C++实现的金融衍生品定价模型

下载需积分: 10 | PDF格式 | 11.62MB | 更新于2025-01-05 | 130 浏览量 | 举报

"Derivative Modelling in C++ 是一本由Justin London编写的金融工程编程教程，专注于在C++中实现各种金融衍生品的定价模型。这本书由TeAM YYeP G进行数字签名，并由John Wiley & Sons出版社出版，该出版社在金融、投资和高级商业执行者领域提供专业和个人知识理解的书籍。Wiley Finance系列涵盖了广泛的金融主题，包括风险管理、金融工程、资产估值和定价策略等。" 在这本《Derivative Modelling in C++》中，作者Justin London深入探讨了如何利用C++这一强大的编程语言来构建和实施金融衍生品的数学模型。这些模型对于理解和定价金融市场的复杂产品至关重要。金融衍生品是基于基础资产（如股票、债券或商品）价格变动的金融工具，它们的价值直接依赖于这些基础资产的表现。本书的核心内容可能包括以下几个关键知识点： 1. **基础概念**：首先，读者将了解金融衍生品的基础知识，如期货、期权、互换和信用衍生品的基本结构和性质。这涉及到对金融市场的基本理解，以及金融工具的风险和回报特性。 2. **定价理论**：书中会详细讲解Black-Scholes模型和其他著名的衍生品定价模型，如二叉树模型（Binomial Tree Model）和蒙特卡洛模拟（Monte Carlo Simulation）。这些模型是理解衍生品定价的关键，它们提供了计算衍生品公平价值的方法。 3. **C++编程技术**：Justin London会介绍如何使用C++来实现这些复杂的数学模型，包括面向对象编程、模板类、STL（标准模板库）、多线程和高效算法的应用。这些技能对于构建高性能的金融软件至关重要。 4. **风险管理和对冲策略**：书中可能涵盖如何使用衍生品来管理风险，包括希腊字母（Delta, Gamma, Theta, Vega等）的概念，这些都是衡量价格敏感性的关键指标，用于优化对冲策略。 5. **实际应用**：作者可能会讨论实际市场中的案例研究，展示如何将理论模型应用于真实世界的交易决策和风险管理。 6. **电子化交易和实时定价**：随着金融市场的电子化，书中可能会涉及如何利用C++进行实时数据处理和衍生品定价，以及如何与交易系统集成。通过阅读《Derivative Modelling in C++》，读者不仅能够掌握金融衍生品的定价理论，还能获得在C++中实现这些模型的实际编程经验，这对于在金融工程领域工作的专业人士来说是非常宝贵的资源。

展开

the University of Chicago, and Carnegie-Mellon, respectively, may start to adapt

their curricula, if they have not done so already, to place more emphasis on the prac-

tical implementation and coding of models as many of their graduates head to Wall

Street to work in quantitative research and trading development groups.

I felt that since no book bridged the gap between the two and because such a

book would have helped me both in school and afterward on the job as a quantita-

tive developer, I should write such a book so as to help others. Such a book was an

enormous undertaking and required contacting many of the model developers of

some of the more complicated models to try to understand how they implemented

them and in some cases to even obtain their code. In those cases where I was not

able to get model details or code from an author, I was able to verify the accuracy

and robustness of the code I developed by being able to reproduce numerical results

of the models in published papers and books.

Modeling Derivatives in C++ is the ﬁrst book to provide the source code for

most models used for pricing equity and ﬁxed income derivatives. The objective of

the book is to ﬁll the large gap that has existed between theory and practice of the

quantitative ﬁnance ﬁeld. Readers will learn how to correctly code in C++ many de-

rivatives models used by research and trading desks. The book bridges the gap be-

tween theory and practice by providing both the theory and mathematical

derivations behind the models as well as the actual working code implementations

of these models. While there have been other books that have helped bridge this

gap such as Clewlow and Strickland’s Implementing Derivatives Models (John Wi-

ley & Sons, 1998a), they provide only pseudocode and do not emphasize robust

and efﬁcient object-oriented code that is reusable. The assumption that readers can

easily or correctly translate pseudocode, which may have complex embedded sub-

routines of numerical computations that is needed, often is mistaken. Sometimes,

readers learn by analyzing and reviewing the complete and working code, which is

what this book attempts to accomplish. However, Implementing Derivatives Mod-

els does contain useful model discussions and pseudocode implementations, some

of which are implemented and built upon in this book using C++, such as the hedge

control variate method discussed in Chapter 2 and the alternating direction implicit

method discussed in Chapter 5.

Modeling Derivatives in C++ goes several steps beyond just providing C++

code; it discusses inefﬁciencies in some of the implementations and how they can be

improved with more robust object-oriented implementations by providing code

from the QuantLib, an open source quantitative pricing library, as well as by pro-

viding alternative implementations. For instance, three separate implementations

are given for the Hull-White model to show readers different coding approaches.

The book contains hundreds of classes, and there is a complete pricing engine li-

brary on the CD-ROM accompanying this book, which includes the code discussed

and given in the book. QuantPro, an MFC Windows application, for pricing many

equity and ﬁxed income derivatives using the models discussed in the book, as well

as for simulating derivatives trades, is also provided on the CD-ROM.

It is the goal of the book that readers will be able to write their own models in

Preface xv

C++ and then be able to adapt some of the coded models in this book to their own

pricing libraries and perhaps even use to trade. Most important, the book is in-

tended to guide readers through the complexities and intricacies of the theory and

of applying it in practice. The book is aimed at advanced undergraduate students

well as graduate (MBA and Ph.D.) students in ﬁnancial economics, computer sci-

ence, ﬁnancial engineering, computational ﬁnance, and business as well as Wall

Street practitioners working in a quantitative research or trading group who need a

comprehensive reference guide for implementing their models.

Readers should have a basic understanding of stochastic calculus, probability

theory, linear algebra, partial differential equation (PDEs), and stochastic

processes. For those readers who may be lacking the background in some of this

material or need to review, the appendixes provide a review of some of this mater-

ial. Due to the comprehensiveness of the book, it can be used by professors as ei-

ther a primary text or a supplementary text in their courses.

The chapters are grouped into two main sections: The ﬁrst focuses on the pric-

ing of equity derivatives and comprises Chapter 1 to Chapter 9, and the second

part focuses on the pricing of interest rate derivatives: Chapter 10 to Chapter 14.

Chapter 1 focuses on the derivation and foundations of the Black-Scholes

model for asset pricing in the risk-neutral world. The Black-Scholes partial dif-

ferential equation describes the evolution of all derivatives whose payoff is a

function on a single underlying asset following geometric Brownian motion

(GBM) and time.

Chapter 2 discusses Monte Carlo methods for valuation of European as well as

path-dependent derivatives. Various random number generators for pseudoran-

dom, quasi-random (deterministic), Sobol, and Faure sequences are discussed. Vari-

ance reduction techniques using control variates and antithetics are discussed to

overcome the computational inefﬁciency of the Monte Carlo method in its basic

form, which typically requires hundreds of thousands of simulations to achieve

good accuracy.

Chapter 3 discusses the binomial tree model for pricing European and Ameri-

can equity options. The binomial tree is shown to be a two-state discrete approxi-

mation to continuous GBM: The mean and variance of the binomial model match

the mean and variance of the lognormal distribution underlying GBM. Further-

more, the binomial model can be adapted to incorporate time-varying volatility, to

pricing path-dependent options, and to pricing derivatives depending on more than

one asset with two-variable binomial trees.

Chapter 4 generalizes binomial trees to the more ﬂexible and widely used trino-

mial trees, which approximate GBM diffusion processes with three states. It also

discusses implied trees, which are trees constructed to ﬁt observable market prices.

Thus, this method builds trees implied by the market.

Chapter 5 discusses ﬁnite-difference methods, numerical methods (actually, ex-

tensions of the trinomial method) for discretizing PDEs that (path-dependent) de-

rivatives with complex payoffs must satisfy and then solving them over a state-time

lattice. The explicit, implicit, and Crank-Nicolson ﬁnite-difference methods are dis-

xvi PREFACE

cussed as well as the alternating direction implicit method for pricing options that

depend on multiple-state variables.

Chapter 6 discusses pricing exotic options including Asian, lookback, and bar-

rier options.

Chapter 7 discusses stochastic volatility models that are used to capture volatil-

ity skews and smiles observed in the options markets. Since the constant volatility

assumption of Black-Scholes is not valid in the real world, alternative models such

as the constant elasticity of variance (CEV), jump diffusion, and multifactor sto-

chastic volatility models can each be used to ﬁt pricing model parameters to observ-

able option market quotes.

Chapter 8 focuses on statistical models for volatility estimation including

GARCH models. Chapter 9 deals with stochastic multifactor models for pricing de-

rivatives like basket options.

Chapter 10 begins the second part of the book and focuses on ﬁxed income

models. The chapter discusses single-factor short rate models including the

Vasicek, Hull-White (HW), Black-Derman-Toy (BDT), and Cox-Ingersoll-Ross

(CIR) models.

Chapter 11 focuses on tree-building procedures for the short rate models dis-

cussed in Chapter 10. It shows how to calibrate the BDT and HW models initially

to the yield curve and then to both the yield and volatility curves, and explains how

to price discount bonds, bond options, and swaptions with the models.

Chapter 12 discusses two-factor models as well as the HJM model for pricing

ﬁxed income derivatives.

Chapter 13 provides an in-depth discussion of the LIBOR market model (also

known as the Brace-Gatarek-Musiela/Jamshidian (BGM/J) model, showing how to

calibrate the model to cap and swaption volatilites for pricing. Correlation struc-

tures and stochastic extensions of the model are also discussed. The chapter shows

the difference and inconsistencies between the LIBOR forward-rate model (LFM)

for pricing caps and the Libor swap model (LSM) for pricing swaptions and swaps.

Chapter 14 discusses exotic interest rate derivatives. Bermudan swaptions,

range notes, index-amortizing swaps, trigger swaps, and quantos are discussed

along with pricing models and implementations for them. Gaussian quadrature is

also discussed as a useful tool for evaluating certain numerical integrals used in de-

rivatives pricing such as those for spread options and quantos.

Appendix A contains a probability review of important probability concepts

used throughout the book. Appendix B contains a stochastic calculus review of

Brownian motion, stochastic integrals, and Ito’s formula. Appendix C contains a

discussion of the fast Fourier transform (FFT) method, a powerful numerical tech-

nique for valuation of higher-dimensional integrals. Appendix D discusses build-

ing models, pricing engines, and calibrating models in practice with a focus on

building robust models. Appendix E contains some useful code routines including

the random number generator for generating uniform deviates for Monte Carlo

simulation from Press et al., Numerical Recipes in C (1992). Appendix F shows

the mathematical details for solving the Black-Scholes PDE using Green’s function.

Preface xvii

剩余840页未读，继续阅读

身份认证购VIP最低享 7 折!

30元优惠券

身份认证购VIP最低享 7 折!

30元优惠券

atlantiswind

粉丝: 0

C++实现的金融衍生品定价模型

C++编程地球物理学：基础与应用

C++实现PID调试的上位机程序

Visual C++实现PID温度控制程序详解

SUMS60 Derivative Pricing in Discrete Time.zip

Modeling Derivatives in C++

matlab将代码放大-Fractional-Derivative-in-Sparse-Super-Resolution:稀疏超分辨率中的分数

Options and Derivatives Programming in C++

Higher Derivative Gauge theory in d = 6 and the CP (N f −1) NLSM

凸对策中无导数博弈的改进率_Improved rates for derivative free play in convex

derivative7_derivative算法处理_

最新资源