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金融风险和衍生证券定价理论——从统计物理到风险管理 (第2版)(影印版)

作者：
Jean-Philippe Bouchaud 等著
定价：
55.00元
ISBN：
978-7-04-023982-9
版面字数：
560.000千字
开本：
16开
全书页数：
379页
装帧形式：
平装
重点项目：
暂无
出版时间：
2008-05-30
读者对象：
学术著作
一级分类：
自然科学
二级分类：
数学与统计
三级分类：
金融数学

本书由剑桥大学出版社出版，原书名为：Financial Engineering and Computation: Principles, Mathematics, and Algorithms，是一本非常优秀的有关金融计算的图书。 如今打算在金融领域工作的学生和专家不仅要掌握先进的概念和数学模型，还要学会如何在计算上实现这些模型。《金融风险和衍生证券定价理论》内容广泛，不仅介绍了金融工程背后的理论和数学，并把重点放在了计算上，以便和金融工程在今天资本市场的实际运作保持一致。《金融风险和衍生证券定价理论》不同于大多数的有关投资、金融工程或者衍生证券方面的书，而是从金融的基本想法开始，逐步建立理论。作者提供了很多定价、风险评估以及项目组合管理的算法和理论。

  • 1 Probability theory:basic notions
    • 1.1 Introduction
    • 1.2 Probability distributions
    • 1.3 Typical values and deviations
    • 1.4 Moments and characteristic function
    • 1.5 Divergence of moments-asymptotic behaviour
    • 1.6 Gaussian distribution
    • 1.7 Log-normal distribution
    • 1.8 Levy distributions and Paretian tails
    • 1.9 Other distributions（*）
    • 1.10 Summary
  • 2 Maximum and addition of random variables
    • 2.1 Maximum of random variables
    • 2.2 Sums of random variables
      • 2.2.1 Convolutions
      • 2.2.2 Additivity of cumulants and of tail amplitudes
      • 2.2.3 Stable distributions and self-similarity
    • 2.3 Central limit theorem
      • 2.3.1 Convergence to a Gaussian
      • 2.3.2 Convergence to a Levy distribution
      • 2.3.3 Large deviations
      • 2.3.4 Steepest descent method and Cramer function（*）
      • 2.3.5 The CLT at work on simple cases
      • 2.3.6 Truncated Levy distributions
      • 2.3.7 Conclusion:survival and vanishing of tails
    • 2.4 From sum to max:progressive dominance of extremes（*）
    • 2.5 Linear correlations and fractional Brownian motion
    • 2.6 Summary
  • 3 Continuous time limit, Ito calculus and path integrals
    • 3.1 Divisibility and the continuous time limit
      • 3.1.1 Divisibility
      • 3.1.2 Infinite divisibility
      • 3.1.3 Poisson jump processes
    • 3.2 Functions of the Brownian motion and Ito calculus
      • 3.2.1 Ito's lemma
      • 3.2.2 Novikov's formula
      • 3.2.3 Stratonovich's prescription
    • 3.3 Other techniques
      • 3.3.1 Path integrals
      • 3.3.2 Girsanov's formula and the Martin-Siggia-Rose trick（*）
    • 3.4 Summary
  • 4 Analysis of empirical data
    • 4.1 Estimating probability distributions
      • 4.1.1 Cumulative distribution and densities-rank histogram
      • 4.1.2 Kolmogorov-Smirnov test
      • 4.1.3 Maximum likelihood
      • 4.1.4 Relative likelihood
      • 4.1.5 A general caveat
    • 4.2 Empirical moments:estimation and error
      • 4.2.1 Empirical mean
      • 4.2.2 Empirical variance and MAD
      • 4.2.3 Empirical kurtosis
      • 4.2.4 Error on the volatility
    • 4.3 Correlograms and variograms
      • 4.3.1 Variogram
      • 4.3.2 Correlogram
      • 4.3.3 Hurst exponent
      • 4.3.4 Correlations across different time zones
    • 4.4 Data with heterogeneous volatilities
    • 4.5 Summary
  • 5 Financial products and financial markets
    • 5.1 Introduction
    • 5.2 Financial products
      • 5.2.1 Cash（Interbank market）
      • 5.2.2 Stocks
      • 5.2.3 Stock indices
      • 5.2.4 Bonds
      • 5.2.5 Commodities
      • 5.2.6 Derivatives
    • 5.3 Financial markets
      • 5.3.1 Market participants
      • 5.3.2 Market mechanisms
      • 5.3.3 Discreteness
      • 5.3.4 The order book
      • 5.3.5 The bid-ask spread
      • 5.3.6 Transaction costs
      • 5.3.7 Time zones, overnight, seasonalities
    • 5.4 Summary
  • 6 Statistics of real prices:basic results
    • 6.1 Aim of the chapter
    • 6.2 Second-order statistics
      • 6.2.1 Price increments vs. returns
      • 6.2.2 Autocorrelation and power spectrum
    • 6.3 Distribution of returns over different time scales
      • 6.3.1 Presentation of the data
      • 6.3.2 The distribution of returns
      • 6.3.3 Convolutions
    • 6.4 Tails,what tails?
    • 6.5 Extreme markets
    • 6.6 Discussion
    • 6.7 Summary
  • 7 Non-linear correlations and volatility fluctuations
    • 7.1 Non-linear correlations and dependence
      • 7.1.1 Non identical variables
      • 7.1.2 A stochastic volatility model
      • 7.1.3 GARCH(1,1)
      • 7.1.4 Anomalous kurtosis
      • 7.1.5 The case of infinite kurtosis
    • 7.2 Non-linear correlations in financial markets:empirical results
      • 7.2.1 Anomalous decay of the cumulants
      • 7.2.2 Volatility correlations and variogram
    • 7.3 Models and mechanisms
      • 7.3.1 Multifractality and multifractal models（*）
      • 7.3.2 The microstructure of volatility
    • 7.4 Summary
  • 8 Skewness and price-volatility correlations
    • 8.1 Theoretical considerations
      • 8.1.1 Anomalous skewness of sums of random variables
      • 8.1.2 Absolute vs. relative price changes
      • 8.1.3 The additive-multiplicative crossover and the q-transformation
    • 8.2 A retarded model
      • 8.2.1 Definition and basic properties
      • 8.2.2 Skewness in the retarded model
    • 8.3 Price-volatility correlations:empirical evidence
      • 8.3.1 Leverage effect for stocks and the retarded model
      • 8.3.2 Leverage effect for indices
      • 8.3.3 Return-volume correlations
    • 8.4 The Heston model:a model with volatility fluctuations and skew
    • 8.5 Summary
  • 9 Cross-correlations
    • 9.1 Correlation matrices and principal component analysis
      • 9.1.1 Introduction
      • 9.1.2 Gaussian correlated variables
      • 9.1.3 Empirical correlation matrices
    • 9.2 Non-Gaussian correlated variables
      • 9.2.1 Sums of non Gaussian variables
      • 9.2.2 Non-linear transformation of correlated Gaussian variables
      • 9.2.3 Copulas
      • 9.2.4 Comparison of the two models
      • 9.2.5 Multivariate Student distributions
      • 9.2.6 Multivariate Levy variables（*）
      • 9.2.7 Weakly non Gaussian correlated variables（*）
    • 9.3 Factors and clusters
      • 9.3.1 One factor models
      • 9.3.2 Multi-factor models
      • 9.3.3 Partition around medoids
      • 9.3.4 Eigenvector clustering
      • 9.3.5 Maximum spanning tree
    • 9.4 Summary
    • 9.5 Appendix A:central limit theorem for random matrices
    • 9.6 Appendix B: density of eigenvalues for random correlation matrices
  • 10 Risk measures
    • 10.1 Risk measurement and diversification
    • 10.2 Risk and volatility
    • 10.3 Risk of loss, value at risk'(VaR) and expected shortfall
      • 10.3.1 Introduction
      • 10.3.2 Value-at-risk
      • 10.3.3 Expected shortfall
    • 10.4 Temporal aspects: drawdown and cumulated loss
    • 10.5 Diversification and utility-satisfaction thresholds
    • 10.6 Summary
  • 11 Extreme correlations and variety
    • 11.1 Extreme event correlations
      • 11.1.1 Correlations conditioned on large market moves
      • 11.1.2 Real data and surrogate data
      • 11.1.3 Conditioning on large individual stock returns:exceedance correlations
      • 11.1.4 Tail dependence
      • 11.1.5 Tail covariance（*）
    • 11.2 Variety and conditional statistics of the residuals
      • 11.2.1 The variety
      • 11.2.2 The variety in the one-factor model
      • 11.2.3 Conditional variety of the residuals
      • 11.2.4 Conditional skewness of the residuals
    • 11.3 Summary
    • 11.4 Appendix C:some useful results on power-law variables
  • 12 Optimal portfolios
    • 12.1 Portfolios of uncorrelated assets
      • 12.1.1 Uncorrelated Gaussian assets
      • 12.1.2 Uncorrelated'power-law' assets
      • 12.1.3 'Exponential' assets
      • 12.1.4 General case: optimal portfolio and VaR（*）
    • 12.2 Portfolios of correlated assets
      • 12.2.1 Correlated Gaussian fluctuations
      • 12.2.2 Optimal portfolios with non-linear constraints（*）
      • 12.2.3 'Power-law' fluctuations-linear model（*）
      • 12.2.4 'Power-law' fluctuations-Student model（*）
    • 12.3 Optimized trading
    • 12.4 Value-at-risk-general non-linear portfolios（*）
      • 12.4.1 Outline of the method: identifying worst cases
      • 12.4.2 Numerical test of the method
    • 12.5 Summary
  • 13 Futures and options: fundamental concepts
    • 13.1 Introduction
      • 13.1.1 Aim of the chapter
      • 13.1.2 Strategies in uncertain conditions
      • 13.1.3 Trading strategies and efficient markets
    • 13.2 Futures and forwards
      • 13.2.1 Setting the stage
      • 13.2.2 Global financial balance
      • 13.2.3 Riskless hedge
      • 13.2.4 Conclusion:global balance and arbitrage
    • 13.3 Options:definition and valuation
      • 13.3.1 Setting the stage
      • 13.3.2 Orders of magnitude
      • 13.3.3 Quantitative analysis-option price
      • 13.3.4 Real option prices, volatility smile and ‘implied’kurtosis
      • 13.3.5 The case of an infinite kurtosis
    • 13.4 Summary
  • 14 Options:hedging and residual risk
    • 14.1 Introduction
    • 14.2 Optimal hedging strategies
      • 14.2.1 A simple case: static hedging
      • 14.2.2 The general case and‘△’hedging
      • 14.2.3 Global hedging vs. instantaneous hedging
    • 14.3 Residual risk
      • 14.3.1 The Black-Scholes miracle
      • 14.3.2 The‘stop-loss’strategy does not work
      • 14.3.3 Instantaneous residual risk and kurtosis risk
      • 14.3.4 Stochastic volatility models
    • 14.4 Hedging errors. A variational point of view
    • 14.5 Other measures of risk-hedging and VaR （*）
    • 14.6 Conclusion of the chapter
    • 14.7 Summary
    • 14.8 Appendix D
  • 15 Options: the role of drift and correlations
    • 15.1 Influence of drift on optimally hedged option
      • 15.1.1 A perturbative expansion
      • 15.1.2 ‘Risk neutral’probability and martingales
    • 15.2 Drift risk and delta-hedged options
      • 15.2.1 Hedging the drift risk
      • 15.2.2 The price of delta-hedged options
      • 15.2.3 A general option pricing formula
    • 15.3 Pricing and hedging in the presence of temporal correlations（*）
      • 15.3.1 A general model of correlations
      • 15.3.2 Derivative pricing with small correlations
      • 15.3.3 The case of delta-hedging
    • 15.4 Conclusion
      • 15.4.1 Is the price of an option unique?
      • 15.4.2 Should one always optimally hedge?
    • 15.5 Summary
    • 15.6 Appendix E
  • 16 Options:the Black and Scholes model
    • 16.1 Ito calculus and the Black-Scholes equation
      • 16.1.1 The Gaussian Bachelier model
      • 16.1.2 Solution and Martingale
      • 16.1.3 Time value and the cost of hedging
      • 16.1.4 The Log-normal Black-Scholes model
      • 16.1.5 General pricing and hedging in a Brownian world
      • 16.1.6 The Greeks
    • 16.2 Drift and hedge in the Gaussian model（*）
      • 16.2.1 Constant drift
      • 16.2.2 Price dependent drift and the Ornstein-Uhlenbeck paradox
    • 16.3 The binomial model
    • 16.4 Summary
  • 17 Options:some more specific problems
    • 17.1 Other elements of the balance sheet
      • 17.1.1 Interest rate and continuous dividends
      • 17.1.2 Interest rate corrections to the hedging strategy
      • 17.1.3 Discrete dividends
      • 17.1.4 Transaction costs
    • 17.2 Other types of options
      • 17.2.1 ‘Put-call’parity
      • 17.2.2 ‘Digital’options
      • 17.2.3 ‘Asian’options
      • 17.2.4 ‘American’options
      • 17.2.5 ‘Barrier’options（*）
      • 17.2.6 Other types of options
    • 17.3 The ‘Greeks’and risk control
    • 17.4 Risk diversification（*）
    • 17.5 Summary
  • 18 Options:minimum variance Monte-Carlo
    • 18.1 Plain Monte-Carlo
      • 18.1.1 Motivation and basic principle
      • 18.1.2 Pricing the forward exactly
      • 18.1.3 Calculating the Greeks
      • 18.1.4 Drawbacks of the method
    • 18.2 An ‘hedged’Monte-Carlo method
      • 18.2.1 Basic principle of the method
      • 18.2.2 A linear parameterization of the price and hedge
      • 18.2.3 The Black-Scholes limit
    • 18.3 Non Gaussian models and purely historical option pricing
    • 18.4 Discussion and extensions. Calibration
    • 18.5 Summary
    • 18.6 Appendix F:generating some random variables
  • 19 The yield curve
    • 19.1 Introduction
    • 19.2 The bond market
    • 19.3 Hedging bonds with other bonds
      • 19.3.1 The general problem
      • 19.3.2 The continuous time Gaussian limit
    • 19.4 The equation for bond pricing
      • 19.4.1 A general solution
      • 19.4.2 The Vasicek model
      • 19.4.3 Forward rates
      • 19.4.4 More general models
    • 19.5 Empirical study of the forward rate curve
      • 19.5.1 Data and notations
      • 19.5.2 Quantities of interest and data analysis
    • 19.6 Theoretical considerations（*）
      • 19.6.1 Comparison with the Vasicek model
      • 19.6.2 Market price of risk
      • 19.6.3 Risk-premium and the θ law
    • 19.7 Summary
    • 19.8 Appendix G:optimal portfolio of bonds
  • 20 Simple mechanisms for anomalous price statistics
    • 20.1 Introduction
    • 20.2 Simple models for herding and mimicry
      • 20.2.1 Herding and percolation
      • 20.2.2 Avalanches of opinion changes
    • 20.3 Models of feedback effects on price fluctuations
      • 20.3.1 Risk-aversion induced crashes
      • 20.3.2 A simple model with volatility correlations and tails
      • 20.3.3 Mechanisms for long ranged volatility correlations
    • 20.4 The Minority Game
    • 20.5 Summary
  • Index of most important symbols

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