马尔科夫机制转换谱正Lévy风险模型中的最优分红策略

在马尔科夫机制转换谱正Levy风险模型中,研究最优分红问题.通过构造辅助的最优化问题,利用动态规划准则和Levy过程的漂移理论,证明了调节有界分红策略是最优策略,通过迭代方法得到了值函数和最优分红水平.     

具有停止损失再保险策略和最终值的扩散模型的最优分红与注资问题（英文）

本文研究了具有停止损失再保险和最终值的最优分红和融资策略问题.通过运用近似扩散和动态规划及构造次最优问题的方法,得到了解决一般最优问题所应符合的HJB方程和验证定理.假设有比例和固定交易费用以及在破产时刻产生最终值,得到了相应的最优值函数,最优分红策略,再保险策略以及融资策略.     

复合二项对偶模型中的周期性分红问题

该文主要在有界红利率的条件下讨论复合二项对偶模型的周期性分红问题.通过对值函数进行变换,得到了最优红利策略的一些性质,并且证明了最优值函数是一个HJB方程的唯一解.从而得到了最优策略和最优值函数的一个简单计算方法.根据最优红利策略的一些性质,该文还得到了最优值函数的可无限逼近的上界和下界.最后提供一些数值计算实例来说明该算法.     

复合Poisson模型带投资-借贷利率和固定交易费用的最优分红策略

本文研究了复合Poisson模型带投资-借贷利率和固定交易费用的最优分红问题。通过控制分红时刻和分红量，最大化直到绝对破产时刻的累积期望折现分红。由于考虑固定交易费用，问题为一个随机脉冲控制问题。首先，本文给出了一个策略是平稳马氏策略的充分必要条件。借助于测度值生成元理论得到测度值动态规划方程(简称测度值DPE),并且在没有任何附加条件下证明了验证定理。通过Lebesgue分解，本文讨论了测度值DPE和拟变分不等式(简称QVI)之间的关系，证明了最优分红策略为具有波段结构的平稳马氏策略。最后，本文给出了求解n-波段策略和相应值函数的算法。当索赔额服从指数分布时，得到了值函数的显示解和最优分红策略。     

离散时间模型下带分红交易费的最优分红

研究离散Sparre-Andersen模型下带分红交易费的最优分红问题.在分红有界的条件下,通过更新初始时间得到最优值函数并证明最优值函数为Hamilton-Jacobi-Bellman方程的唯一有界解.另外,运用Bellman递推算法通过最优值变换获得最优分红.     

带相关风险的保险公司的最优分红和再保险问题

本文的研究对象是带两种相关风险业务的保险公司.本文用复合Poisson过程描述这两种风险;应用扩散逼近理论,建立了一个扩散逼近模型.利用动态再保险策略,公司可以降低其破产概率,同时通过给客户分红,公司可以保持竞争力.公司的目标是寻找最优策略和值函数来最大化期望折现分红.因为超额损失再保险策略优于比例再保险策略,所以,本文考虑公司的超额损失再保险及其分红问题.问题分两种情形讨论:分红率有界和分红率无界.在这两种情形下,本文最终得到了值函数和相应最优策略的具体表达式.     

带利率和随机观测时间的布朗运动模型中最优分红策略

本文研究了带利率和随机观测时间的布朗运动模型中的最优分红问题.利用随机控制理论,获得了最优值函数相应的HJB方程,表明最优分红策略是障碍策略,并给出了最优值函数的显式表达式,推广了文献[19]的结果.     

经典风险模型分红控制过程的最优停止问题

本文在带注资的经典风险模型的最优分红控制过程的基础上,进一步引入最优停止策略.目标是要找到最优的停止时刻,使得到该时刻为止,股东的折现分红与带有一定费用的折现注资二者之差的期望值最大化.通过建立值函数V(x)满足的HJB方程,我们找到了最优停止时刻τ~*.特别的,当索赔服从指数分布时,通过计算最终得到了值函数V(x)和最优停止时刻.τ~*的清晰表达式.     

带利率和随机观测时间的布朗运动模型中最优分红策略（英文）

本文研究了带利率和随机观测时间的布朗运动模型中的最优分红问题.利用随机控制理论,获得了最优值函数相应的HJB方程,表明最优分红策略是障碍策略,并给出了最优值函数的显式表达式,推广了文献[19]的结果.     

带线性约束条件的跳扩散风险模型中的最优分红策略（英文）

对于一个金融或保险公司而言,寻求最优分红策略和最优分红值函数是一个受到广泛讨论的热点问题.在本文中,我们假设公司面临两类风险:Brownian风险和Poisson风险.公司可以控制其对股东的分红数额和分红时间.为了充分考虑公司经营的安全性,文中定义破产时间为公司盈余水平首次低于线性门槛b+κt的时刻,而非首次低于0的时刻,参见文献[1].本文解决了最大化公司从开始运营直至破产期间总分红折现值的期望的问题.通过求解一个含有二阶微分-积分算子的HJB方程,本文刻画出来了最优的分红值函数和最优的分红策略.结果表明,最优分红策略为线性门槛分红策略.即,当公司的盈余水平低于某线性门槛x_0+κt时,公司不分红;而当公司的盈余水平超过该线性门槛时,超过部分将全部作为红利分出.     

基于隐Markov模型的最优资产组合选择

在具有可观测和不可观测状态的金融市场中,利用隐马尔可夫链描述不可观测状态的动态过程,研究了不完全信息市场中的多阶段最优投资组合选择问题.通过构造充分统计量,不完全信息下的投资组合优化问题转化为完全信息下的投资组合优化问题,利用动态规划方法求得了最优投资组合策略和最优值函数的解析解.作为特例,还给出了市场状态完全可观测时的最优投资组合策略和最优值函数.     

考虑破产惩罚的对偶风险模型的最优控制

本文主要研究对偶风险模型的最优控制问题. 为了考虑破产对保险公司(金融机构)的影响, 我们在构造价值函数的过程中引入了一个变量来测度破产对公司盈利的影响. 为了求得最优的控制策略, 我们首先研究了两类带有约束的优化问题. 基于这些带约束优化问题的解, 我们给出了无约束的最优策略.     

Performance Bounds with Curvature for Batched Greedy Optimization

The batched greedy strategy is an approximation algorithm to maximize a set function subject to a matroid constraint. Starting with the empty set, the batched greedy strategy iteratively adds to the current solution set a batch of elements that results in the largest gain in the objective function while satisfying the matroid constraints. In this paper, we develop bounds on the performance of the batched greedy strategy relative to the optimal strategy in terms of a parameter called the total batched curvature. We show that when the objective function is a polymatroid set function, the batched greedy strategy satisfies a harmonic bound for a general matroid constraint and an exponential bound for a uniform matroid constraint, both in terms of the total batched curvature. We also study the behavior of the bounds as functions of the batch size. Specifically, we prove that the harmonic bound for a general matroid is nondecreasing in the batch size and the exponential bound for a uniform matroid is nondecreasing in the batch size under the condition that the batch size divides the rank of the uniform matroid. Finally, we illustrate our results by considering a task scheduling problem and an adaptive sensing problem.     

A continuous-time seat control model for single-leg flights with no-shows and optimal overbooking upper bound

This article proposes a continuous-time model to combine seat control and overbooking policies for single-leg flights. We assume that demand is time-and-fare dependent and follows a Poisson process. No-show passengers receive refunds which depend on their fares. Overbooking penalty is a uniformly convex function of oversale. To maximize the expected revenue, airline managers seek optimal seat allocation among competing passengers. In the meantime, they need to determine an optimal aggregate overbooking upper bound, which balances the no-show refunds and oversale penalties. Our basic model shows (i) although the nested-fare structure does not hold for the face value of fares, its essence is preserved in the sense of net fares; i.e., the face value less the no-show refund; (ii) the optimal control policy is based on a set of pre-calculated time thresholds, which is easy to implement; and (iii) there exists an optimal overbooking upper bound, below which the value function strictly increases in the upper bound, and above which the value function stays constant. We further extend the basic model to consider fare-dependent no-show rates. Numerical examples are presented.     

Using conical partition to globally maximizing the nonlinear sum of ratios

This article presents a global optimization algorithm for globally maximizing the sum of concave–convex ratios problem with a convex feasible region. The algorithm uses a branch and bound scheme where a concave envelope of the objective function is constructed to obtain an upper bound of the optimal value by using conical partition. As a result, the upper-bound subproblems during the algorithm search are all ordinary convex programs with less variables and constraints and do not grow in size from iterations to iterations in the computation procedure, and furthermore a new bounding tightening strategy is proposed such that the upper-bound convex relaxation subproblems are closer to the original nonconvex problem to enhance solution procedure. At last, some numerical examples are given to vindicate our conclusions.     

Optimal excess-of-loss reinsurance and investment problem for an insurer with jump–diffusion risk process under the Heston model

In this paper, we study the optimal excess-of-loss reinsurance and investment problem for an insurer with jump–diffusion risk model. The insurer is allowed to purchase reinsurance and invest in one risk-free asset and one risky asset whose price process satisfies the Heston model. The objective of the insurer is to maximize the expected exponential utility of terminal wealth. By applying stochastic optimal control approach, we obtain the optimal strategy and value function explicitly. In addition, a verification theorem is provided and the properties of the optimal strategy are discussed. Finally, we present a numerical example to illustrate the effects of model parameters on the optimal investment–reinsurance strategy and the optimal value function.     

Model-adaptive optimal discretization of stochastic integrals*

We study the optimal discretization error of stochastic integrals driven by a multidimensional continuous Brownian semimartingale. In the previous works a pathwise lower bound for the renormalized quadratic variation of the error was provided together with an asymptotically optimal discretization strategy, i.e. for which the lower bound is attained. However the construction of the optimal strategy involved the knowledge about the diffusion coefficient of the semimartingaleunder study. In this work we provide a model-adaptive asymptotically optimal discretization strategy that does not require any prior knowledge about the model. We prove the optimality for quite general class of discretization strategies based on kernel techniques for adaptive estimation and previously obtained optimal strategies that use random ellipsoid hitting times.     

Dividend-reinsurance strategy in the Sparre Andersen model

In this paper, we introduce a reinsurance strategy into the Sparre Andersen risk model with a horizon dividend barrier, which is named dividend-reinsurance strategy. It is shown that the value function of the new strategy far exceeds that of the optimal barrier strategy (even that of the optimal dividend strategy). Some results on the advantages of the new strategy are obtained, and the methods for computing the value functions are provided. Numerical illustrations for Erlang (2) and compound Poisson risk models are also given.     

Evaluation and Incremental Determination of Almost Optimal Allocations of Resources

The problem of allocating resources to activities with strictly concave return functions is considered; the objective function to be maximized is the sum of the returns from each activity. It is demonstrated that any set of feasible allocations can be used to obtain an explicit upper bound of the optimal value of this function. The upper bound is used to check that a numerically fast incremental procedure produces almost optimal allocations. A conservative solution of the allocation problem is generated by successively incrementing allocations with the greatest marginal returns; practical allocations are obtained from the conservative allocations by a method resulting in a reduction of the number of nonzero allocations and a simultaneous increase of the value of the objective function.