关于无风险资产不存在时资产组合选择的研究

以往关于资产组合选择的研究大多假设市场上存在无风险资产,但无风险资产实际上是不存在的.当不存在无风险资产时,假设投资者的效用定义在消费上,消费一直是投资者财富的一个固定比例,投资者的最优资产组合由两部分组成:短视的资产组合和对冲组合.假设只有股票和债券两种风险资产,当股票和债券的风险具有负的相关性时,投资者现在会消费更多,同时也会在股票上投资更多;两者正相关时,投资者无法降低风险,会减持股票并降低当前消费;两者不相关时,投资者持有的股票权重和存在无风险资产时一样.最后,还推导出了多种资产情况下最优消费和资产组合的解析表达式.     

极大极小投资组合选择模型和均衡价格系统

讨论了市场上不存在无风险资产条件下投资组合选择的极大极小模型,推导出市场上不存在无风险资产时极大极小模型的最优投资策略和有效前沿,得到了资本市场均衡时存在唯一的非负均衡价格系统的充分必要条件和各资产均衡价格的解析表达式.     

不同均值-风险准则下的资产组合有效前沿比较研究

本文根据V aR和CV aR风险度量方法,对马克维茨的均值-方差资产组合选择模型进行拓展,研究在均值-风险准则下更具有一般性的资产组合选择问题.并在正态分布假设条件下,证明当不存在无风险资产时和存在无风险资产时,基于方差、V aR和CV aR风险度量准则的资产组合有有沿之间的关系,指出根据均值-V aR准则和均值-CV aR准则求解有效资产组合时,置信水平必须满足的条件     

“安全第一”下的不连续价格过程的投资组合问题

主要研究了"安全第一"原则下的连续时间随机过程的投资组合问题,所考察的模型是带有布朗运动和跳跃扩散的随机过程.推导出了相应的哈密顿雅克比贝尔曼方程.当不存在无风险资产时,得出了最优投资策略的闭环解.同样讨论了存在无风险资产投资时的最优投资策略.最后给出了一个实例加以说明此模型和方法的有效性和可行性.     

无风险资产有限借入的不相关证券组合有效集的解析表示

本文研究了不允许卖空条件下存在无风险资产有借入的不相关证券有效组合问题,给出了有效集及投资比例的解析表示。     

具有随机利率、随机变差的最优投资和联合比例-超额损失再保险

对跳-扩散风险模型,研究了最优投资和再保险问题.保险公司可以购买再保险减少理赔,保险公司还可以把盈余投资在一个无风险资产和一个风险资产上.假设再保险的方式为联合比例-超额损失再保险.还假设无风险资产和风险资产的利率是随机的,风险资产的方差也是随机的.通过解决相应的Hamilton-Jacobi-Bellman(HJB)方程,获得了最优值函数和最优投资、再保险策略的显示解.特别的,通过一个例子具体的解释了得到的结论.     

Vasicek利率模型下带有零息票债券的投资-消费模型

应用随机最优控制理论研究Vasicek利率模型下的投资-消费问题,其中假设无风险利率是服从Vasicek利率模型的随机过程,且与股票价格过程存在一般相关性.假设金融市场由一种无风险资产、一种风险资产和一种零息票债券所构成,投资者的目标是最大化中期消费与终端财富的期望贴现效用.应用变量替换方法得到了幂效用下最优投资-消费策略的显示表达式,并分析了最优投资-消费策略对市场参数的灵敏度.     

真实利率波动对长期资产配置的影响(英)

对于单期的投资者而言,无违约风险的固定收益证券被视为无风险资产.这是因为固定收益证券的收益率在投资的初期就能确定.然而在考虑长期的投资时,投资者可以调整资产配置,固定收益证券也将面临再投资的利率波动风险,因此不能再被视为无风险资产.本文在一类特殊的``习惯形成"效用函数的框架下讨论长期资产配置.在一系列为简化问题而作的假设之下,本文推导出了真实利率波动对风险资产配置权重的影响,并且为计算实际长期资产配置的最优比例提供了理论依据和算法.     

真实利率波动对长期资产配置的影响(英文)

对于单期的投资者而言,无违约风险的固定收益证券被视为无风险资产.这是因为固定收益证券的收益率在投资的初期就能确定.然而在考虑长期的投资时,投资者可以调整资产配置,固定收益证券也将面临再投资的利率波动风险,因此不能再被视为无风险资产.本文在一类特殊的"习惯形成"效用函数的框架下讨论长期资产配置.在一系列为简化问题而作的假设之下,本文推导出了真实利率波动对风险资产配置权重的影响,并且为计算实际长期资产配置的最优比例提供了理论依据和算法.     

新增证券对证券组合有效前沿影响的动态分析

不存在无风险资产时，新增k(k≥1)种与前n种资产相关的证券时，其对证券组合前沿的影响问题，研究比较复杂，本作提出了解决这一问题的动态算法，对这一问题进行了漂移分析，并给出了一系列定理和新投资比例计算公式，这些定理揭示了证券组合前沿的左相离特征。最后，本对结果作了计算机仿真。     

Robust decomposable Markov decision processes motivated by allocating school budgets

Motivated by an application to school funding, we introduce the notion of a robust decomposable Markov decision process (MDP). A robust decomposable MDP model applies to situations where several MDPs, with the transition probabilities in each only known through an uncertainty set, are coupled together by joint resource constraints. Robust decomposable MDPs are different than both decomposable MDPs, and robust MDPs and cannot be solved by a direct application of the solution methods from either of those areas. In fact, to the best of our knowledge, there is no known method to tractably compute optimal policies in robust, decomposable MDPs. We show how to tractably compute good policies for this model, and apply the derived method to a stylized school funding example.     

Robust estimation for partially linear models with large-dimensional covariates

We are concerned with robust estimation procedures to estimate the parameters in partially linear models with large-dimensional covariates. To enhance the interpretability, we suggest implementing a nonconcave regularization method in the robust estimation procedure to select important covariates from the linear component. We establish the consistency for both the linear and the nonlinear components when the covariate dimension diverges at the rate of o(n1/2), where n is the sample size. We show that the robust estimate of linear component performs asymptotically as well as its oracle counterpart which assumes the baseline function and the unimportant covariates were known a priori. With a consistent estimator of the linear component, we estimate the nonparametric component by a robust local linear regression. It is proved that the robust estimate of nonlinear component performs asymptotically as well as if the linear component were known in advance.Comprehensive simulation studies are carried out and an application is presented to examine the fnite-sample performance of the proposed procedures.     

Resonance bifurcation from homoclinic cycles

Differential equations that are equivariant under the action of a finite group can possess robust homoclinic cycles that can moreover be asymptotically stable. For differential equations in R⁴ there exists a classification of different robust homoclinic cycles for which moreover eigenvalue conditions for asymptotic stability are known. We study resonance bifurcations that destroy the asymptotic stability of robust ‘simple homoclinic cycles’ in four-dimensional differential equations. We establish that typically a periodic trajectory near the cycle is created, asymptotically stable in the supercritical case.     

Capacity planning and warehouse location in supply chains with uncertain demands

We discuss the strategic capacity planning and warehouse location problem in supply chains operating under uncertainty. In particular, we consider situations in which demand variability is the only source of uncertainty. We first propose a deterministic model for the problem when all relevant parameters are known with certainty, and discuss related tractability and computational issues. We then present a robust optimization model for the problem when the demand is uncertain, and demonstrate how robust solutions may be determined with an efficient decomposition algorithm using a special Lagrangian relaxation method in which the multipliers are constructed from dual variables of a linear program.     

Robust best approximation with interpolation constraints under ellipsoidal uncertainty: Strong duality and nonsmooth Newton methods

In this paper we present a duality approach for finding a robust best approximation from a set involving interpolation constraints and uncertain inequality constraints in a Hilbert space that is immunized against the data uncertainty using a nonsmooth Newton method. Following the framework of robust optimization, we assume that the input data of the inequality constraints are not known exactly while they belong to an ellipsoidal data uncertainty set. We first show that finding a robust best approximation is equivalent to solving a second-order cone complementarity problem by establishing a strong duality theorem under a strict feasibility condition. We then examine a nonsmooth version of Newton’s method and present their convergence analysis in terms of the metric regularity condition.     

Robust optimization of contaminant sensor placement for community water systems

We present a series of related robust optimization models for placing sensors in municipal water networks to detect contaminants that are maliciously or accidentally injected. We formulate sensor placement problems as mixed-integer programs, for which the objective coefficients are not known with certainty. We consider a restricted absolute robustness criteria that is motivated by natural restrictions on the uncertain data, and we define three robust optimization models that differ in how the coefficients in the objective vary. Under one set of assumptions there exists a sensor placement that is optimal for all admissible realizations of the coefficients. Under other assumptions, we can apply sorting to solve each worst-case realization efficiently, or we can apply duality to integrate the worst-case outcome and have one integer program. The most difficult case is where the objective parameters are bilinear, and we prove its complexity is NP-hard even under simplifying assumptions. We consider a relaxation that provides an approximation, giving an overall guarantee of near-optimality when used with branch-and-bound search. We present preliminary computational experiments that illustrate the computational complexity of solving these robust formulations on sensor placement applications.     

Relaxed robust second-order-cone programming

In this paper, we propose an approximate optimization model for the robust second-order-cone programming problem with a single-ellipsoid uncertainty set for which the computational complexity is not known yet. We prove that this approximate robust model can be equivalently reformulated as a finite convex optimization problem.     

On the recoverable robust traveling salesman problem

We consider an uncertain traveling salesman problem, where distances between nodes are not known exactly, but may stem from an uncertainty set of possible scenarios. This uncertainty set is given as intervals with an additional bound on the number of distances that may deviate from their expected, nominal values. A recoverable robust model is proposed, that allows a tour to change a bounded number of edges once a scenario becomes known. As the model contains an exponential number of constraints and variables, an iterative algorithm is proposed, in which tours and scenarios are computed alternately. While this approach is able to find a provably optimal solution to the robust model, it also needs to solve increasingly complex subproblems. Therefore, we also consider heuristic solution procedures based on local search moves using a heuristic estimate of the actual objective function. In computational experiments, these approaches are compared.     

Distributionally robust chance constraint with unimodality-skewness information and conic reformulation

Moment-based ambiguity sets are mostly used in distributionally robust chance constraints (DRCCs). Their conservatism can be reduced by imposing unimodality, but the known reformulations do not scale well. We propose a new ambiguity set tailored to unimodal and seemingly symmetric distributions by encoding unimodality-skewness information, which leads to conic reformulations of DRCCs that are more tractable than known ones based on semi-definite programs. Besides, the conic reformulation yields a closed-form expression of the inverse of unimodal Cantelli's bound.     

Robust control system design for polytopic stable LPV systems

We investigate robust control system design for polytopic stablelinear parameter-varying (LPV) plants using prior and non-real-timeknowledge of the parameter. A gain-scheduled framework and robustmodel matching (RMM) strategy are combined to develop controllers.First, a self-scheduled H-infinity method is applied to designa nominal controller using a known parameter. Then a robustcompensator is added in order to reduce the influence of parameterperturbation due to the real parameter's deviation from thenominal parameter. Thus, a RMM design method that is a practicalapproach to the design of attachable robust compensators forthe linear time-invariant plant, is extended in applicationto the LPV plant. Finally, robust stability of the overall systemfor possible parameter trajectories is confirmed. A design exampleand simulation results are presented in order to demonstratethe proposed method.