impact of Earnings Announcements on Stock Prices
389(7)
14.5 Using a Two-Factor Model of Returns for an Event Study
396(5)
14.6 Using Excel's Offset Function to Locate a Regression in a Data Set
401(2)
14.7 Conclusion
403(2)
15 The Black-Litterman Approach to Portfolio Optimization
405(30)
15.1 Overview
405(2)
15.2 A Naive Problem
407(5)
15.3 Black and Litterman's Solution to the Optimization Problem
412(1)
15.4 BL Step 1: What Does the Market Think?
413(4)
15.5 BL Step 2: Introducing Opinions-What Does Joanna Think?
417(10)
15.6 Using BL for International Asset Allocation
427(5)
15.7 Summary
432(1)
Exercises
432(3)
IV Options 435(156)
16 Introduction to Options
437(22)
16.1 Overview
437(1)
16.2 Basic Option Definitions and Terminology
437(3)
16.3 Some Examples
440(1)
16.4 Option Payoff and Profit Patterns
441(4)
16.5 Option Strategies: Payoffs from Portfolios of Options and Stocks
445(3)
16.6 Option Arbitrage Propositions
448(7)
16.7 Summary
455(1)
Exercises
455(4)
17 The Binomial Option Pricing Model
459(40)
17.1 Overview
459(1)
17.2 Two-Date Binomial Pricing
459(2)
17.3 The State Prices
461(3)
17.4 The Multi-period Binomial Model
464(7)
17.5 Pricing American Options Using the Binomial Pricing Model
471(3)
17.6 Programming the Binomial Option Pricing Model
474(7)
17.7 Convergence of Binomial Pricing to the Black-Scholes Price
481(2)
17.8 Using the Binomial Model to Price Employee Stock Options
483(10)
17.9 Using the Binomial Model to Price Nonstandard Options: An Example
493(2)
17.10 Summary
495(1)
Exercises
495(4)
18 The Black-Scholes Model
499(38)
18.1 Overview
499(1)
18.2 The Black-Scholes Model
499(2)
18.3 Programming the Black-Scholes Option Pricing Model
501(4)
18.4 Calculating the Volatility
505(3)
18.5 Programming a Function to Find the Implied Volatility
508(4)
18.6 Dividend Adjustments to the Black-Scholes
512(6)
18.7 "Bang for the Buck" with Options
518(2)
18.8 The Black Model for Bond Option Valuation
520(2)
18.9 Using the Black-Scholes Model to Price Risky Debt
522(2)
18.10 Using the Black-Scholes Formula to Price Structured Securities
524(9)
18.11 Summary
533(1)
Exercises
534(3)
19 Option Greeks
537(32)
19.1 Overview
537(1)
19.2 Defining and Computing the Greeks
538(8)
19.3 Delta Hedging a Call
546(2)
19.4 The Greeks of a Portfolio
548(2)
19.5 Greek-Neutral Portfolio
550(4)
19.6 The Relationship between Delta, Theta, and Gamma
554(1)
19.7 Summary
555(1)
Exercises
555(1)
Appendix 19.1: VBA for Greeks
556(7)
Appendix 19.2: R Code for Greeks
563(6)
20 Real Options
569(22)
20.1 Overview
569(1)
20.2 A Simple Example of the Option to Expand
570(3)
20.3 The Abandonment Option
573(7)
20.4 Valuing the Abandonment Option as a Series of Puts
580(2)
20.5 Valuing a Biotechnology Project
582(7)
20.6 Summary
589(1)
Exercises
589(2)
V Monte Carlo Methods 591(238)
21 Generating and Using Random Numbers
593(46)
21.1 Overview
593(1)
21.2 Rand() and Rnd: The Excel and VBA Random-Number Generators
594(10)
21.3 Scaling Uniformly Distributed Numbers
604(1)
21.4 Generating Normally Distributed Random Numbers
605(12)
21.5 Norm.Inv: Another Way to Generate Normal Deviates
617(2)
21.6 Scaling Normally Distributed Numbers
619(1)
21.7 Generating Correlated Random Numbers
620(4)
21.8 What's Our Interest in Correlation? A Small Case
624(3)
21.9 Multiple Random Variables with Correlation: The Cholesky Decomposition
627(7)
21.10 Multivariate Uniform Simulations
634(2)
21.11 Summary
636(1)
Exercises
636(3)
22 An Introduction to Monte Carlo Methods
639(22)
22.1 Overview
639(1)
22.2 Computing n Using Monte Carlo
639(5)
22.3 Programming the Monte Carlo Approach to Estimate π
644(4)
22.4 Another Monte Carlo Problem: Investment and Retirement
648(2)
22.5 A Monte Carlo Simulation of the Investment Problem
650(7)
22.6 Summary
657(1)
Exercises
657(2)
Appendix: Some Comments on the Value of π
659(2)
23 Simulating Stock Prices
661(28)
23.1 Overview
661(1)
23.2 What Do Stock Prices Look Like?
662(5)
23.3 Lognormal Price Distributions and Geometric Diffusions
667(4)
23.4 What Does the Lognormal Distribution Look Like?
671(3)
23.5 Simulating Lognormal Price Paths
674(7)
23.6 Technical Analysis
681(1)
23.7 Calculating the Parameters of the Lognormal Distribution from Stock Prices
682(2)
23.8 Summary
684(1)
Exercises
685(2)
Appendix: The Ito's Lemma
687(2)
24 Monte Carlo Simulations for Investments
689(26)
24.1 Overview
689(1)
24.2 Simulating Price and Returns for a Single Stock
689(4)
24.3 Portfolio of Two Stocks
693(4)
24.4 Adding a Risk-Free Asset
697(2)
24.5 Multiple Stock Portfolios
699(4)
24.6 Simulating Savings for Pensions
703(4)
24.7 Beta and Return
707(4)
24.8 Summary
711(1)
Exercises
711(4)
25 Value at Risk (VaR)
715(18)
25.1 Overview
715(3)
25.2 The Three Types of VaR Models
718(6)
25.3 VaR of an N-Asset Portfolio
724(5)
25.4 Backtesting
729(4)
26 Replicating Options and Option Strategies
733(32)
26.1 Overview
733(5)
26.2 Imperfect but Cashless Replication of a Call Option
738(4)
26.3 Simulating Portfolio Insurance
742(7)
26.4 Some Properties of Portfolio Insurance
749(1)
26.5 Digression: Insuring Total Portfolio Returns
750(4)
26.6 Simulating a Butterfly
754(8)
26.7 Summary
762(1)
Exercises
763(2)
27 Using Monte Carlo Methods for Option Pricing
765(64)
27.1 Overview
765(1)
27.2 Pricing Plain-Vanilla Options Using Monte Carlo Methods
766(7)
27.3 State Prices, Probabilities, and Risk-Neutrality
773(3)
27.4 Pricing Plain-Vanilla Options-Monte Carlo Binomial Model Approach
776(13)
27.5 Pricing Asian Options
789(13)
27.6 Barrier Options
802(11)
27.7 Basket Options
813(3)
27.8 Rainbow Options
816(2)
27.9 Binary Options
818(2)
27.10 Chooser Options
820(1)
27.11 Lookback Options
821(4)
27.12 Summary
825(1)
Exercises
826(3)
VI Technical 829(134)
28 Data Tables
831(18)
28.1 Overview
831(1)
28.2 An Example
831(1)
28.3 Creating a One-Dimensional Data Table
832(2)
28.4 Creating a Two-Dimensional Data Table
834(1)
28.5 An Aesthetic Note: Hiding the Formula Cells
835(2)
28.6 Excel Data Tables Are Arrays
837(1)
28.7 Data Tables on Blank Cells (Advanced)
837(7)
28.8 Data Tales Can Stop Your Computer
844(1)
Exercises
845(4)
29 Matrices
849(10)
29.1 Overview
849(1)
29.2 Matrix Operations
849(5)
29.3 Matrix Inverses
854(1)
29.4 Solving Systems of Simultaneous Linear Equations
855(1)
Exercises
856(3)
30 Excel Functions
859(46)
30.1 Overview
859(1)
30.2 Financial Functions
859(14)
30.3 Dates and Date Functions
873(6)
30.4 Statistical Functions
879(5)
30.5 Doing Regressions with Excel
884(12)
30.6 Conditional Functions
896(2)
30.7 Reference Functions
898(3)
30.8 Large, Rank, Percentile, and Percentrank
901(1)
30.9 Count, CountA, Countif, Countifs, Averageif, Averageifs
902(3)
31 Array Functions
905(14)
31.1 Overview
905(1)
31.2 Some Built-In Excel Array Functions
906(5)
31.3 Homemade Array Functions
911(2)
31.4 Array Formulas with Matrices
913(4)
Exercises
917(2)
32 Some Excel Hints
919(32)
32.1 Overview
919(1)
32.2 Fast Copy: Filling in Data Next to Filled-In Column
919(2)
32.3 Filling Cells with a Series
921(1)
32.4 Multi-line Cells
922(1)
32.5 Multi-line Cells with Text Formulas
923(1)
32.6 Writing on Multiple Spreadsheets
924(1)
32.7 Moving Multiple Sheets of an Excel Notebook
925(1)
32.8 Text Functions in Excel
926(1)
32.9 Chart Titles That Update
926(3)
32.10 Putting Greek Symbols in Cells
929(2)
32.11 Superscripts and Subscripts
931(2)
32.12 Named Cells
933(2)
32.13 Hiding Cells (in Data Tables and Other Places)
935(3)
32.14 Formula Auditing
938(2)
32.15 Formatting Millions as Thousands
940(2)
32.16 Excel's Personal Notebook: Automating Frequent Procedures
942(7)
32.17 Quick Number Formatting
949(2)
33 Essentials of R Programming
951(12)
33.1 Rule #1: Use the Provided Help for R Functions
951(1)
33.2 Installing a Package
952(1)
33.3 Setting a Default Folder (Working Directory)
953(1)
33.4 Understanding Data Types in R
954(1)
33.5 How to Read a Table from a CSV File
955(1)
33.6 How to Directly Import Stock Price Data to R
956(1)
33.7 Defining a Function
957(1)
33.8 Plotting Data in R
958(2)
33.9 The apply Function
960(1)
33.10 The lapply and sapply Functions
961(2)
Selected References 963(12)
Index 975