風險和資産配置（英文版） [Risk and Asset Allocation] 下載 mobi epub pdf 電子書 2024

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《風險和資産配置(英文版)》是一部全麵介紹風險與資産分配的統計教材。多變量估計的方法分析深入，包括非正態假設下的無參和極大似然估計，壓縮理論、魯棒以及一般的貝葉斯技巧。作者用獨到的眼光講述瞭資産分配，給齣瞭該學科的精華。重點突齣，包含瞭MATLAB數學工具軟件，對於以數學為中心的投資行業來說該書是一本必選書。

內頁插圖

目錄

Preface
Audience and style
Structure of the work
A guided tour by means of a simplistic example
Acknowledgments

Part Ⅰ The statistics of asset allocation
Univariate statistics
1.1 Building blocks
1.2 Summary statistics
1.2.1 Location
1.2.2 Dispersion
1.2.3 Higher-order statistics
1.2.4 Graphical representations
1.3 Taxonomy of distributions
1.3.1 Uniform distribution
1.3.2 Normal distribution
1.3.3 Cauchy distribution
1.3.4 Student t distribution
1.3.5 Lognormal distribution
1.3.6 Gamma distribution
1.3.7 Empirical distribution
1.T Technical appendix
1.E Exercises

2 Multivariate statistics
2.1 Building blocks
2.2 Factorization of a distribution
2.2.1 Marginal distribution
2.2.2 Copulas
2.3 Dependence
2.4 Shape summary statistics
2.4.1 Location
2.4.2 Dispersion
2.4.3 Location-dispersion ellipsoid
2.4.4 Higher-order statistics
2.5 Dependence summary statistics
2.5.1 Measures of dependence
2.5.2 Measures of concordance
2.5.3 Correlation
2.6 Taxonomy of distributions
2.6.1 Uniform distribution
2.6.2 Normal distribution
2.6.3 Student t distribution
2.6.4 Cauchy distribution
2.6.5 Log-distributions
2.6.6 Wishart distribution
2.6.7 Empirical distribution
2.6.8 Order statistics
2.7 Special classes of distributions
2.7.1 Elliptical distributions
2.7.2 Stable distributions
2.7.3 Infinitely divisible distributions
2.T Technical appendix
2.E Exercises

3 Modeling the market
3.1 The quest for invariance
3.1.1 Equities， commodities， exchange rates
3.1.2 Fixed-income market
3.1.3 Derivatives
3.2 Projection of the invariants to the investment horizon
3.3 From invariants to market prices
3.3.1 Raw securities
3.3.2 Derivatives
3.4 Dimension reduction
3.4.1 Explicit factors
3.4.2 Hidden factors
3.4.3 Explicit vs. hidden factors
3.4.4 Notable examples
3.4.5 A useful routine
3.5 Case study： modeling the swap market
3.5.1 The market invariants
3.5.2 Dimension reduction
3.5.3 The invariants at the investment horizon
3.5.4 From invariants to prices
3.T Technical appendix
3.E Exercises

Part Ⅱ Classical asset allocation
Estimating the distribution of the market invariants
4.1 Estimators
4.1.1 Definition
4.1.2 Evaluation
4.2 Nonparametric estimators
4.2.1 Location， dispersion and hidden factors
4.2.2 Explicit factors
4.2.3 Kernel estimators
4.3 Maximum likelihood estimators
4.3.1 Location， dispersion and hidden factors
4.3.2 Explicit factors
4.3.3 The normal case
4.4 Shrinkage estimators
4.4.1 Location
4.4.2 Dispersion and hidden factors
4.4.3 Explicit factors
4.5 Robustness
4.5.1 Measures of robustness
4.5.2 Robustness of previously introduced estimators
4.5.3 Robust estimators
4.6 Practical tips
4.6.1 Detection of outliers
4.6.2 Missing data
4.6.3 Weighted estimates
4.6.4 Overlapping data
4.6.5 Zero-mean invariants
4.6.6 Model-implied estimation
4.T Technical appendix
4.E Exercises

5 Evaluating allocations
5.1 Investors objectives
5.2 Stochastic dominance
5.3 Satisfaction
5.4 Certainty-equivalent （expected utility）
5.4.1 Properties
5.4.2 Building utility functions
5.4.3 Explicit dependence on allocation
5.4.4 Sensitivity analysis
5.5 Quantile （value at risk）
5.5.1 Properties
5.5.2 Explicit dependence on allocation
5.5.3 Sensitivity analysis
5.6 Coherent indices （expected shortfall）
5.6.1 Properties
5.6.2 Building coherent indices
5.6.3 Explicit dependence on allocation
5.6.4 Sensitivity analysis
5.T Technical appendix
5.E Exercises

6 Optimizing allocations
6.1 The general approach
6.1.1 Collecting information on the investor
6.1.2 Collecting information on the market
6.1.3 Computing the optimal allocation
6.2 Constrained optimization
6.2.1 Positive orthants： linear programming
6.2.2 Ice-cream cones： second-order cone programming
6.2.3 Semidefinite cones： semidefinite programming
6.3 The mean-variance approach
6.3.1 The geometry of allocation optimization
6.3.2 Dimension reduction： the mean-variance framework
6.3.3 Setting up the mean-variance optimization
6.3.4 Mean-variance in terms of returns
6.4 Analytical solutions of the mean-variance problem
6.4.1 Efficient frontier with affme constraints
6.4.2 Efficient frontier with linear constraints
6.4.3 Effects of correlations and other parameters
6.4.4 Effects of the market dimension
6.5 Pitfalls of the mean-variance framework
6.5.1 MV as an approximation
6.5.2 MV as an index of satisfaction
6.5.3 Quadratic programming and dual formulation
6.5.4 MV on returns： estimation versus optimization
6.5.5 MV on returns： investment at different horizons
6.6 Total-return versus benchmark allocation
6.7 Case study： allocation in stocks
6.7.1 Collecting information on the investor
6.7.2 Collecting information on the market
6.7.3 Computing the optimal allocation
6.T Technical appendix
6.E Exercises

Part Ⅲ Accounting for estiamation risk
Part Ⅳ Appendices

精彩書摘

The financial markets contain many sources of risk. When dealing with severalsources of risk at a time we cannot treat them separately: the joint structureof multi-dimensionai randomness contains a wealth of information that goesbeyond the juxtaposition of the information contained in each single variable.
In this chapter we discuss multivariate statistics. The structure of thischapter reflects that of Chapter 1: to ease the comprehension of the multi-variate case refer to the respective section in that chapter. For more on thissubject see also references such as Mardia, Kent, and Bibby （1979）, Press（1982） and Morrison （2002）.
In Section 2.1 we introduce the building blocks of multivariate distributionswhich are direct generalizations of the one-dimensional case. These include thethree equivalent representations of a distribution in terms of the probabilitydensity function, the characteristic function and the cumulative distributionfunction.
In Section 2.2 we discuss the factorization of a distribution into its purelyunivariate components, namely the marginal distributions, and its purely jointcomponent, namely the copula. To present copulas we use the leading exampleof vanilla options.
In Section 2.3 we introduce the concept of independence among randomvariables and the related concept of conditional distribution.
In Section 2.4 we discuss the location summary statistics of a distributionsuch as its expected value and its mode, and the dispersion summary statisticssuch as the covariance matrix and the modal dispersion. We detail the geo- metrical representations of these statistics in terms of the location-dispersionellipsoid, .and their probabilistic interpretations in terms of a multivariateversion of Chebyshevs inequality. We conclude introducing more summarystatistics such as the multivariate moments, which provide a deeper insightinto the shape of a multivariate distribution.

前言/序言

　　In an asset allocation problem the investor， who can be the trader， or thefund manager， or the private investor， seeks the combination of securitiesthat best suit their needs in an uncertain environment. In order to determinethe optimum allocation， the investor needs to model， estimate， assess andmanage uncertainty.
　　The most popular approach to asset allocation is the mean-variance frame-work pioneered by Markowitz， where the investor aims at maximizing theportfolios expected return for a given level of variance and a given set of investment constraints. Under a few assumptions it is possible to estimate themarket parameters that feed the model and then solve the ensuing optimization problem.
　　More recently， measures of risk such as the value at risk or the expectedshortfall have found supporters in the financial community. These measuresemphasize the potential downside of an allocation more than its potential benefits. Therefore， they are better suited to handle asset allocation in modern，highly asymmetrical markets.
　　All of the above approaches are highly intuitive. Paradoxically， this can bea drawback， in that one is tempted to rush to conclusions or implementations，without pondering the underlying assumptions.
　　For instance， the term "mean-variance" hints at the identificati 風險和資産配置（英文版） [Risk and Asset Allocation] 下載 mobi epub pdf txt 電子書 格式

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買人並持有策略適用於資本市場環境和投資者的偏好變化不大，或者改變資産配置狀態的成本大於收益時的狀態。

評分☆☆☆☆☆

買人並持有策略適用於資本市場環境和投資者的偏好變化不大，或者改變資産配置狀態的成本大於收益時的狀態。

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投資組閤保險

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風險管理書籍，彆人推薦的

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在現代投資管理體製下，投資一般分為規劃、實施和優化管理三個階段。投資規劃即資産配置，它是資産組閤管理決策製定步驟中最重要的環節。對資産配置的理解必須建立在對機構投資者資産和負債問題的本質、對普通股票和固定收人證券的投資特徵等多方麵問題的深刻理解基礎之上。在此基礎上，資産管理還可以利用期貨、期權等衍生金融産品來改善資産配置的效果，也可以采用其他策略實現對資産配置的動態調整。不同配置具有自身特有的理論基礎、行為特徵和支付模式，並適用於不同的市場環境和客戶投資需求。

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資産配置在不同層麵有不同含義，從範圍上看，可分為全球資産配置、股票債券資産配置和行業風格資産配置；從時間跨度和風格類彆上看，可分為戰略性資産配置、戰術性資産配置和資産混閤配置；從資産管理人的特徵與投資者的性質上，可分為買人並持有策略(Buy-and-hold Strategy)、恒定混閤策略(Constant-mix Strategy)、投資組閤保險策略(Portfolio-insurance Strategy)和戰術性資産配置策略(Tactical Asset Allocation Strategy)。

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Springer Finance 影印版 (共11冊), 這套叢書還有《利率模型理論和實踐》,《隨機金融概要》,《金融隨機分析（第2捲）》,《金融數學中的隨機變分法》,《金融隨機分析（第1捲）》 等。

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因此，當風險資産收益率上升時，風險資産的投資比例隨之上升，如果風險資産收益繼續上升，投資組閤保險策略將取得優於買人並持有策略的結果；而如果收益轉而下降，則投資組閤保險策略的結果將因為風險資産比例的提高而受到更大的影響，從而劣於買人並持有策略的結果。

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