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More About This Textbook
Overview
A balanced introduction to the theoretical foundations and realworld applications of mathematical finance
The evergrowing use of derivative products makes it essential for financial industry practitioners to have a solid understanding of derivative pricing. To cope with the growing complexity, narrowing margins, and shortening lifecycle of the individual derivative product, an efficient, yet modular, implementation of the pricing algorithms is necessary. Mathematical Finance is the first book to harmonize the theory, modeling, and implementation of today's most prevalent pricing models under one convenient cover. Building a bridge from academia to practice, this selfcontained text applies theoretical concepts to realworld examples and introduces stateoftheart, objectoriented programming techniques that equip the reader with the conceptual and illustrative tools needed to understand and develop successful derivative pricing models.
Utilizing almost twenty years of academic and industry experience, the author discusses the mathematical concepts that are the foundation of commonly used derivative pricing models, and insightful Motivation and Interpretation sections for each concept are presented to further illustrate the relationship between theory and practice. Indepth coverage of the common characteristics found amongst successful pricing models are provided in addition to key techniques and tips for the construction of these models. The opportunity to interactively explore the book's principal ideas and methodologies is made possible via a related Web site that features interactive Java experiments and exercises.
While a high standard of mathematical precision is retained, Mathematical Finance emphasizes practical motivations, interpretations, and results and is an excellent textbook for students in mathematical finance, computational finance, and derivative pricing courses at the upper undergraduate or beginning graduate level. It also serves as a valuable reference for professionals in the banking, insurance, and asset management industries.
Editorial Reviews
From the Publisher
"…very useful to practitioners and students…" (MAA Reviews, December 26, 2007)"An excellent textbook for students in mathematical finance, computational finance, and derivative pricing courses at the upper undergraduate or beginning graduate level." (Mathematical Reviews 2007)
Product Details
Related Subjects
Meet the Author
Christian Fries, PhD, is Lecturer of Mathematical Finance at the University of Frankfurt and head of financial model development at DZ Bank AG Frankfurt, both located in Germany. With extensive knowledge in various programming languages, Dr. Fries has conducted quantitative analysis and overseen the implementation of mathematical modeling platforms at numerous financial institutions. His research interests within the field of mathematical finance include the LIBOR Market Model, Efficient Calculation of Risk Measures with MonteCarlo Methods, Pricing of Bermudan Options with MonteCarlo Methods, and Markov Functional Models.
Table of Contents
1. Introduction.
1.1 Theory, Modeling and Implementation.
1.2 Interest Rate Models and Interest Rate Derivatives.
1.3 How to Read this Book.
1.3.1 Abridged Versions.
1.3.2 Special Sections.
1.3.3 Notation.
I: FOUNDATIONS.
2. Foundations.
2.1 Probability Theory.
2.2 Stochastic Processes.
2.3 Filtration.
2.4 Brownian Motion.
2.5 Wiener Measure, Canonical Setup.
2.6 Itô Calculus.
2.6.1 Itô Integral.
2.6.2 Itô Process.
2.6.3 Itô Lemma and Product Rule.
2.7 Brownian Motion with Instantaneous Correlation.
2.8 Martingales.
2.8.1 Martingale Representation Theorem.
2.9 Change of Measure (Girsanov, Cameron, Martin).
2.10 Stochastic Integration.
2.11 Partial Differential Equations (PDE).
2.11.1 FeynmanKac Theorem .
2.12 List of Symbols.
3. Replication.
3.1 Replication Strategies.
3.1.1 Introduction.
3.1.2 Replication in a discrete Model.
3.2 Foundations: Equivalent Martingale Measure.
3.2.1 Challenge and Solution Outline.
3.2.2 Steps towards the Universal Pricing Theorem.
3.3 Excursus: Relative Prices and Risk Neutral Measures.
3.3.1 Why relative prices?
3.3.2 Risk Neutral Measure.
II: FIRST APPLICATIONS.
4. Pricing of a European Stock Option under theBlackScholes Model.
5. Excursus: The Density of the Underlying of a EuropeanCall Option.
6. Excursus: Interpolation of European Option Prices.
6.1 NoArbitrage Conditions for Interpolated Prices.
6.2 Arbitrage Violations through Interpolation.
6.2.1 Example (1): Interpolation of four Prices.
6.2.2 Example (2): Interpolation of two Prices.
6.3 ArbitrageFree Interpolation of European Option Prices.
7. Hedging in Continuous and Discrete Time and theGreeks.
7.1 Introduction.
7.2 Deriving the Replications Strategy from Pricing Theory.
7.2.1 Deriving the Replication Strategy under the Assumption ofa Locally Riskless Product.
7.2.2 The BlackScholes Differential Equation.
7.2.3 The Derivative V(t) as a Function of its Underlyings S_{i}(t).
7.2.4 Example: Replication Portfolio and PDE under aBlackScholes Model.
7.3 Greeks.
7.3.1 Greeks of a European CallOption under the BlackScholesmodel.
7.4 Hedging in Discrete Time: Delta and DeltaGamma Hedging.
7.4.1 Delta Hedging.
7.4.2 Error Propagation.
7.4.3 DeltaGamma Hedging.
7.4.4 Vega Hedging.
7.5 Hedging in Discrete Time: Minimizing the Residual Error(BouchaudSornette Method).
7.5.1 Minimizing the Residual Error at Maturity T.
7.5.2 Minimizing the Residual Error in each Time Step.
III: INTEREST RATE STRUCTURES, INTEREST RATE PRODUCTS ANDANALYTIC PRICING FORMULAS.
Motivation and Overview.
8. Interest Rate Structures.
8.1 Introduction.
8.1.1 Fixing Times and Tenor Times.
8.2 Definitions.
8.3 Interest Rate Curve Bootstrapping.
8.4 Interpolation of Interest Rate Curves.
8.5 Implementation.
9. Simple Interest Rate Products.
9.1 Interest Rate Products Part 1: Products withoutOptionality.
9.1.1 Fix, Floating and Swap.
9.1.2 MoneyMarket Account.
9.2 Interest Rate Products Part 2: Simple Options.
9.2.1 Cap, Floor, Swaption.
9.2.2 Foreign Caplet, Quanto.
10. The Black Model for a Caplet.
11. Pricing of a Quanto Caplet (Modeling the FFX).
11.1 Choice of Numéraire.
12. Exotic Derivatives.
12.1 Prototypical Product Properties.
12.2 Interest Rate Products Part 3: Exotic Interest RateDerivatives.
12.2.1 Structured Bond, Structured Swap, Zero Structure.
12.2.2 Bermudan Option.
12.2.3 Bermudan Callable and Bermudan Cancelable.
12.2.4 Compound Options.
12.2.5 Trigger Products.
12.2.6 Structured Coupons.
12.2.7 Shout Options.
12.3 Product Toolbox.
IV: DISCRETIZATION AND NUMERICAL VALUATION METHODS.
Motivation and Overview.
13. Discretization of time and state space.
13.1 Discretization of Time: The Euler and the MilsteinScheme.
13.1.1 Definitions.
13.1.2 TimeDiscretization of a Lognormal Process.
13.2 Discretization of Paths (MonteCarlo Simulation) .
13.2.1 MonteCarlo Simulation.
13.2.2 Weighted MonteCarlo Simulation.
13.2.3 Implementation.
13.2.4 Review.
13.3 Discretization of State Space.
13.3.1 Definitions.
13.3.2 BackwardAlgorithm.
13.3.3 Review.
13.4 Path Simulation through a Lattice: Two Layers.
14. Numerical Methods for Partial DifferentialEquations.
15. Pricing Bermudan Options in a Monte CarloSimulation.
15.1 Introduction.
15.2 Bermudan Options: Notation.
15.2.1 Bermudan Callable.
15.2.2 Relative Prices.
15.3 Bermudan Option as Optimal Exercise Problem.
15.3.1 Bermudan Option Value as single (unconditioned)Expectation: The Optimal Exercise Value.
15.4 Bermudan Option Pricing  The Backward Algorithm.
15.5 Resimulation.
15.6 Perfect Foresight.
15.7 Conditional Expectation as Functional Dependence.
15.8 Binning.
15.8.1 Binning as a LeastSquare Regression.
15.9 Foresight Bias.
15.10 Regression Methods  Least Square MonteCarlo.
15.10.1 Least Square Approximation of the ConditionalExpectation.
15.10.2 Example: Evaluation of a Bermudan Option on a Stock(Backward Algorithm with Conditional Expectation Estimator).
15.10.3 Example: Evaluation of a Bermudan Callable.
15.10.4 Implementation.
15.10.5 Binning as linear LeastSquare Regression.
15.11 Optimization Methods.
15.11.1 Andersen Algorithm for Bermudan Swaptions.
15.11.2 Review of the Threshold Optimization Method.
15.11.3 Optimization of Exercise Strategy: A more generalFormulation.
15.11.4 Comparison of Optimization Method and Regression.
Method.
15.12 Duality Method: Upper Bound for Bermudan OptionPrices.
15.12.1 Foundations.
15.12.2 American Option Evaluation as Optimal StoppingProblem.
15.13 PrimalDual Method: Upper and Lower Bound.
16. Pricing PathDependent Options in a BackwardAlgorithm.
16.1 Evaluation of a Snowball / Memory in a BackwardAlgorithm.
16.2 Evaluation of a Flexi Cap in a Backward Algorithm.
17. Sensitivities (Partial Derivatives) of Monte CarloPrices.
17.1 Introduction.
17.2 Problem Description.
17.2.1 Pricing using MonteCarlo Simulation.
17.2.2 Sensitivities from MonteCarlo Pricing.
17.2.3 Example: The Linear and the Discontinuous Payout.
17.2.4 Example: Trigger Products.
17.3 Generic Sensitivities: Bumping the Model.
17.4 Sensitivities by Finite Differences.
17.4.1 Example: Finite Differences applied to Smooth andDiscontinuous Payout.
17.5 Sensitivities by Pathwise Differentiation.
17.5.1 Example: Delta of a European Option under a BlackScholesModel.
17.5.2 Pathwise Differentiation for Discontinuous Payouts.
17.6 Sensitivities by Likelihood Ratio Weighting.
17.6.1 Example: Delta of a European Option under a BlackScholesModel using Pathwise Derivative.
17.6.2 Example: Variance Increase of the Sensitivity when usingLikelihood Ratio Method for Smooth Payouts.
17.7 Sensitivities by Malliavin Weighting.
17.8 Proxy Simulation Scheme.
18. Proxy Simulation Schemes for Monte Carlo Sensitivitiesand Importance Sampling.
18.1 Full Proxy Simulation Scheme.
18.1.1 Calculation of MonteCarlo weights.
18.2 Sensitivities by Finite Differences on a Proxy SimulationScheme.
18.2.1 Localization.
18.2.2 ObjectOriented Design.
18.3 Importance Sampling.
18.3.1 Example.
18.4 Partial Proxy Simulation Schemes.
18.4.1 Linear Proxy Constraint.
18.4.2 Comparison to Full Proxy Scheme Method.
18.4.3 NonLinear Proxy Constraint.
18.4.4 Transition Probability from a Nonlinear ProxyConstraint.
18.4.5 Sensitivity with respect to the Diffusion Coefficients Vega.
18.4.6 Example: LIBOR Target Redemption Note.
18.4.7 Example: CMS Target Redemption Note.
V: PRICING MODELS FOR INTEREST RATE DERIVATIVES.
19. LIBOR Market Models.
19.1 LIBOR Market Model.
19.1.1 Derivation of the Drift Term.
19.1.2 The Short Period Bond P(T_{m(t)+1};t) .
19.1.3 Discretization and (MonteCarlo) Simulation.
19.1.4 Calibration  Choice of the free Parameters.
19.1.5 Interpolation of Forward Rates in the LIBOR MarketModel.
19.2 Object Oriented Design.
19.2.1 Reuse of Implementation.
19.2.2 Separation of Product and Model.
19.2.3 Abstraction of Model Parameters.
19.2.4 Abstraction of Calibration.
19.3 Swap Rate Market Models (Jamshidian 1997).
19.3.1 The Swap Measure.
19.3.2 Derivation of the Drift Term.
19.3.3 Calibration  Choice of the free Parameters.
20. Swap Rate Market Models.
20.1 Definitions.
20.2 Terminal Correlation examined in a LIBOR Market ModelExample.
20.2.1 Decorrelation in a OneFactor Model.
20.2.2 Impact of the Time Structure of the InstantaneousVolatility on Caplet and Swaption Prices.
20.2.3 The Swaption Value as a Function of Forward Rates.
20.3 Terminal Correlation is dependent on the EquivalentMartingale Measure.
20.3.1 Dependence of the Terminal Density on the MartingaleMeasure.
21. Excursus: Instantaneous Correlation and TerminalCorrelation.
21.1 Short Rate Process in the HJM Framework.
21.2 The HJM Drift Condition.
22.HeathJarrowMorton Framework: Foundations.
22.1 Introduction.
22.2 The Market Price of Risk.
22.3 Overview: Some Common Models.
22.4 Implementations.
22.4.1 MonteCarlo Implementation of ShortRate Models.
22.4.2 Lattice Implementation of ShortRate Models.
23. ShortRate Models.
23.1 Short Rate Models in the HJM Framework.
23.1.1 Example: The HoLee Model in the HJM Framework.
23.1.2 Example: The HullWhite Model in the HJM Framework.
23.2 LIBOR Market Model in the HJM Framework.
23.2.1 HJM Volatility Structure of the LIBOR Market Model.
23.2.2 LIBOR Market Model Drift under the QB Measure.
23.2.3 LIBOR Market Model as a Short Rate Model.
24 HeathJarrowMorton Framwork: Immersion of ShortRateModels and LIBOR Market Model.
24.1 Model.
24.2 Interpretation of the Figures.
24.3 Mean Reversion.
24.4 Factors.
24.5 Exponential Volatility Function.
24.6 Instantaneous Correlation.
25. Excursus: Shape of teh Interst Rate Curve under MeanReversion and a Multifactor Model.
25.1 Introduction.
25.2 Cheyette Model.
26. RitchkenSakarasubramanian Framework: JHM with Low MarkovDimension.
26.1 Introduction.
26.1.1 The Markov Functional Assumption (independent of themodel considered) .
26.1.2 Outline of this Chapter .
26.2 Equity Markov Functional Model.
26.2.1 Markov Functional Assumption.
26.2.2 Example: The BlackScholes Model.
26.2.3 Numerical Calibration to a Full TwoDimensional EuropeanOption Smile Surface.
26.2.4 Interest Rates.
26.2.5 Model Dynamics.
26.2.6 Implementation.
26.3 LIBOR Markov Functional Model.
26.3.1 LIBOR Markov Functional Model in Terminal Measure.
26.3.2 LIBOR Markov Functional Model in Spot Measure.
26.3.3 Remark on Implementation.
26.3.4 Change of numéraire in a MarkovFunctionalModel.
26.4 Implementation: Lattice.
26.4.1 Convolution with the Normal Probability Density.
26.4.2 State space discretization.
Markov Functional Models.
PART VI: Extended Models.
27.1 Introduction  Different Types of Spreads.
27.1.1 Spread on a Coupon.
27.1.2 Credit Spread.
27.2 Defaultable Bonds.
27.3 Integrating deterministic Credit Spread into a PricingModel.
27.3.1 Deterministic Credit Spread.
27.3.2 Implementation.
27.4 Receiver’s and Payer’s Credit Spreads.
27.4.1 Example: Defaultable Forward Starting Coupon Bond.
27.4.2 Example: Option on a Defaultable Coupon Bond.
28. Credit Spreads.
28.1 Cross Currency LIBOR Market Model.
28.1.1 Derivation of the Drift Term under SpotMeasure.
28.1.2 Implementation.
28.2 Equity Hybrid LIBOR Market Model.
28.2.1 Derivation of the Drift Term under SpotMeasure.
28.2.2 Implementation.
28.3 EquityHybrid CrossCurrency LIBOR Market Model.
28.3.1 Summary.
28.3.2 Implementation.
29. Hybrid Models.
29.1 Elements of Object Oriented Programming: Class andObjects.
29.1.1 Example: Class of a Binomial Distributed RandomVariable.
29.1.2 Constructor.
29.1.3 Methods: Getter, Setter, Static Methods.
29.2 Principles of Object Oriented Programming.
29.2.1 Encapsulation and Interfaces.
29.2.2 Abstraction and Inheritance.
29.2.3 Polymorphism.
29.3 Example: A Class Structure for One Dimensional RootFinders.
29.3.1 Root Finder for General Functions.
29.3.2 Root Finder for Functions with Analytic Derivative:Newton Method.
29.3.3 Root Finder for Functions with Derivative Estimation:Secant Method.
29.4 Anatomy of a Java™ Class.
29.5 Libraries.
29.5.1 Java™2 Platform, Standard Edition (j2se).
29.5.2 Java™2 Platform, Enterprise Edition (j2ee).
29.5.3 Colt.
29.5.4 CommonsMath: The Jakarta Mathematics Library.
29.6 Some Final Remarks.
29.6.1 Object Oriented Design (OOD) / Unified ModelingLanguage.
PART VII: Implementation
30. ObjectOriented Implementatin inJava^{TM}.
PART VIII: Appendices.
A: A small Collection of Common Misconceptions.
B: Tools (Selection).
B.1 Linear Regression.
B.2 Generation of Random Numbers.
B.2.1 Uniform Distributed Random Variables.
B.2.2 Transformation of the Random Number Distribution via theInverse Distribution Function.
B.2.3 Normal Distributed Random Variables.
B.2.4 Poisson Distributed Random Variables.
B.2.5 Generation of Paths of an ndimensional BrownianMotion.
B.3 Factor Decomposition  Generation of Correlated BrownianMotion.
B.4 Factor Reduction.
B.5 Optimization (onedimensional): Golden Section Search.
B.6 Convolution with Normal Density.
C: Exercises.
D: List of Symbols.
E: Java™ Source Code (Selection).
E.1 Java™ Classes for Chapter 29.
List of Figures.
List of Tables.
List of Listings.
Bibliography.
Index.