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　　A comprehensive guide to financial engineering that stresses

real-world applications

Financial engineering expert Charles S. Tapiero has his finger on

the pulse of shifts coming to financial engineering and its

applications. With an eye toward the future, he has crafted a

comprehensive and accessible book for practitioners and students of

Financial Engineering that emphasizes an intuitive approach to

financial and quantitative foundations in financial and risk

engineering. The book covers the theory from a practitioner

perspective and applies it to a variety of real-world

problems.

Examines the cornerstone of the explosive growth in markets

worldwide

Presents important financial engineering techniques to price,

hedge, and manage risks in general

Author heads the largest financial engineering program in the

world

Author Charles Tapiero wrote the seminal work Risk and Financial

Management.

Introduction.

Who This Book is For.

How This Book is Structured.

What's on the Companion Website.

Chapter 1: Risk, Finance, Corporate Management and

Society.

Overview.

1.1 Risks Everywhere—A Consequence of Uncertainty.

1.2 Risks and Finance: Basic Concepts.

Example: An IBM day-trades record.

Example: Constructing a portfolio.

1.3 Option Contracts.

Problem 1.1: Options and their Price.

Example: Options and the Price of Equity.

Example: Management Stock Options.

1.4 Options and Trading in Specialized Markets.

1.5 Real Life Crises and Finance.

1.6 The 2008 Meltdown and Financial Theory.

1.7 Finance and Ethics.

Summary.

Test Yourself.

References.

Chapter 2: Applied Finance.

Overview.

2.1 Finance and Practice.

2.2 Financial Risk Pricing: A Historical Perspective.

2.3 Essential of Financial Risk Management.

2.4 Technology and Complexity.

2.5 Market Making and Pricing Practice.

Summary.

Test Yourself.

References.

Chapter 3: Risk Measurement and Volatility.

Overview.

3.1 Risk, Volatility and Measurement.

3.2 Moments and Measures of Volatility.

Example: IBM Returns Statistics.

Example: Moments and the CAPM.

Problem 3.1: Calculating the Beta of a Security.

3.3 Statistical Estimations.

Example: The AR(1) ARCH(1) Model.

Example: A Garch (1,1) Model.

3.4 High-Low Estimators of Volatility.

3.5 Extreme Measures, Volume, and Intraday Prices.

Problem 3.2: The Probability of the Range.

3.6 Data Transformation.

Example: Taylor Series.

3.7 Value at Risk and Risk Exposure.

Example: VaR and Shortfall.

Example*: VaR, Normal ROR and Portfolio Design.

Summary.

Test Yourself.

References.

Chapter 4: Risk Finance Modeling and Dependence*.

Overview.

4.1 Introduction.

4.2 Statistical Dependence.

Example:  Risk Factors Aggregation.

Example: Principal Components Analysis (PCA).

Example: A Bi-Variate Data Matrix and PCA.

Example: A Market Index and PCA.

4.3 Dependence and Copulas.

Example: The Gumbel Copula, the Highs and the Lows.

Example:  Copulas and Conditional Dependence.

Example: Copula and the Conditional Distribution.

4.4 Financial Modeling and Inter-Temporal Models.

4.5 The R/S Index.

Summary.

Test Yourself.

References.

Chapter 5: Risk, Value, and Financial Prices.

Overview.

5.1 Value and Price.

5.2 Utility, Risk and Money.

5.3 Lotteries and Utility Functions.

Example: The utility of a lottery.

Example: The power utility function.

Example:  Valuation and the Pricing of Cash Flows.

Example:  Risk and the Financial Meltdown.

5.4 Utility Rational Foundations.

Examples: Specific Utility Functions.

5.5 The Price and the Utility of Consumption.

Example: Kernel Pricing and the exponential utility

function.

Example: The Pricing Kernel and the CAPM.

Example:  Kernel Pricing and the HARA utility function.

Summary.

Test Yourself.

References.

Chapter 6: Applied Utility Finance.

Overview.

6.1 Risk and the Utility of Time.

6.2 Assets Allocation and Investments.

Example: A Two securities problem.

Example: A 2 stocks portfolio.

Problem 6.1: The Efficiency Frontier.

Problem 6.2: A Two Securities Portfolio.

6.4 Conditional Kernel Pricing and the Price of Infrastructure

Investments.

6.5 Conditional Kernel Pricing and the Pricing of

Inventories.

6.6 Agency and Utility.

Example: A linear risk sharing rule.

6.7 Information Asymmetry: Moral Hazard and Adverse

Selection.

6.8 Adverse Selection.

6.9 The Moral Hazard Problem.

6.10 Signaling and Screening.

Summary.

Test Yourself.

References.

Chapter 7: Derivative Finance and Complete Markets.

Discrete States.

Overview.

7.1 The Arrow-Debreu Fundamental Approach to Asset Pricing.

Example: Generalization to n states.

Example: Binomial Option Pricing.

Problem 7.1: The Implied Risk Neutral Probability.

Example: The Price of a Call option.

Example:  A generalization to multiple periods.

Problem 7.2: Options and their Prices.

7.2 Put Call Parity.

Problem 7.3: Proving the Put-Call Parity.

Example: Put Call Parity and Dividend Payments.

Problem 7.4: Options PUT-CALL Parity.

7.3 The Price deflator and the Pricing Martingale.

7.4 Pricing and Complete Markets.

7.5 Options Galore.

Example: Look-Back Options.

Example: Asiatic Options.

Example:  Exchange options.

Example:  Chooser Options.

Example: Barrier and Other Options.

Example: Passport Options.

7.6 Options and Their “Real Uses”.

Example: Pricing a Forward.

Example: Pricing a floating rate bond.

Example: Pricing fixed rate bond.

Example: The Term Structure of Interest Rate.

Problem 7.5: Annuities and Obligations.

7.7 Pricing and Franchises with a Binomial Process.

7.8 Pricing a Pricing Policy.

7.9 Options Trading, Speculation, and Risk Management.

Example: Options and Trading Practice.

Example: Insuring and derivative hedges.

Problem 7.6: Portfolio Strategies.

Summary.

Appendix A: Martingales.

Example: Change of Measure in a Binomial Model.

Example: A Two Stages Random Walk and the Radon Nikodym

Derivative.

Appendix B: Formal Notations, Key terms and Definitions.

Test Yourself.

References.

Chapter 8: Options Applied.

Overview.

8.1 Introduction.

8.2 Optional Applications.

Problem 8.1: Pricing a Multi Period Forward.

Example:  Options Implied insurance pricing.

8.3 Random volatility and options pricing.

8.4. Real Assets and Real Options.

8.5 The Black Scholes Vanilla Option and the Greeks*.

8.6 The Greeks and Their Applications.

Summary.

Test Yourself.

References.

Chapter 9: Credit Scoring and the Price of Credit

Risk.

Overview.

9.1 Credit and Money.

9.2 Credit and Credit Risk.

9.3 Pricing Credit Risk: Principles.

9.4 Credit Scoring and Granting.

9.5 Credit Scoring: Real- Approaches.

Example: A Separatrix.

Example: The Separatrix and Bayesian Probabilities.

9.6 Probability Default Models.

Example: A Bivariate Dependent Default Distribution.

Example: A Portfolio of default loans.

Example: A Portfolio of dependent default loans.

Problem 9.1: The joint Bernoulli default distribution.

9.7 Credit Granting.

Example: Credit Granting and Creditor’s Risks.

Example: A Bayesian default model.

Example: A Financial Approach.

Example: An Approximate Solution.

Problem 9.2: The rate of return of loans.

9.8 The Reduced Form (Financial) Model.

Example: Calculating the spread of a default bond.

Example:  The Loan Model Again.

Example: Pricing default bonds.

Example: Pricing default bonds and the hazard rate.

9.9 Examples.

Example: The bank interest rate on a house loan.

Example: Buy insurance to protect the portfolio from loan

defaults.

Example: Use the portfolio as an underlying and buy or sell

derivatives on this underlying.

Problem: Lending rates of returns (T.S. Ho and E.O. Vieira).

9.10 Credit Risk and Collaterals Pricing.

Example: Hedge funds rates of returns.

Example: Equity Linked Life Insurance.

Example: Default and the price of homes.

Example: A banks profit from a loan.

9.11 Risk Management and Leverage.

Summary.

Test Yourself.

References.

Chapter 10: Multi-Names and Credit Risk Portfolios.

Overview.

10.1 Introduction.

10.2 Credit Default Swaps.

Example: Total Returns Swaps.

Example: Pricing a project launch.

10.3 Credit Derivatives: A Historical

Perspectives1.

10.4 CDOs: Examples and Models.

Example: Collateralized Mortgage Obligations (CMOs).

Example: Insurance and Risk Layering.

Example: A CDO with numbers.

Example: The CDO and SPV (BNP Paribas, France).

Example: A Synthetics CDO.

Example: A Portfolio of Loans, VaR and the Normal

Approximation.

Example: Insurance and Reinsurance and Stop/Excess Loss

Valuation.

10.5 Constructing a Credit Risk Portfolio and CDOs.

Example: A Simple Portfolio of Loans.

Example: Random and Dependent Default.

Example: The KMV Loss Model.

Summary.

Test Yourself.

References.

Chapter 11: Engineered Implied Volatility and Implied Risk

Neutral Distributions*.

Overview.

11.1 Introduction.

11.2 The Implied Risk Neutral Distribution.

Example: An Implied Binomial Distribution.

Example: Calculating the implied risk neutral probability.

11.3 The Implied Volatility.

Example: The implied volatility in a lognormal process.

11.4 Implied Distributions: Parametric Models.

Example: The Generalized Beta of the second kind.

11.5 A-parametric Approach and the Black-Scholes Model.

Example: The Shimko technique.

11.6 The Implied Risk Neutral Distribution and Information

Discrimination.

Example: En...

　　Charles S. Tapiero is the Topfer Distinguished Professor of

Financial Engineering and Technology Management at the New York

University Polytechnic Institute. He is also Chair and founder of

the Department of Finance and Risk Engineering, as well as

cofounder and co–Editor in Chief of Risk and Decision Analysis. An

active researcher and consultant, Professor Tapiero has published

over 350 papers and thirteen books on a broad range of issues

spanning risk analysis, actuarial and financial risk engineering,

and management, including Risk and Financial Management:

Mathematical and Computational Methods, also by Wiley.

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　　Charles Tapiero, as the head of the biggest financial

engineering program in the world and business consultant, has his

finger on the pulse of the shift that is coming in financial

engineering applications and study. With an eye toward the future,

he has crafted a comprehensive and practical book that emphasizes

an intuitive approach to the financial and quantitative foundations

of financial and risk engineering and its many applications to

asset pricing and risk management. Covering the theory from a

practitioner perspective, he then applies it to a variety of real

world problems. The book presents important techniques to price,

hedge, and manage risks in general - while acknowledging the high

degree of uncertainty in the real world.