Stability of neutral stochastic functional differential equations with Markovian switching driven by G-Brownian motion

Little seems to be known about stability results on the neutral stochastic function differential equations with Markovian switching driven by G-Brownian (G-NSFDEwMSs). This paper aims at investigating the pth moment exponential stability for G-NSFDEwMSs to fill this gap. Some sufficient conditions on the pth moment exponential stability of the trivial solution are derived by employing the Razumikhin-type method, stochastic analysis, and algebraic inequality technique. Moreover, an example is provided to illustrate the effectiveness of the obtained results.     

群体决策随机偏爱数映射的若干性质

1引言近年来,群体决策在社会选择、福利、经济和军事等领域得到广泛应用,从而其理论研究越来越被人们所重视.20世纪中叶,K.J,Arrow提出了偏爱公理系和不可能性定理,并     

Nonhomogeneous markov chains with desired properties - the nearest markovian model

A new class of operators performing an optimization (optimization operators or, simply, optimators) which generate transition matrices with required properties such as ergodicity, recurrence etc., is considered and their fundamental features are described. Some criteria for comparing such operators by taking into account their strenght are given and sufficient conditions for both weak and strong ergodicity are derived. The nearest Markovian model with respect to a given set of observed probability vectors is then defined as a sequence of transition matrices satisfying certain constraints that express our prior knowledge about the system. Finally, sufficient conditions for the existence of such a model are given and the related algorithm is illustrated by an example.     

A stochastic system with MMPP input and an access function

In classical studies of loss systems with restricted availability, the utilization was suggested of a probabilistic loss function, defining the conditional probability of an incoming call being rejected, as a function of the number of occupations in the destination group of servers. This paper gives an exact analysis of stochastic processes of practical relevance, associated with a system with MMPP (Markov Modulated Poisson Process) input, finite queueing capacity and a general loss function, assuming exponential service times. In addition to the process defining the state of the system at any instant, the analysis of the overflow point process (associated with the rejected arriving customers), the accepted point process (associated with the accepted arriving customers), and of the departure process will be presented. Together with the exact analysis of this system, based on the matrix analytical methodology of Neuts, (1981), we will derive expressions for calculating some key-parameters of pertinent associated processes, which may also be used for their approximate modelling. Also, examples of applications and of blocking probability calculations in specific models of this class will be presented.     

Aggregation/Disaggregation Iterative Methods Applied to Leontev Systems and Markov Chains

The paper surveys some recent results on iterative aggregation/disaggregation methods (IAD) for computing stationary probability vectors of stochastic matrices and solutions of Leontev linear systems. A particular attention is paid to fast IAD methods.     

Nonlinear Deformational Properties of Dispersely Strengthened Materials

A method and an algorithm for determining the effective deformational properties of dispersely strengthened materials with a physically nonlinear matrix and quasi-spheroidal linearly elastic inclusions are elaborated based on the stochastic differential equations of the physically nonlinear theory of elasticity. Their transformation to integral equations and the application of the method of conditional moments reduce the problem to a system of nonlinear algebraic equations, whose solution is constructed by the iteration method. The deformation diagrams as functions of the volume content of inclusions are investigated.     

广义随机Solow经济增长模型

本文在文[1]的基础上进一步拓广了随机Solow经济增长模型．利用白噪声分析理论建立的广义随机Solow经济增长模型，将随机Solow模型推广到包含广义白噪声泛函及具有非可料扩散系数的情形，并且借助U—泛函方法表明了Picard迭代法在此仍十分有效．     

Maximum Principle for a Stochastic Optimal Control Problem and Application to Portfolio/Consumption Choice

We consider mainly an optimal control problem motivated by a portfolio and consumption choice problem in a financial market where the utility of the investor is assumed to have a given homogeneous form. A Pontryagin local maximum principle is obtained by using classical variational methods. We apply the result to make optimal portfolio and consumption decisions for the problem under consideration. The optimal selection coincides with the one obtained in Refs. 1 and 2, where the Bellman dynamic programming principle was used.     

On confined McKean Langevin processes satisfying the mean no-permeability boundary condition

We construct a confined Langevin type process aimed to satisfy a mean no-permeability condition at the boundary. This Langevin process lies in the class of conditional McKean Lagrangian stochastic models studied by Bossy, Jabir and Talay (2010) [5]. The confined process considered here is a first construction of solutions to the class of Lagrangian stochastic equations with boundary condition issued by the so-called PDF methods for Computational Fluid Dynamics. We prove the well-posedness of the confined system when the state space of the Langevin process is a half-space.     

A mixed integer linear programming model for optimal sovereign debt issuance

Governments borrow funds to finance the excess of cash payments or interest payments over receipts, usually by issuing fixed income debt and index-linked debt. The goal of this work is to propose a stochastic optimization-based approach to determine the composition of the portfolio issued over a series of government auctions for the fixed income debt, to minimize the cost of servicing debt while controlling risk and maintaining market liquidity. We show that this debt issuance problem can be modeled as a mixed integer linear programming problem with a receding horizon. The stochastic model for the interest rates is calibrated using a Kalman filter and the future interest rates are represented using a recombining trinomial lattice for the purpose of scenario-based optimization. The use of a latent factor interest rate model and a recombining lattice provides us with a realistic, yet very tractable scenario generator and allows us to do a multi-stage stochastic optimization involving integer variables on an ordinary desktop in a matter of seconds. This, in turn, facilitates frequent re-calibration of the interest rate model and re-optimization of the issuance throughout the budgetary year allows us to respond to the changes in the interest rate environment. We successfully demonstrate the utility of our approach by out-of-sample back-testing on the UK debt issuance data.