Analysis for some properties of discrete time Markov decision processes

This paper investigates properties of the optimality equation and optimal policies in discrete time Markov decision processes with expected discounted total rewards under weak conditions that the model is well defined and the optimality equation is true. The optimal value function is characterized as a solution of the optimality equation and the structure of optimal policies is also given.     

Analysis of the multiple roots of the Lundberg fundamental equation in the PH (n) risk model

In the study of the Sparre Andersen risk model with phase‐type (n) inter‐claim times (PH (n) risk model), the distinct roots of the Lundberg fundamental equation in the right half of the complex plane and the linear independence of the eigenvectors related to the Lundberg matrix L_δ(s) play important roles. In this paper, we study the case where the Lundberg fundamental equation has multiple roots or the corresponding eigenvectors are linearly dependent in the PH (n) risk model. We show that the multiple roots of the Lundberg fundamental equation det[L_δ(s)] = 0 can be approximated by the distinct roots of the generalized Lundberg equation introduced in this paper and that the linearly dependent eigenvectors can be approximated by the corresponding linearly independent ones as well. Using this result we derive the expressions for the Gerber–Shiu penalty function. Two special cases of the generalized Erlang(n) risk model and a Coxian(3) risk model are discussed in detail, which illustrate the applicability of main results. Finally, we consider the PH(2) risk model and conclude that the roots of the Lundberg fundamental equation in the right half of the complex plane are distinct and that the corresponding eigenvectors are linearly independent. Copyright © 2011 John Wiley & Sons, Ltd.     

Data envelopment analysis models of investment funds

This paper develops theory missing in the sizable literature that uses data envelopment analysis to construct return-risk ratios for investment funds. It explores the production possibility set of the investment funds to identify an appropriate form of returns to scale. It discusses what risk and return measures can justifiably be combined and how to deal with negative risks, and identifies suitable sets of measures. It identifies the problems of failing to deal with diversification and develops an iterative approximation procedure to deal with it. It identifies relationships between diversification, coherent measures of risk and stochastic dominance. It shows how the iterative procedure makes a practical difference using monthly returns of 30 hedge funds over the same time period. It discusses possible shortcomings of the procedure and offers directions for future research.     

Risk averse decision making under catastrophic risk

A nonstandard probabilistic setting for modeling of the risk of catastrophic events is presented. It allows random variables to take on infinitely large negative values with non-zero probability, which correspond to catastrophic consequences unmeasurable in monetary terms, e.g. loss of human lives. Thanks to this extension, the safety-first principle is proved to be consistent with traditional axioms on a preference relation, such as monotonicity, continuity, and risk aversion. Also, a robust preference relation is introduced, and an example of a monotone robust preference relation, sensitive to catastrophic events in the sense of Chichilnisky (2002), is provided. The suggested setting is demonstrated in evaluating nuclear power plant projects when the probability of a catastrophe is itself a random variable.     

基于马尔可夫决策过程的医疗检查预约优化模型

医技部门的医疗检查如电子计算机断层扫描（CT）、核磁共振成像设备（MRI）、X射线（X－rays）常常有如下三种病人类型：门诊病人、住院病人和急诊病人。针对不同病人类型的需求特点，运用马尔可夫决策过程原理和动态规划方法，建立了医疗检查设备的预约优化模型，并证明了模型的最优性质，得出了不同病人类型的最优预约策略。数值算例的结果表明：本文的预约策略不仅易于实施，而且，通过该模型获得的最大收益比按传统先来先预约的模式所获得的收益要大。     

Optimal intervention under stress scenarios: A case of the Korean financial system

We consider optimal intervention methods under budget constraints when financial systems face economic shocks. We propose two policies formulated by mixed-integer linear programs where regulators inject cash into institutions. One is to minimize systemic losses, and the other is to minimize the number of defaulting institutions. Using publicly available data on the Korean financial system, we construct its entire network and apply stress scenarios to the system to compare the performances of intervention strategies and derive insights on their workings.     

Collective risk models with dependence

In actuarial science, collective risk models, in which the aggregate claim amount of a portfolio is defined in terms of random sums, play a crucial role. In these models, it is common to assume that the number of claims and their amounts are independent, even if this might not always be the case. We consider collective risk models with different dependence structures. Due to the importance of such risk models in an actuarial setting, we first investigate a collective risk model with dependence involving the family of multivariate mixed Erlang distributions. Other models based on mixtures involving bivariate and multivariate copulas in a more general setting are then presented. These different structures allow to link the number of claims to each claim amount, and to quantify the aggregate claim loss. Then, we use Archimedean and hierarchical Archimedean copulas in collective risk models, to model the dependence between the claim number random variable and the claim amount random variables involved in the random sum. Such dependence structures allow us to derive a computational methodology for the assessment of the aggregate claim amount. While being very flexible, this methodology is easy to implement, and can easily fit more complicated hierarchical structures.     

Bayesian Estimation and Statistical Analysis of Risk Measurements

The Bayesian model are established for the VaR and related risk measurements. The relationship between VaR and other risk measurements including expect shortfall, tail condition expectation and conditional value at risk are discussed. Furthermore, the Bayesian estimates and Bayesian predictors of these risk measurement are derived. Thirdly, the consistency and asymptotic normality in the exponential risk model are proved. Finally, the numerical simulation method is used to verify the convergence rate under different sample sizes.     

时间敏感选项依赖下的基础设施恢复决策研究

突发事件发生后,基础设施之间的恢复依赖为恢复过程带来了严重挑战。为了能够在突发事件后高效而有序的实现基础设施恢复运行,根据相互之间的恢复依赖关系制定合理的恢复决策非常关键。本文基于网络流理论,以累积恢复效能最大化为目标,建立了时间敏感选项依赖下的恢复设计与调度决策混合整数规划模型。然后,讨论了模型在完全中心化、完全分散和信息共享决策环境下的应用方法。最后,通过真实基础设施数据集测试了模型,结果表明:(1)该模型在突发事件后的基础设施恢复决策中具有应用可行性;(2)决策环境显著影响存在恢复依赖的基础设施网络整体累积恢复效能;(3)与完全分散决策环境相比,在信息共享决策环境下独立决策的整体累积恢复效能可以得到大幅提升。