部分信息下资产收益率发生紊乱的最优投资策略

本文研究了在部分信息且市场利率非零的情形下,资产预期收益率发生紊乱(disorder)时,终端净财富的期望指数效用最大化问题.利用半鞅和倒向随机微分方程(BSDE)刻画价值过程的方法,获得了最优交易策略和价值过程的明确表达式,推广了一般框架下最优投资组合的研究结果.     

隐半马尔科夫市场下投资组合的动态决策问题（英文）

在长期投资组合中,既要考虑金融资产自身的价格波动风险,又要关注宏观经济环境变化及通胀风险对各资产的影响.为此,本文建立宏观经济环境服从隐半马尔科夫链的金融市场,由通胀指数债券、银行存款和普通股票构成投资组合.由期望效用最大化构建随机控制模型,考虑到该隐半马尔科夫市场的不完备性,进一步将该投资组合问题视作部分信息的随机控制问题,并利用隐半马尔科夫滤波将部分信息控制问题转化问完全信息问题,得到解的存在唯一性.本文最后给出若干数值模拟结果,结果显示本文建立的模型优于普通市场的模型.     

奈特不确定下资产收益率发生紊乱的最优投资策略

在部分信息且市场利率非零的情形下,应用α-极大极小期望效用(α-MEU)模型区别投资者的含糊和含糊态度,研究资产预期收益率发生紊乱(disorder)时的投资组合问题.首先,利用倒向随机微分方程理论刻画了α-MEU.其次,给出紊乱时刻的后验概率过程满足的随机微分方程(SDE),以及价值过程所满足的倒向随机微分方程(BSDE).最后,应用鞅论解出指数效用时的最优交易策略和价值过程的明确表达式.     

部分信息下带有保费返还条款的DC养老金时间一致性投资策略

本文研究了部分信息下带有保费返还条款的DC养老金的时间一致性投资策略.假设养老金管理者只拥有股票的部分信息,即只能观测到股票的价格,而不能观测到股票的收益率.养老金带有保费返还条款,在基金累积期死亡的参与者可以获得前期缴纳的所有保费.此外,本文还考虑了通胀风险以及随机工资.首先,利用卡尔曼滤波理论,将部分信息情形下的最优投资组合问题转化为一个完全信息情形下的问题.然后,通过求解一个扩展的HJB方程,得到时间一致性投资策略和最优值函数,并给出了均值–方差有效前沿的参数表达式.最后,用蒙特卡洛方法进行数值模拟,分析了部分信息和保费返还条款对股票投资比例和有效前沿的影响,并给出了相应的经济学解释.     

部分信息下股票付息的Black-Scholes期权定价公式和一类最优投资问题

假定金融市场中的投资者仅掌握部分信息,即投资者仅能观测到股票和债券价格,而股票的瞬时回报率和市场的噪声源不能观测.对存款利率和贷款利率不相等的情形,运用凸分析和滤波技术得到了部分信息下股票付红利的Black-Scholes期权定价公式.对部分信息下最大化终端财富的问题,获得了最优投资策略.     

随机通胀风险下基于半鞅理论的最优投资策略

本文在半鞅理论框架下,构建包括可交易风险资产、不可交易风险资产和未定权益的金融投资模型。在考虑随机通胀风险和获取部分市场信息的情形下,研究投资经理人终端真实净财富指数效用最大化问题。运用滤波理论、半鞅和倒向随机微分方程(BSDE)理论,求解带有随机通胀风险的最优投资策略和价值过程精确解。数值分析结果发现,可交易风险资产最优投资额随着预期通胀率的增加而减少,投资价值呈先增后减态势。当通胀波动率无限接近可交易风险资产名义价格波动率时,通胀风险可完全对冲,投资人会不断追加在可交易风险资产的投资额,以期实现终端真实净财富期望指数效用最大化。研究结果为金融市场的投资决策提供更加科学的理论参考。     

Heston模型下基于HARA效用准则的资产-负债管理策略

研究了Heston随机波动率模型下带有负债过程的动态投资组合问题,并且假设风险资产价格过程满足Heston随机波动率模型,负债过程服从带漂移的布朗运动.金融市场由一种无风险资产和一种风险资产所构成.首先,应用动态规划原理得到相应值函数所满足的HJB方程.然后,假设投资者对风险的偏好程度满足双曲绝对风险厌恶(HARA)效用函数,并应用Legendre变换法和分离变量法得到在HARA效用函数下最优投资策略的显示解.最后,给出数值算例分析部分市场参数对最优投资策略的影响.     

Heston随机波动率模型下的资产负债管理问题

应用随机最优控制方法研究Heston随机波动率模型下带有负债过程的动态投资组合问题,其中假设股票价格服从Heston随机波动率模型,负债过程由带漂移的布朗运动所驱动.金融市场由一种无风险资产和一种风险资产组成.应用随机动态规划原理和变量替换法得出了上述问题在幂效用和指数效用函数下最优投资策略的显示解,并给出数值算例分别分析了市场参数在幂效用和指数效用函数下对最优投资策略的影响.     

基于Hamilton-Jacobi-Bellman方程求解保险业最优投资策略

在经典复合泊松模型中,保险公司将资金投入一个风险投资过程和一个无风险投资过程.当索赔的分布确定后,运用随机控制中的HJB方程最小化保险公司的破产概率,在已知投资规模或投资组合的情况下求解二者中的另一项,进而得到最优投资策略并讨论各种策略的运用对破产概率的影响.解决保险公司的投资资金分配问题,在实际应用中具有一定的参考价值.     

部分信息下的投资决策的q理论

将生产率冲击期望回报的不可观测性引入托宾q投资理论的标准框架,运用贝叶斯原理通过管理者信念这一概念刻画管理者基于部分信息的学习过程.同时,考虑投资调整成本,建立公司托宾q值关于管理者信念的常微分方程,并讨论了管理者信念以及投资调整成本对公司托宾q值,投资决策,公司价值随管理者信念变化的敏感度以及公司当前资产价值与成长机会价值的影响.模型和结论对公司估值以及投资决策制定具有一定参考价值.     

A Procedure for Decision Making Based on Incomplete Fuzzy Preference Relation

In this paper, we investigate the decision making problem based on fuzzy preference relation with incomplete information. We first introduce incomplete fuzzy preference relation and present some of its desirable properties. We then develop a system of equations. Based on this system of equations, we propose a procedure for decision making based on incomplete fuzzy preference relation, and finally, a numerical example is presented to illustrate the proposed procedure.     

Mean-Variance Hedging Under Multiple Defaults Risk

We solve a mean-variance hedging problem in an incomplete market where multiple defaults can occur. For this purpose, we use a default-density modeling approach. The global market information is formulated as a progressive enlargement of a default-free Brownian filtration, and the dependence of the default times is modelled using a conditional density hypothesis. We prove the quadratic form of each value process between consecutive default times and recursively solve systems of coupled quadratic backward stochastic differential equations (BSDEs). We demonstrate the existence of these solutions using BSDE techniques. Then, using a verification theorem, we prove that the solutions of each subcontrol problem are related to the solution of our global mean-variance hedging problem. As a byproduct, we obtain an explicit formula for the optimal trading strategy. Finally, we illustrate our results for certain specific cases and for a multiple defaults case in particular.     

Portfolio selection under incomplete information

We study an optimal investment problem under incomplete information and power utility. We analytically solve the Bellman equation, and identify the optimal portfolio policy. Moreover, we compare the solution to the value function in the fully observable case, and quantify the loss of utility due to incomplete information.     

竞争环境下风险规避的航班动态定价

以同一航线上的多个竞争航班为研究对象,在假设各竞争航班之间具有完全信息的基础上,利用马尔可夫决策过程和博弈论,建立了竞争环境下风险规避的航班动态定价的数学模型,证明了均衡价格的存在性。在此基础上,进一步讨论了信息不完全情况下风险规避的竞争航班的动态定价问题。数值实验表明:在竞争环境下,各风险规避航班的均衡价格随自身剩余座位数量和风险规避系数的增加而下降,随其他竞争航班的剩余座位数量和风险规避系数的增加而提高。     

Reconstructing parameters of a medium from incomplete spectral information

The problem of the synthesis of a stratified medium with specified amplitude and phase properties is investigated. The wave propagation in the medium is described by a system of differential equations. The synthesis problem considered in the paper relates to inverse problems of spectral analysis with incomplete spectral information. Using the contour integral method we study properties of spectral characteristics and obtain algorithms for the solution of the synthesis problem for differential equations with singularities.     

Sparse Approximation of Data-Driven Polynomial Chaos Expansions: An Induced Sampling Approach

One of the open problems in the field of forward uncertainty quantification(UQ) is the ability to form accurate assessments of uncertainty having only incomplete information about the distribution of random inputs. Another challenge is to efficiently make use of limited training data for UQ predictions of complex engineering problems, particularly with high dimensional random parameters. We address these challenges by combining data-driven polynomial chaos expansions with a recently developed preconditioned sparse approximation approach for UQ problems. The first task in this two-step process is to employ the procedure developed in [1] to construct an "arbitrary" polynomial chaos expansion basis using a finite number of statistical moments of the random inputs. The second step is a novel procedure to effect sparse approximation via l1 minimization in order to quantify the forward uncertainty. To enhance the performance of the preconditioned l1 minimization problem, we sample from the so-called induced distribution, instead of using Monte Carlo (MC) sampling from the original, unknown probability measure. We demonstrate on test problems that induced sampling is a competitive and often better choice compared with sampling from asymptotically optimal measures(such as the equilibrium measure) when we have incomplete information about the distribution. We demonstrate the capacity of the proposed induced sampling algorithm via sparse representation with limited data on test functions, and on a Kirchoff plating bending problem with random Young's modulus.     

The rise of complex phenomena in Cournot duopoly games due to demand functions without inflection points

In this paper, we propose Cournot duopoly games where quantity-setting firms use non-linear demand functions that have no inflection points. Two different kinds of repeated games are introduced based on rationality process of firms and Puu’s incomplete approach. First, a model of two rational firms that are in competition and produce homogenous commodities is introduced. The equilibrium points of this model are obtained and their dynamical characteristics such as stability, bifurcation and chaos are investigated. By using rationality process firms do not need to solve any optimization problem but they adjust their production based on estimation of the marginal profit. Using Puu’s incomplete information approach a new model is introduced. As in the first model, the equilibrium points are obtained and their dynamical characteristics are investigated. By using Puu’s approach firms only need to know their profits and the quantities produced in the past two times. We compare the properties of the two models under the two approaches. The paper extends and generalizes the results of other authors that consider similar processes.     

基于改进的证据推理方法的多指标决策模型

对于一个多指标决策问题，证据理论可以通过构造辨识框架和基本概率分配函数、采取递归的证据合成方法。计算出原始数据在反映多个指标联合作用的情况下对不同判别结果的支持程度，并可以在信息复杂或数据不完整的条件下做出评估决策。本文首先建立基于证据推理的多指标评估问题的基本模型，然后引入了模糊数据方法以处理具有模糊概念或推理关系的复杂问题，同时还考虑了实际问题中可能出现加权证据或者相关证据的情况，其目的是为了建立一套具有实用性的、准确有效的多指标评估模型。文章最后设计一个风险评估的算例，分析了该方法的优点以及需要进一步完善之处。     

An adjustment scheme for nonlinear pricing problem with two buyers

We examine a contracting problem with asymmetric information in a monopoly pricing setting. Traditionally, the problem is modeled as a one-period Bayesian game, where the incomplete information about the buyers’ preferences is handled with some subjective probability distribution. Here we suggest an iterative online method to solve the problem. We show that, when the buyers behave myopically, the seller can learn the optimal tariff by selling the product repeatedly. In a practical modification of the method, the seller offers linear tariffs and adjusts them until optimality is reached. The adjustment can be seen as gradient adjustment, and it can be done with limited information and so that it benefits both the seller and the buyers. Our method uses special features of the problem and it is easily implementable.     

基于证据推理的不完全信息决策方法研究及应用

提出了一种基于证据推理和优化模型的不完全信息决策方法。针对专家认知偏好的多样性以及决策问题的复杂性特点,提出了一类评价指标不尽相同的不完全信息决策问题;运用证据理论中的基本信度分配来描述专家意见,给出了此类问题的信度函数、似真度函数、合成法则和不同专家贡献度的定义,计算了各个指标的基本信度分配值;从最大程度保持专家原始判断偏好的角度,建立了指标权重确定的优化模型;文后以商用飞机成本管控风险的重要性评价为例,说明了方法的应用步骤和有效性。