《金融中的數值方法和最佳化》是2013年由世界圖書出版公司出版的一本書籍，作者吉利。
基本介紹
- 書名：金融中的數值方法和最佳化
- 作者：吉利
- ISBN：9787510052651
- 頁數：584
- 定價：89.00元
- 出版社：世界圖書出版公司
- 出版時間：2013-1
內容簡介,圖書目錄,
內容簡介
《金融中的數值方法和最佳化(英文)》旨在為讀者介紹金融計算工具—基本數值分析和計算技巧，如期權定價、並突出了模擬和最佳化的重要性，用許多章講述投資組合保險和風險估計問題。特別地，有幾章用於講述最佳化探索和如何將他們套用於投資組合的選擇、估值的校準和期權定價模型。這些具體的例子讓讀者學習了解決問題的具體步驟，以及將這些步驟舉一反三。同時，這些套用使得《金融中的數值方法和最佳化(英文)》的參考價值大大提高。
圖書目錄
List of Algorithms
Acknowledgements
1.Introduction
1.1 About this book
1.2 Principles
1.3 on software
1.4 on approximations andaccuracy
1.5 Summary: the theme of the book
Part One Fundamentals
Acknowledgements
1.Introduction
1.1 About this book
1.2 Principles
1.3 on software
1.4 on approximations andaccuracy
1.5 Summary: the theme of the book
Part One Fundamentals
2. Numerical analysisin a nutshell
2.1 Computer arithmetic
Representation of real numbers
Machine precision
Example of limitations of floating point arithmetic
2.2 Measuringerrors
2.3 Approximating derivatives with finite differences
Approximating first-order derivatives
Approximating second-order derivatives
Partial derivatives
How to choose h
Truncation error for forward difference
2.4 Numerical instability and ill-conditioning
Example of a numerically unstable algorithm
Example of an ill-conditioned problem
2.5Condition number of a matrix
Comments and examples
2.6 A primer on algorithmic and computational complexity
2.6.1 Criteria for comparison
Order of complexity and classification
2.A Operation count for basiclinear algebra operations
2.1 Computer arithmetic
Representation of real numbers
Machine precision
Example of limitations of floating point arithmetic
2.2 Measuringerrors
2.3 Approximating derivatives with finite differences
Approximating first-order derivatives
Approximating second-order derivatives
Partial derivatives
How to choose h
Truncation error for forward difference
2.4 Numerical instability and ill-conditioning
Example of a numerically unstable algorithm
Example of an ill-conditioned problem
2.5Condition number of a matrix
Comments and examples
2.6 A primer on algorithmic and computational complexity
2.6.1 Criteria for comparison
Order of complexity and classification
2.A Operation count for basiclinear algebra operations
3. Linear equations and Least Squares problems
Choice of method
3.1 Direct methods
3.1.1 Triangular systems
3.1.2 LU factorization
3.1.3 Cholesky factorization
3.1.4 QRdecomposition
3.1.5 Singular value decomposition
3.2 Iterative methods
3.2.1 Jacobi, Gauss-Seidel, and SOR
Successive overrelaxation
3.2.2 Convergence of niterative methods
3.2.3 General structure of algorithms for iterative methods
3.2.4 Block iterative methods
3.3 Sparse linear systems
3.3.1 Tridiagonal systems
3.3.2 Irregular sparse matrices
3.3.3 Structural properties of sparse matrices
3.4 The Least Squares problem
3.4.1 Method of normal equations
3.4.2 Least Squares via QR factorization
3.4.3 Least Squares via SVD decomposition
3.4.4 Final remarks
The backslash operator in Matlab
Choice of method
3.1 Direct methods
3.1.1 Triangular systems
3.1.2 LU factorization
3.1.3 Cholesky factorization
3.1.4 QRdecomposition
3.1.5 Singular value decomposition
3.2 Iterative methods
3.2.1 Jacobi, Gauss-Seidel, and SOR
Successive overrelaxation
3.2.2 Convergence of niterative methods
3.2.3 General structure of algorithms for iterative methods
3.2.4 Block iterative methods
3.3 Sparse linear systems
3.3.1 Tridiagonal systems
3.3.2 Irregular sparse matrices
3.3.3 Structural properties of sparse matrices
3.4 The Least Squares problem
3.4.1 Method of normal equations
3.4.2 Least Squares via QR factorization
3.4.3 Least Squares via SVD decomposition
3.4.4 Final remarks
The backslash operator in Matlab
4. Finite difference methods
4.1 An example of a numerical solution
A first numerical approximation
A second numerical approximation
4.2 Classification of differential equations
4.3 The Black-Scholes equation
4.3.1 Explicit, implicit, and θ-methods
4.3.2 Initial and boundary conditions and definition of thegrid
4.3.3 Implementation of the θ-method with Matlab
4.3.4 Stability
4.3.5 Coordinate transformation of space variables
4.4 American options
4.A A note on Matlab's function spdiags
4.1 An example of a numerical solution
A first numerical approximation
A second numerical approximation
4.2 Classification of differential equations
4.3 The Black-Scholes equation
4.3.1 Explicit, implicit, and θ-methods
4.3.2 Initial and boundary conditions and definition of thegrid
4.3.3 Implementation of the θ-method with Matlab
4.3.4 Stability
4.3.5 Coordinate transformation of space variables
4.4 American options
4.A A note on Matlab's function spdiags
5.Binomialtrees
5.1 Motivation
Matching moments
5.2 Growing the tree
5.2.1 Implementing a tree
5.2.2 Vectorization
5.2.3 Binomial expansion
5.3 Early exerase
5.4 Dividends
5.5 The Greeks
Greeks from the tree
Part Two Simulation
5.1 Motivation
Matching moments
5.2 Growing the tree
5.2.1 Implementing a tree
5.2.2 Vectorization
5.2.3 Binomial expansion
5.3 Early exerase
5.4 Dividends
5.5 The Greeks
Greeks from the tree
Part Two Simulation
6. Generatmg random numbers
6.1 Monte Carlo methods and sampling
6.1.1 How it allbegan
6.1.2 Financialapplications
6.2 Uniform random number generators
6.2.1 Congruential generators
6.2.2 Mersenne Twister
6.3 Nonuniform distributions
6.3.1 The inversion method
6.3.2 Acceptance-rejection method
6.4 Specialized methods for selected distributions
6.4.1 Normal distribution
6.4.2 Higher order moments and the Cornish-Fisher expansion
6.4.3 Further distributions
6.5 Sampling from a discrete set
6.5.1 Discrete uniform selection
6.5.2 Roulette wheel selection
6.5.3 Random permutations and shuffling
6.6 Sampling errors-and how to reduce them
6.6.1 The basic problem
6.6.2 Quasi-Monte Carlo
6.6.3 Stratified sampling
6.6.4 Variance reduction
6.7Drawing from empirical distributions
6.7.1 Data randomization
6.7.2 Bootstrap
6.8 Controlled experiments and experimental design
6.8.1 Replicability and ceteris paribus analysis
6.8.2 Available random number generators in Matlab
6.8.3 Uniform random numbers from Matlab's rand function
6.8.4 Gaussian random numbers from Matlab's randn function
6.8.5 Remedies
6.1 Monte Carlo methods and sampling
6.1.1 How it allbegan
6.1.2 Financialapplications
6.2 Uniform random number generators
6.2.1 Congruential generators
6.2.2 Mersenne Twister
6.3 Nonuniform distributions
6.3.1 The inversion method
6.3.2 Acceptance-rejection method
6.4 Specialized methods for selected distributions
6.4.1 Normal distribution
6.4.2 Higher order moments and the Cornish-Fisher expansion
6.4.3 Further distributions
6.5 Sampling from a discrete set
6.5.1 Discrete uniform selection
6.5.2 Roulette wheel selection
6.5.3 Random permutations and shuffling
6.6 Sampling errors-and how to reduce them
6.6.1 The basic problem
6.6.2 Quasi-Monte Carlo
6.6.3 Stratified sampling
6.6.4 Variance reduction
6.7Drawing from empirical distributions
6.7.1 Data randomization
6.7.2 Bootstrap
6.8 Controlled experiments and experimental design
6.8.1 Replicability and ceteris paribus analysis
6.8.2 Available random number generators in Matlab
6.8.3 Uniform random numbers from Matlab's rand function
6.8.4 Gaussian random numbers from Matlab's randn function
6.8.5 Remedies
7.Modelingdependenaes
7.1 Transformation methods
7.1.1 Linear correlation
7.1.2 Rank correlation
7.2 Markov chains
7.2.1 Concepts
7.2.2 The Metropolis algorithm
7.3 Copula models
7.3.1 Concepts
7.3.2 Simulation using copulas
7.1 Transformation methods
7.1.1 Linear correlation
7.1.2 Rank correlation
7.2 Markov chains
7.2.1 Concepts
7.2.2 The Metropolis algorithm
7.3 Copula models
7.3.1 Concepts
7.3.2 Simulation using copulas
8. A gentle introduction to financial simulation
8.1 Setting the stage
8.2 Single-period simulations
8.2.1 Terminal asset prices
8.2.2 l-over-N portfolios
8.2.3 European options
8.2.4 VaR of a covered put portfolio
8.3 Simple price processes
8.4 Processes with memoryin thelevels of returns
8.4.1 Efficient versus adaptive markets
8.4.2 Moving averages
8.4.3 Autoregressive models
8.4.4 Autoregressive moving average （ARMA） models
8.4.5 Simulating ARMA models
8.4.6 Models withlong-term memory
8.5 Time-varying volatility
8.5.1 Theconcepts
8.5.2 Autocorrelated time-varying volatility
8.5.3 Simulating GARCH processes
8.5.4 Selected further autoregressive volatility models
8.6 Adaptive expectations and patternsin price processes
8.6.1 Price-earningsmodels
8.6.2 Models with learning
8.7Historical simulation
8.7.1 Backtesting
8.7.2 Bootstrap
8.8 Agent-based models and complexity
8.1 Setting the stage
8.2 Single-period simulations
8.2.1 Terminal asset prices
8.2.2 l-over-N portfolios
8.2.3 European options
8.2.4 VaR of a covered put portfolio
8.3 Simple price processes
8.4 Processes with memoryin thelevels of returns
8.4.1 Efficient versus adaptive markets
8.4.2 Moving averages
8.4.3 Autoregressive models
8.4.4 Autoregressive moving average （ARMA） models
8.4.5 Simulating ARMA models
8.4.6 Models withlong-term memory
8.5 Time-varying volatility
8.5.1 Theconcepts
8.5.2 Autocorrelated time-varying volatility
8.5.3 Simulating GARCH processes
8.5.4 Selected further autoregressive volatility models
8.6 Adaptive expectations and patternsin price processes
8.6.1 Price-earningsmodels
8.6.2 Models with learning
8.7Historical simulation
8.7.1 Backtesting
8.7.2 Bootstrap
8.8 Agent-based models and complexity
9. Financial simulation at work: some case studies
9.1Constant proportion portfolio insurance （CPPI）
9.1.1 Basicconcepts
9.1.2 Bootstrap
9.2 VaR estimation with Extreme Value Theory
9.2.1 Basicconcepts
9.2.2 Scaling the data
9.2.3 Using Extreme Value Theory
9.3 Option pricing
9.3.1 Modeling prices
9.3.2 Pricingmodels
9.3.3 Greeks
9.3.4 Quasi-Monte Carlo
Part Three Optimization
9.1Constant proportion portfolio insurance （CPPI）
9.1.1 Basicconcepts
9.1.2 Bootstrap
9.2 VaR estimation with Extreme Value Theory
9.2.1 Basicconcepts
9.2.2 Scaling the data
9.2.3 Using Extreme Value Theory
9.3 Option pricing
9.3.1 Modeling prices
9.3.2 Pricingmodels
9.3.3 Greeks
9.3.4 Quasi-Monte Carlo
Part Three Optimization
