通胀市场下的风险补偿在线租赁策略设计

考虑到实际租赁市场中设备的租赁和购买价格随着时间推移持续上涨的特征,研究了通货膨胀市场中的租赁问题在有利率情形下的在线竞争策略,并建立了相应的概率预期的风险补偿模型。首先运用在线算法和竞争分析理论设计了该问题的最优竞争策略,并给出最优竞争比。接着,在风险补偿竞争分析框架下,进一步讨论该问题,投资者可以控制风险,根据自己不同的风险容忍度和未来预期选择补偿最大的租赁策略;也可以根据给定的补偿收益约束选择风险最小的策略。最后通过数值分析,验证了相关结论的正确性,也表明了基于概率预期的风险补偿策略大大改善了该租赁问题的竞争比性能。     

太湖水华危机应急预案在线风险补偿启动策略

应急预案启动时机的选择是应急管理领域中应对危机时需要解决的关键问题.太湖水华危机代表一类具有生长速度或传播速度不确定性的突发公共卫生事件,以此为背景基于在线方法,设计了应急预案启动策略,并证明是最优竞争比策略.同时,考虑到对未知信息的预期,设计了在线风险补偿启动策略.最后对危机进行实际数值计算,并指出策略的竞争性与预案应对强度之间的关系,还有在线风险补偿策略的补偿收益与决策者风险容忍度之间的关系,研究结果具有一定的实际指导意义和参考价值.     

基于随机微分博弈的最优投资

研究存在模型风险的最优投资决策问题,将该问题刻画为投资者与自然之间的二人-零和随机微分博弈,其中自然是博弈的"虚拟"参与者.利用随机微分博弈分析方法,通过求解最优控制问题对应的HJBI(Hamilton-Jacobi-Bellman-Isaacs)方程,在完备市场和存在随机收益流的非完备市场模型下,都得到了投资者最优投资策略以及最优值函数的解析表达式.结果表明,在完备市场条件下,投资者的最优风险投资额为零,在非完备市场条件下最优投资策略将卖空风险资产,且卖空额随着随机收益流波动率的增大而增加,随风险资产波动率增大而减少.     

基于相依风险模型框架均值方差准则下的最优时间一致的投资再保险策略问题（英文）

本文研究了在相依风险模型的框架下保险公司的最优投资和再保险问题.在均值方差准则下,利用博弈论的相关理论,求解扩展的HJB方程系统,得到最优时间一致的投资和再保险策略以及相应的最优值函数,并通过数值例子展现模型参数对最优策略的影响。     

马尔可夫机制转换模型下确定缴费型养老金计划的最优投资策略

研究了马尔可夫机制转换模型下确定缴费型养老金计划的最优投资问题.假定市场中风险资产价格与企业员工的工资都满足马尔可夫调制的几何布朗运动模型,它们的预期回报率和波动率都依赖于市场经济状态,其经济状态由一连续时间马尔可夫链来描述.利用最终财富的最大期望效用准则,得到了养老金管理者的最优投资策略,结果表明市场的经济状态对最优投资策略有着很大的影响.最后通过数值计算分析了市场利率和绝对风险厌恶系数与最优投资策略的关系.     

跳-扩散风险模型的最优投资和再保险策略

本文对跳-扩散风险模型,在赔付进行比例再保险,以及盈余投资于无风险资产和风险资产的条件下,研究使得最终财富的指数期望效用最大的最优投资和比例再保险策略.得到最优投资策略和最优再保险策略,以及最大指数期望效用函数的显式表达式,发现最优策略和值函数都受到无风险利率的影响.最后通过数值计算,得到最优投资和比例再保险策略,以及值函数与模型各个参数之间的关系.     

带最低保障和红利的养老基金随机控制问题研究

本文提出了带有最低保障固定供款养老基金最优分配的连续时间随机控制模型。在带状态约束且考虑股票支付红利的最优随机控制模型框架下,用预期幂效用最大化刻画基金管理者对无限区间上养老基金财富的效用,运用随机控制给出了作为HJB方程解的值函数的显式解及反馈形式的最优投资策略的显式解。     

基于银行下侧风险规避的第三方B2B平台激励策略研究

以电子仓单融资为例, 基于银行下侧风险规避角度, 研究联合授信和委托授信下当第三方B2B平台存在行为隐匿的道德风险时, 银行对B2B平台的激励策略设计问题。研究发现:B2B平台的最优努力水平随收益分配比例、回购比例的增大而减小, 随质押率、贷款利率、产品采购量、损失补偿比例的增大而增大;同时银行为规避违约风险, 需设置质押率、贷款利率和贷款额上限及回购比例下限, 并且银行最优收益分配比例与损失补偿比例、最优损失补偿比例与贷款损失率均成正相关关系。此外, 随着B2B平台工作效率的提高, 联合授信下最优收益分配比例将减小, 最优损失补偿比例将增大, 最终近似于委托授信下的最优损失补偿比例。最后给出数值分析。     

基于支持向量机的区间模糊数时间序列预测

研究了区间模糊数时间序列的预测方法.首先将区间模糊数序列转换为等量信息的精确数序列,然后对精确数序列建立支持向量机回归模型,通过还原公式,得到区间模糊数序列的拟合值和预测值.最后给出了数值实验,实验结果表明方法有效可行,且比ARMA回归模型以及灰色模型的预测精度更高.     

具有共同冲击相依性的跳扩散金融市场中有着延迟和违约风险的鲁棒最优再保险和投资策略

在本文中,我们考虑跳扩散模型下具有延迟和违约风险的鲁棒最优再保险和投资问题,保险人可以投资无风险资产,可违约的债券和两个风险资产,其中两个风险资产遵循跳跃扩散模型且受到同种因素带来共同影响而相互关联.假设允许保险人购买比例再保险,特别地再保险保费利用均值方差保费原则来计算.在考虑与绩效相关的资本流入/流出下,保险公司的财富过程通过随机微分延迟方程建模.保险公司的目标是最大程度地发挥终端财富和平均绩效财富组合的预期指数效用,以分别研究违约前和违约后的情况.此外,推导了最优策略的闭式表达式和相应的价值函数.最后通过数值算例和敏感性分析,表明了各种参数对最优策略的影响.另外对于模糊厌恶投资者,忽视模型模糊性风险会带来显著的效用损失.     

Time aggregation effect on the correlation coefficient: added-systematically sampled framework

The aggregation of financial and economic time series occurs in a number of ways. Temporal aggregation or systematic sampling is the commonly used approach. In this paper, we investigate the time interval effect of multiple regression models in which the variables are additive or systematically sampled. The correlation coefficient changes with the selected time interval when one is additive and the other is systematically sampled. It is shown that the squared correlation coefficient decreases monotonically as the differencing interval increases, approaching zero in the limit. When two random variables are both added or systematically sampled, the correlation coefficient is invariant with time and equal to the one-period values. We find that the partial regression and correlation coefficients between two additive or systematically sampled variables approach one-period values as n increases. When one of the variables is systematically sampled, they will approach zero in the limit. The time interval for the association analyses between variables is not selected arbitrarily or the statistical results are likely affected.     

g-上鞅的Riesz分解定理

本文给出了当终端时间趋于无穷时一类有限时间区间上的倒向随机微分方程的解的收敛性,并且证明了这类解平方收敛到特定的无穷时间区间上的倒向随机微分方程的解.本文主要研究了由倒向随机微分方程生成的非线性期望及其鞅的性质,证明了当生成元g是超线性时的g-上鞅Riesz分解定理.并且指出经典鞅论中的Riesz分解定理和下期望(又称最小期望)对应的上鞅Riesz分解定理是g-上鞅Riesz分解定理的两种特殊情况.     

Clusters of extremes: modeling and examples

We study clusters of threshold exceedances caused by dependence in time series. The clusters are defined as conglomerates containing consecutive threshold exceedances of the series separated by return intervals with consecutive non-exceedances. We derive asymptotic distributions of the cluster and inter-cluster sizes for processes with the extremal index equal to zero, the asymptotic expectation of the inter-cluster size and an exponential rate of convergence of the distribution tail of the return interval between clusters to the stable distribution tail. Distributions of the cluster and inter-cluster sizes of ARMAX, MM and AR(1) processes are obtained.     

Fuzzy random renewal process and renewal reward process

So far, there have been several concepts about fuzzy random variables and their expected values in literature. One of the concepts defined by Liu and Liu (2003a) is that the fuzzy random variable is a measurable function from a probability space to a collection of fuzzy variables and its expected value is described as a scalar number. Based on the concepts, this paper addresses two processes—fuzzy random renewal process and fuzzy random renewal reward process. In the fuzzy random renewal process, the interarrival times are characterized as fuzzy random variables and a fuzzy random elementary renewal theorem on the limit value of the expected renewal rate of the process is presented. In the fuzzy random renewal reward process, both the interarrival times and rewards are depicted as fuzzy random variables and a fuzzy random renewal reward theorem on the limit value of the long-run expected reward per unit time is provided. The results obtained in this paper coincide with those in stochastic case or in fuzzy case when the fuzzy random variables degenerate to random variables or to fuzzy variables.     

离散型区间概率随机变量和模糊概率随机变量的数学期望

研究离散型区间概率随机变量和离散型第二类模糊概率随机变量数学期望的性质及求解方法．利用模糊分解定理,把求模糊概率随机变量的数学期望问题化为求一系列区间概率随机变量的数学期望．求区间概率随机变量的数学期望是一个典型的线性规划问题,用单纯形方法推导了求区间概率随机变量数学期望的一个很实用的计算公式．算例表明,用该计算公式得到的结果和用数学规划方法得到的结果完全吻合,但计算过程相对简单．     

Robust reward–risk ratio optimization with application in allocation of generation asset

In this article, we study reward–risk ratio models under partially known message of random variables, which is called robust (worst-case) performance ratio problem. Based on the positive homogenous and concave/convex measures of reward and risk, respectively, the new robust ratio model is reduced equivalently to convex optimization problems with a min–max optimization framework. Under some specially partial distribution situation, the convex optimization problem is converted into simple framework involving the expectation reward measure and conditional value-at-risk measure. Compared with the existing reward–risk portfolio research, the proposed ratio model has two characteristics. First, the addressed problem combines with two different aspects. One is to consider an incomplete information case in real-life uncertainty. The other is to focus on the performance ratio optimization problem, which can realize the best balance between the reward and risk. Second, the complicated optimization model is transferred into a simple convex optimization problem by the optimal dual theorem. This indeed improves the usability of models. The generation asset allocation in power systems is presented to validate the new models.     

A two-person zero-sum Markov game with a stopped set

We consider a two-person zero-sum Markov game with continuous time up to the time that the game process goes into a fixed subset of a countable state space, this subset is called a stopped set of the game. We show that such a game with a discount factor has optimal value function and both players will have their optimal stationary strategies. The same result is proved for the case of a nondiscounted Markov game under some additional conditions, that is a reward rate function is nonnegative and the first time τ (entrance time) of the game process going to the stopped set is finite with probability one (i.e., p(τ < ∞) = 1). It is remarkable that in the case of a nondiscounted Markov game, if the expectation of the entrance time is bounded, and the reward rate function need not be nonnegative, then the same result holds.     

Vortex singularities of optimal strategies at the beginning of motion in search problems on <Emphasis Type="Italic">n</Emphasis>-dimensional Riemannian manifolds

This work studies optimal strategies having unexpected singularities. So, in particular, in the problem of searching for an object E on a segment that has the probability distribution function of location going to infinity to both side of a pursuer P, the modulus of whose speed does not exceed some constant, the optimal search strategy has no derivative at the initial instant of time. During an arbitrary small interval of time, the pursuer P changes the direction of his motion infinitely many times in order to be at both sides where the probability of location of E is maximal. In the case of a two-dimensional manifold, with analogous singularities of the probability distribution function of location of E, the velocity of P executes countably many turns during an arbitrary small interval of time for the optimal search (this is the reason for which such singularities are said to be vortex singularities). It is more difficult to imagine optimal strategies arising in the search on manifolds of dimension more than 2. Here, during an arbitrary small initial interval of time, the player P tends to completely inspect a neighborhood of the boundary of the visibility domain at the initial instant of time. Another unexpected phenomenon in the search problem on a segment is as follows: if the distribution function tends to zero at the endpoints of the segment, then the player P changes the direction of motion infinitely many times when approaching the endpoints of the segment. Precisely, when the probability of finding E near a given endpoint of the segment becomes sufficiently small, P runs to another endpoint of the segment, and there, not arriving at this end, he turns backward, and this occurs infinitely many times. In this case, in principle, the search can be performed arbitrarily many time, despite the fact that the inspection of the whole segment requires a fixed finite time. However, the expectation of the search time turns out to be minimal. This paper finds the formulas for switching points, and in the case of infinitely many switching points, the asymptotics of turn points is calculated. Translated from Sovremennaya Matematika i Ee Prilozheniya (Contemporary Mathematics and Its Applications), Vol. 58, Optimal Control, 2008.     

On differentiability of ruin functions under Markov-modulated models

This paper analyzes the continuity and differentiability of several classes of ruin functions under Markov-modulated insurance risk models with a barrier and threshold dividend strategy, respectively. Many ruin related functions in the literature, such as the expectation and the Laplace transform of the Gerber–Shiu discounted penalty function at ruin, of the total discounted dividends until ruin, and of the time-integrated discounted penalty and/or reward function of the risk process, etc, are special cases of the functions considered in this paper. Continuity and differentiability of these functions in the corresponding dual models are also studied.     

A class of estimators of the mean survival time from interval censored data with application to linear regression

A class of estimators of the mean survival time with interval censored data are studied by unbiased transformation method. The estimators are constructed based on the observations to ensure unbiasedness in the sense that the estimators in a certain class have the same expectation as the mean survival time. The estimators have good properties such as strong consistency （with the rate of O（n^-1/1 （log log n）^1/2）） and asymptotic normality. The application to linear regression is considered and the simulation reports are given.