一类带干扰且Cox相关的双险种风险模型

在带有随机扰动的环境中,考虑保单到达及索赔到达均为Cox点过程且两类索赔到达过程相关的一类双险种风险模型.利用鞅技巧,将破产概率的指数上界推广到了更一般的情形.     

一类相依风险模型的破产问题

研究了一类相依的双险种风险模型,其中第一类险种的索赔到达计数过程为E lang(2)过程,第二类险种的索赔到达计数过程为其p-稀疏过程.首先通过更新论证的方法得到罚金折现期望满足的积分-微分方程,然后推导拉普拉斯变换的表达式,并就索赔额服从指数分布的情形得到了罚金折现期望的精确表达式.     

一类考虑破产限的双险种风险模型

本文研究一类考虑破产限的双险种风险模型,其中,一类险种保单到达是强度为λ的Poisson过程,退保、保单的非正常索赔以及正常索赔过程分别是关于保单到达过程的ρ_1—稀疏过程、ρ_2—稀疏过程、ρ_3—稀疏过程,另一类险种保单到达及索赔均服从复合负二项分布,运用鞅方法讨论该模型盈余过程的性质,并给出最终破产概率的表达式和Lundberg不等式.     

保费随机收取的广义二元复合非齐次Poisson风险模型

研究了一类双险种风险模型,其中索赔到达计数过程和保费到达计数过程均为非齐次Po isson过程,用鞅方法得到了有限时间破产概率的一个上界,并给出了当两个险种的个体索赔均服从指数分布时,有限时间破产概率的上界估计.     

一类相依双险种风险模型的罚金折现期望函数

考虑索赔到达具有相依性的一类双险种风险模型,其中第一类险种的索赔计数过程为Poisson过程,第二类险种的索赔计数过程为其p-稀疏过程与广义Erlang(2)过程的和,利用更新论证得到了此风险模型的罚金折现期望函数满足的微积分方程及其Laplace变换的表达式.并就索赔额均服从指数分布的情形,给出了罚金函数及破产概率的精确表达式.     

一类索赔到达计数过程相依的二元风险模型

研究了一类索赔到达计数过程为相依点过程的双险种风险模型.先将两个相依索赔总额转化为相互独立的索赔总额,并得出在PO ISSON情形下,可以转化为古典风险模型,从而可以利用现成的结果给出破产概率.     

一类保费批量到达的双险种风险模型

本文研究了一类保费与索赔均为批量到达的双险种破产模型,在特定的分布下导出了调节系数方程,得到了初始资本为u的破产概率的上界并与非批量到达的模型的破产上界进行了比较。     

一类离散双险种风险模型

本文推广了[1]的离散双险种风险模型,讨论了两类险种的索赔均为负二项随机序列的情形,得到了最终破产概率的Lundberg不等式以及一般表达式.     

一类延迟双险种风险模型的破产概率

本考虑了一类索赔发生分别是Poisson过程和Erlang(n)过程的延迟双险种模型，给出了初始女本为u的破产概率ψ(u)表达式．     

一类广义离散双险种风险模型

本推广了[1]中的离散双险种风险模型，讨论了保单到达过程为Poisson随机序列时的情况，得到了最终破产概率的Lundberg不等式以及一般表达式。     

半参数门限广义非对称随机波动模型研究

为了能够同时刻画和描述金融资产收益序列的偏态、厚尾以及序列的门限效应、非对称杠杆效应等特性,提出把门限广义非对称随机波动模型与非参数Dirichlet过程混合模型有机结合,构建了半参数门限广义非对称随机波动模型,并对模型进行了贝叶斯分析.实证研究中,利用上海黄金价格收益率序列数据进行建模分析,结果表明:半参数门限广义非对称随机波动模型能够有效地刻画上海黄金价格收益率序列波动率的动态特征.     

重复n人随机合作对策的核心

以Su ijs等人(1995)引入的随机合作对策的模型为基础,建立了重复n人随机合作对策的理论,定义了重复n人随机合作对策的支付序列以及支付序列的优超关系,并由此给出了重复n人随机合作对策的核心、超可加性和凸性的定义,并讨论了该核心的一些特征和性质.     

Fitting Social Network Models Using Varying Truncation Stochastic Approximation MCMC Algorithm

The exponential random graph model (ERGM) plays a major role in social network analysis. However, parameter estimation for the ERGM is a hard problem due to the intractability of its normalizing constant and the model degeneracy. The existing algorithms, such as Monte Carlo maximum likelihood estimation (MCMLE) and stochastic approximation, often fail for this problem in the presence of model degeneracy. In this article, we introduce the varying truncation stochastic approximation Markov chain Monte Carlo (SAMCMC) algorithm to tackle this problem. The varying truncation mechanism enables the algorithm to choose an appropriate starting point and an appropriate gain factor sequence, and thus to produce a reasonable parameter estimate for the ERGM even in the presence of model degeneracy. The numerical results indicate that the varying truncation SAMCMC algorithm can significantly outperform the MCMLE and stochastic approximation algorithms: for degenerate ERGMs, MCMLE and stochastic approximation often fail to produce any reasonable parameter estimates, while SAMCMC can do; for nondegenerate ERGMs, SAMCMC can work as well as or better than MCMLE and stochastic approximation. The data and source codes used for this article are available online as supplementary materials.     

关于任意随机序列的强收敛性

该文的目的是要研究任意随机序列的强极限定理。作为推论，得到了一类鞅差序列的强大数定律，一类随机序列公平比的强极限定理，以及任意随机序列部分和估计定理。      

关于任意随机适应序列部分和增长阶的估计

设(Ω,F,P)为概率空间,{Xn,Fn,n 0}为定义在上面的随机适应序列.目的是要研究任意随机适应序列的一个强极限定理.作为推论,推广了Freedman的一个定理以及任意随机适应序列部分和增长阶估计定理.     

Stochastic cyclic flow lines: non-blocking,Markovian models

In this paper we consider stochastic cyclic flow lines where identical sets of jobs are repeatedly produced in the same loading and processing sequence. Each machine has an input buffer with enough capacity. Processing times are stochastic. We model the shop as a stochastic event graph, a class of Petri nets. We characterise the ergodicity condition and the cycle time. For the case where processing times are exponentially distributed, we present a way of computing queue length distributions. For two-machine cases, by the matrix geometric method, we compute the exact queue length distributions. For general cases, we present two methods for approximately decomposing the line model into two-machine submodels, one based on starvation propagation and the other based on transition enabling probability propagation. We experiment our approximate methods for various stochastic cyclic flow lines and discuss performance characteristics as well as accuracy of the approximate methods. Finally, we discuss the effects of job processing sequences of stochastic cyclic flow lines.     

Integral approximation of stochastic differential equations with anticipating initial conditions

We give a sequence of stochastic integro-differential equations that approximates a stochastic differential equation with an anticipating initial condition and localized Skorokhod stochastic integral. A sequence of solutions of these equations is obtained. The convergence of this sequence to a certain process implies that this process is a solution (generally speaking, local) of the original equation.Translated from Ukrainskii Matematicheskii Zhurnal, Vol. 47, No. 7, pp. 936–945, July, 1995.     

American Options in the Heston Model with Stochastic Interest Rate and Its Generalizations

Abstract

We consider the Heston model with the stochastic interest rate of Cox–Ingersoll–Ross (CIR) type and more general models with stochastic volatility and interest rates depending on two CIR-factors; the price, volatility and interest rate may correlate. Time-derivative and infinitesimal generator of the process for factors that determine the dynamics of the interest rate and/or volatility are discretized. The result is a sequence of embedded perpetual options arising in the time discretization of a Markov-modulated Lévy model. Options in this sequence are solved using an iteration method based on the Wiener–Hopf factorization. Typical shapes of the early exercise boundary are shown, and good agreement of option prices with prices calculated with the Longstaff–Schwartz method and Medvedev–Scaillet asymptotic method is demonstrated.     

Modeling sequence scrambling and related phenomena in mixed-model production lines

In this paper we examine the various effects that workstations and rework loops with identical parallel processors and stochastic processing times have on the performance of a mixed-model production line. Of particular interest are issues related to sequence scrambling. In many production systems (especially those operating on just-in-time or in-line vehicle sequencing principles), the sequence of orders is selected carefully to optimize line efficiency while taking into account various line balancing and product spacing constraints. However, this sequence is often altered due to stochastic factors during production. This leads to significant economic consequences, due to either the degraded performance of the production line, or the added cost of restoring the sequence (via the use of systems such as mix banks or automated storage and retrieval systems). We develop analytical formulas to quantify both the extent of sequence scrambling caused by a station of the production line, and the effects of this scrambling on downstream performance. We also develop a detailed Markov chain model to analyze related issues regarding line stoppages and throughput. We demonstrate the usefulness of our methods on a range of illustrative numerical examples, and discuss the implications from a managerial point of view.     

Asymptotic behavior of solutions: An application to stochastic NLP

In this article we study the consistency of optimal and stationary (KKT) points of a stochastic non-linear optimization problem involving expectation functionals, when the underlying probability distribution associated with the random variable is weakly approximated by a sequence of random probability measures. The optimization model includes constraints with expectation functionals those are not captured in direct application of the previous results on optimality conditions exist in the literature. We first study the consistency of stationary points of a general NLP problem with convex and locally Lipschitz data and then apply those results to the stochastic NLP problem and stochastic minimax problem. Moreover, we derive an exponential bound for such approximations using a large deviation principle.