风险和资产配置（英文版） [Risk and Asset Allocation] 下载 mobi epub pdf 电子书 2024

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内容简介

《风险和资产配置(英文版)》是一部全面介绍风险与资产分配的统计教材。多变量估计的方法分析深入，包括非正态假设下的无参和极大似然估计，压缩理论、鲁棒以及一般的贝叶斯技巧。作者用独到的眼光讲述了资产分配，给出了该学科的精华。重点突出，包含了MATLAB数学工具软件，对于以数学为中心的投资行业来说该书是一本必选书。

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目录

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

精彩书摘

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 风险和资产配置（英文版） [Risk and Asset Allocation] 下载 mobi epub pdf txt 电子书 格式

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中国受访者的态度也与日本人颇为相似。即使中国受访者普遍都意识到许多互联网公司的数据收集行为并没有遵守隐私条款，对于大多数数据类别，认为不是隐私的中国受访者远多于认为是隐私的人数。此外，中国受访者的隐私观念之淡薄还体现在有26%的人认可对个人数据的使用可以背离收集数据时的初衷，这远远高出了7%的各国平均值。

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(2)资产配置主要受某种资产类别预期收益率的客观测度驱使，因此属于以价值为导向的过程。可能的驱动因素包括在现金收益、长期债券的到期收益率基础上计算股票的预期收益，或按照股票市场股息贴现模型评估股票实用收益变化等。

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总述

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动态资产配置是根据资本市场环境及经济条件对资产配置状态进行动态调整，从而增加投资组合价值的积极战略。大多数动态资产配置一般具有如下共同特征：

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(4)资产配置一般遵循“回归均衡”的原则，这是动态资产配置中的主要利润机制。

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