基于非参数估计框架的期望效用最大化最优投资组合

本文基于期望效用最大化和非参数估计框架研究了最优投资组合选择问题。和以往大多文献假定资产收益率服从某些特定分布不同资产收益率的分布类型无需作任何假设。首先在一般效用函数下,利用组合收益率密度函数的非参数核估计给出了期望效用的基本非参数估计公式,并建立了期望效用最大化投资组合选择问题的基本框架。然后,在投资者具有幂效用函数的假定下,给出了期望效用具体的非参数计算公式,并给出了求解最大期望效用的数值算法。最后,利用中国证券交易所11支股票日收益率的真实数据给出了一个数值算例。本文提出的非参数估计框架具有一般性,还可以进一步用来研究各种现实条件下（如各种现实不等式约束和具有交易成本）的投资组合管理问题。     

基于风险与收益对称的最优资本结构研究

根据风险与收益的基本关系,以及权益收益率与投资收益率的相互联系,从企业规模角度,建立规模序列计量经济模型,在一般性基础上讨论了投资收益率及其随机扰动变量的近似概率分布,进而得出权益资金收益率的近似概率分布,然后根据筹资者对风险和收益的权衡,以企业股权资金收益率的方差作为筹资风险的度量,建立关于权益资金收益率的企业家效用函数,并根据其期望效用最大化,得到最优资本结构.     

期望未来损失约束下的最优投资问题

在标准的Black-Scholes 型金融市场下,建立了期望未来损失( expected future loss;EFL)约束下基于终端财富效用最大化的投资组合选择模型.运用鞅和优化方法,得到了一般效用投资者在投资计划期内任意时刻的最优财富和最优投资组合选择策略.在对数效用函数下,得到了投资者在投资计划期内任意时刻的最优财富和最优投资组合选择策略的显式表达式.     

动态均值-方差资产组合的有效性:时变风险容忍度视角

尽管均值-方差模型在静态资产组合优化过程中得到广泛运用并证明是有效的,但在动态情景下,均值-方差模型运用于动态资产组合优化过程中的有效性问题引起人们的质疑：一是常风险规避系数的设定不符合事实;二是投资者偏好设定不符合动态情景下的主流效用函数族。鉴于此,本文假设投资者风险容忍度是资产组合投资期与投资者期望收益率的函数,研究动态均值-方差资产组合的有效性问题。基于均值-方差分析框架构建时变风险容忍度下的动态资产组合模型;运用伊藤定理和拉格朗日乘子法获得最优资产组合封闭解;基于二次效用偏好下的动态资产组合,从资产组合策略、夏普率、确定性等价收益率和有效前沿等视角验证动态均值-方差资产组合策略和业绩,并予以实证。结果表明：动态均值-方差资产组合不但具有同等业绩而且体现了其灵活性和风险对冲价值;尽管动态均值-方差资产组合表现出高杠杆性,但其确定性等价收益率较高,且随投资期的增加呈现倒U型趋势;动态均值-方差资产组合的投资期效应显著,强于投资者期望收益率。研究指出,时变风险容忍度下的动态均值-方差资产组合管理和优化策略有效,但在短投资期（低于12个月）和（或）低期望收益率下并不适用。研究不但拓展了均值-方差模型在动态情境下的应用,而且体现了投资者源于心理和（或）其财富变化的投资行为调整。     

具有不同效用函数的最优投资组合分析

最优投资组合是解决在一定的约束条件下,选择交易策略使投资者的效用函数最大化问题。如果市场不存在套利机会,在风险中性概率或者鞅概率测度下存在最优交易策略。本文从投资者所拥有不同的效用函数出发,分别推出其最优财富以及最优期望效用值。     

考虑损失规避的期望效用投资组合模型

投资者行为的本质特征会影响投资者对不同风险资产的投资决策。本文从期望效用最大化的角度研究不同风险资产的配置问题,并将人的心理活动引入了投资组合模型之中。论文借鉴了损失规避这个重要的概念,将投资者的效用函数表示为期末财富和财富变化的函数;建立了基于损失规避的最优投资组合模型;并对我国上海股票市场进行了实证分析,得到了在财富变化的不同关心程度下的组合前沿。     

基于损失厌恶的非线性投资组合问题

借鉴Kahneman&Tversky(1979)提出的展望理论,本文从期望效用最大化的角度研究不同风险资产的配置问题。通过将投资者的效用函数表示为期末财富变化的函数,建立了基于损失厌恶的最优投资组合模型。针对S-型效用函数在参考点附近的非光滑问题,设计了一个三次样条函数对其进行光滑化处理;同时,还设计了一个随机搜索算法用以处理由于目标函数的非凹性而导致出现多个局部最优解的问题。最后利用中国证券市场的实际数据验证了该模型的合理性和有效性。     

分形布朗运动下最优投保和消费策略

基于Merton的最优消费和投资组合模型,通过假设风险资产的价格变化服从几何分形布朗运动,探讨了一类具有人寿保险的最优投资消费问题.首先根据投资者在整个生命周期的消费和投保效用期望值最大的原则,利用贝尔曼动态规划原理,建立了最优投保和消费策略模型.然后在给定消费和遗赠评价效用函数的情况下,给出了最优投保和消费的闭式解,并获得了最优投资组合受模型参数变化影响的一些重要性质.最后,通过数值例子讨论了时间间隔、赫斯特指数变化时最优投保和最大期望效用的变化趋势.     

引入流动性一定降低期末财富效用吗?——基于理论视角的探讨

考察在单期投资下,以投资者期末变现后财富效用最大化为目标,将流动性作为变现成本引入效用函数模型中将投资管理的视角从二维空间拓展至三维空间.研究结果表明:①期末期望财富效用是非流动性及非流动性方差的减函数;②考虑流动性并不一定降低投资者的期末期望财富效用.     

不同存贷利率下极大化终止时刻期望效用

假设投资者具有效用函数U(x)=xρ/ρ,0<ρ<1,基于贷款利率高于存款利率的实际,运用随机分析中的It公式,得到了个体投资者极大化期望终止效用的最优投资策略显式解,该策略易于投资者操作使用.     

A Polynomial Optimization Approach to Principal–Agent Problems

This paper presents a new method for the analysis of moral hazard principal–agent problems. The new approach avoids the stringent assumptions on the distribution of outcomes made by the classical first‐order approach and instead only requires the agent's expected utility to be a rational function of the action. This assumption allows for a reformulation of the agent's utility maximization problem as an equivalent system of equations and inequalities. This reformulation in turn transforms the principal's utility maximization problem into a nonlinear program. Under the additional assumptions that the principal's expected utility is a polynomial and the agent's expected utility is rational in the wage, the final nonlinear program can be solved to global optimality. The paper also shows how to first approximate expected utility functions that are not rational by polynomials, so that the polynomial optimization approach can be applied to compute an approximate solution to nonpolynomial problems. Finally, the paper demonstrates that the polynomial optimization approach extends to principal–agent models with multidimensional action sets.     

基于概率权重函数和随机占优准则的开放式基金评级

传统的开放式基金评级方法存在两个缺陷,首先是忽视了现实中投资者是如何做决策的,假定投资者对利益和损失的主观态度相同;其次是忽略了现实的样本性质,假定随机收益的样本达到渐近正态的规模。通过期望效用的高阶泰勒序列展开建立超额收益的高阶矩和效用函数的关系,以高阶矩为约束条件估计样本的经验概率,再对经验概率进行决策权重调整。在此基础上,通过扩展夏普比和应用随机占优准则进行基金评级,并对645种开放式基金进行了案例分析。     

金融市场条件高阶矩风险与动态组合投资

针对传统组合投资理论没有考虑高阶矩风险和静态处理问题两大缺陷,提出多元GARCHSK模型用于衡量时变的高阶矩风险,基于效用函数的Taylor展开推导出带有高阶矩风险的动态组合投资策略,并利用遗传算法进行求解。实证研究表明,中国股市不仅存在高阶矩风险,而且风险具有时变特征;为防范高阶矩风险,投资者需要动态地修正他的资产配置权重。     

The Law of Demand and Risk Aversion

This note proposes a necessary and sufficient condition on a utility function to guarantee that it generates a demand function satisfying the law of demand. This condition can be interpreted in terms of an agent's attitude towards lotteries in commodity space. As an application, we show that when an agent has an expected utility function, her demand for securities satisfies the law of demand if her coefficient of relative risk aversion does not vary by more than 4.     

The Monotonicity of Individual and Market Demand

This paper studies the monotonicity of individual and market demand with the aid of the indirect utility function. We identify sufficient (and in a sense, necessary) conditions on an agent's indirect utility which will guarantee that he has a monotonic demand function. Our conditions also point to a natural way of extending the result of Hildenbrand (1983). Hildenbrand showed that market demand is monontonic if the income distribution has a downward sloping density, even though individual agents' demand function might violate monotonicity. Using the indirect utility function, we introduce a measure of violations of individual monotonicity that allows us to identify a larger class of density functions that will generate a monotonic market demand.     

基于累积前景理论的报酬契约模型设计与分析

传统报酬契约的机制设计以委托代理模型为依据,并基于期望效用理论加以分析、讨论。实证研究认为,委托代理模型中决策者的行为偏好更符合累积前景理论假设,即委托人与代理人均会依据不同预期收益呈现相应的风险偏好,并对收益的概率估计赋以非线性的决策权重。本文引入累积前景理论重构报酬契约模型,通过构建价值函数、决策权重函数,设置参照点,研究委托人、代理人均无明显风险偏好表征下的决策行为。模型结果表明,在参照点为0的情况下,代理人的最优努力水平与自身风险态度系数或决策权重系数无关;在信息透明情形下,委托人制定的最优利润分享系数,完全由自身的风险态度系数与决策权重系数决定。据此,本文提出系列激励机制优化对策。     

Nonparametric Engel Curves and Revealed Preference

This paper applies revealed preference theory to the nonparametric statistical analysis of consumer demand. Knowledge of expansion paths is shown to improve the power of nonparametric tests of revealed preference. The tightest bounds on indifference surfaces and welfare measures are derived using an algorithm for which revealed preference conditions are shown to guarantee convergence. Nonparametric Engel curves are used to estimate expansion paths and provide a stochastic structure within which to examine the consistency of household level data and revealed preference theory. An application is made to a long time series of repeated cross–sections from the Family Expenditure Survey for Britain. The consistency of these data with revealed preference theory is examined. For periods of consistency with revealed preference, tight bounds are placed on true cost of living indices.     

Distribution of Aggregate Utility Using Stochastic Elements of Additive Multiattribute Utility Models

Conventionally, elements of a multiattribute utility model characterizing a decision maker's preferences, such as attribute weights and attribute utilities, are treated as deterministic, which may be unrealistic because assessment of such elements can be imprecise and erroneous, or differ among a group of individuals. Moreover, attempting to make precise assessments can be time consuming and cognitively demanding. We propose to treat such elements as stochastic variables to account for inconsistency and imprecision in such assessments. Under these assumptions, we develop procedures for computing the probability distribution of aggregate utility for an additive multiattribute utility function (MAUF), based on the Edgeworth expansion. When the distributions of aggregate utility for all alternatives in a decision problem are known, stochastic dominance can then be invoked to filter inferior alternatives. We show that, under certain mild conditions, the aggregate utility distribution approaches normality as the number of attributes increases. Thus, only a few terms from the Edgeworth expansion with a standard normal density as the base function will be sufficient for approximating an aggregate utility distribution in practice. Moreover, the more symmetric the attribute utility distributions, the fewer the attributes to achieve normality. The Edgeworth expansion thus can provide a basis for a computationally viable approach for representing an aggregate utility distribution with imprecisely specified attribute weights and utilities assessments (or differing weights and utilities across individuals). Practical guidelines for using the Edgeworth approximation are given. The proposed methodology is illustrated using a vendor selection problem.     

需求受销售价格与变质时间影响的时滞变质品供应链协调研究

在由单个生产商和单个销售商组成的两级时滞变质品供应链中，销售商面临的市场需求受销售价格和物品变质时间影响。分别建立分散决策与集中决策下的时滞变质品供应链定价与补货模型，得到两种决策模式下的销售价格与订货批量均衡解。通过比较两种决策模式发现，集中决策下的销售价格更低且订货批量和预期利润更高。引入数量折扣契约对时滞变质品供应链进行协调，并借助常相对风险规避型效用函数和纳什讨价还价理论来分配协调后的预期利润增量。通过数值算例演示了决策变量求解过程，敏感性分析给出了时滞变质品特性参数变动时对决策变量产生的影响。