Implementing QuantLib
Implementing QuantLib
About the Book
This book is a report on the design and implementation of QuantLib, alike in spirit—but, hopefully, with less frightening results—to the How I did it book prominently featured in Mel Brooks' Young Frankenstein (in this case, of course, it would be "how we did it"). If you are, or want to be, a QuantLib user, you will find here useful information on the design of the library that might not be readily apparent when reading the code. If you're working in quantitative finance, even if not using QuantLib, you can still read it as a field report on the design of a financial library. You will find that it covers issues that you might also face, as well as some possible solutions and their rationale. Based on your constraints, it is possible—even likely—that you will choose other solutions; but you might profit from this discussion just the same.
The book is primarily aimed at users wanting to extend the library with their own instruments or models; if you desire to do so, the description of the available class hierarchies and frameworks will provide you with information about the hooks you need to integrate your code with QuantLib and take advantage of its facilities. If you're not this kind of user, don't give up on the book yet; you can find useful information too. However, you might want to look at the QuantLib Python Cookbook instead.
Implementing QuantLib is also available as a paperback from your local Amazon store.
About the Author
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Reader Testimonials
Roland Lichters
Co-founder and CTO of Quaternion Risk Management, co-author of “Modern Derivatives Pricing and Credit Exposure Analysis”
The co-founder of the QuantLib project guides the reader on a safe path through the jungle of this precious library’s design ideas and class hierarchies. This book thus unlocks the library to the novice, and it is full of insightful discussions that makes it surprising and enjoyable reading for the long-time QuantLib user. I would have benefitted a lot had this work been available when I started my own and initially bumpy journey of exploring and using QuantLib in 2001!
Daniel J. Duffy
Author of “Financial Instrument Pricing Using C++” and “Introduction to C++ for Financial Engineers: An Object-Oriented Approach”
C++ is the gold standard for computational finance applications. Luigi has done a thorough job of integrating modern C++ and Design Patterns to a range of equity and fixed income pricing problems. I warmly recommend it to MSc and MFE students of finance who wish to learn how to use and to write structured code.
Table of Contents
- 1. Introduction
-
2. Financial instruments and pricing engines
-
2.1 The
Instrumentclass- 2.1.1 Interface and requirements
- 2.1.2 Implementation
- 2.1.3 Example: interest-rate swap
- 2.1.4 Further developments
-
2.2 Pricing engines
- 2.2.1 Example: plain-vanilla option
-
2.1 The
-
3. Term structures
-
3.1 The
TermStructureclass- 3.1.1 Interface and requirements
- 3.1.2 Implementation
-
3.2 Interest-rate term structures
- 3.2.1 Interface and implementation
- 3.2.2 Discount, forward-rate, and zero-rate curves
- 3.2.3 Example: bootstrapping an interpolated curve
- 3.2.4 Example: adding z-spread to an interest-rate curve
-
3.3 Other term structures
- 3.3.1 Default-probability term structures
- 3.3.2 Inflation term structures
- 3.3.3 Volatility term structures
- 3.3.4 Equity volatility structures
- 3.3.5 Interest-rate volatility structures
-
3.1 The
-
4. Cash flows and coupons
-
4.1 The
CashFlowclass -
4.2 Interest-rate coupons
- 4.2.1 Fixed-rate coupons
- 4.2.2 Floating-rate coupons
- 4.2.3 Example: LIBOR coupons
- 4.2.4 Example: capped/floored coupons
- 4.2.5 Generating cash-flow sequences
- 4.2.6 Other coupons and further developments
-
4.3 Cash-flow analysis
- 4.3.1 Example: fixed-rate bonds
-
4.1 The
-
5. Parameterized models and calibration
-
5.1 The
CalibrationHelperclass- 5.1.1 Example: the Heston model
- 5.2 Parameters
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5.3 The
CalibratedModelclass- 5.3.1 Example: the Heston model, continued
-
5.1 The
-
6. The Monte Carlo framework
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6.1 Path generation
- 6.1.1 Random-number generation
- 6.1.2 Stochastic processes
- 6.1.3 Random path generators
- 6.2 Pricing on a path
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6.3 Putting it all together
- 6.3.1 Monte Carlo traits
- 6.3.2 The Monte Carlo model
- 6.3.3 Monte Carlo simulations
- 6.3.4 Example: basket option
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6.1 Path generation
-
7. The tree framework
-
7.1 The
LatticeandDiscretizedAssetclasses- 7.1.1 Example: discretized bonds
- 7.1.2 Example: discretized option
-
7.2 Trees and tree-based lattices
-
7.2.1 The
Treeclass template - 7.2.2 Binomial and trinomial trees
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7.2.3 The
TreeLatticeclass template
-
7.2.1 The
-
7.3 Tree-based engines
- 7.3.1 Example: callable fixed-rate bonds
-
7.1 The
-
8. The finite-difference framework
-
8.1 The old framework
- 8.1.1 Differential operators
- 8.1.2 Evolution schemes
- 8.1.3 Boundary conditions
- 8.1.4 Step conditions
-
8.1.5 The
FiniteDifferenceModelclass - 8.1.6 Example: American option
- 8.1.7 Time-dependent operators
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8.2 The new framework
- 8.2.1 Meshers
- 8.2.2 Operators
- 8.2.3 Examples: Black-Scholes operators
- 8.2.4 Initial, boundary, and step conditions
- 8.2.5 Schemes and solvers
-
8.1 The old framework
- 9. Conclusion
-
A. Odds and ends
- Basic types
-
Date calculations
- Dates and periods
- Calendars
- Day-count conventions
- Schedules
-
Finance-related classes
- Market quotes
- Interest rates
- Indexes
- Exercises and payoffs
-
Math-related classes
- Interpolations
- One-dimensional solvers
- Optimizers
- Statistics
- Linear algebra
- Global settings
-
Utilities
- Smart pointers and handles
- Error reporting
- Disposable objects
-
Design patterns
- The Observer pattern
- The Singleton pattern
- The Visitor pattern
- B. Code conventions
- QuantLib license
- Bibliography
- Notes
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