关于随机环境中分支过程物种总体的一个注记

本文研究随机环境中分支过程直流到n代的物种总体的渐近行为问题.利用概率生成函数的方法,得到了其期望和方差的具体表达式,并且在n趋于无穷大时,给出了灭绝概率和全部物种个数的生成函数之间的关系式.     

关于随机序的一个注记

本文引入随机序(≤_d)的概念,说明在讨论随机变量列的依概率(依分布)收敛问题时,它是一个颇为恰当的“控制尺度”.     

关于随机对偶性的一个注记

对任意随机局部凸模（S,{x^d}d∈D）,本文证明了{x^d}d∈D可表示成关于自然的随机对偶对〈S,S＊〉的—个随机可允许结构．     

关于布朗运动的一个注记

本文对布朗运动的一个充要条件给出另一种证明     

随机环境中分枝过程的等价定理

给定了随机环境中分枝过程(BPRE)的精确定义,讨论了有关的可测性问题和BPRE的基本性质．在此基础上,证明了BPRE的一个等价定理．     

随机系统均方稳定性的一个注记

本文研究随机定常系统dx=Fxdt GxdW的稳定性，给出了该方程均方稳定性的几个充分必要条件，最后给出了所得结果的两个应用。     

关于AQSI随机序列的一个注记

首先建立关于AQSI随机序列的Borel-Cantelli引理,在适当的条件下,获得了同分布AQSI随机序列的M-Z型强大数定理成立的充分必要条件.     

随机过程序列部分和的收敛性的注记

讨论了随机过程序列部分和的弱收敛性，弱化了肖庆宪等人（1999）给出的条件并将结果推广到了混合序列的情形。     

关于随机真度的若干注记

以随机真度为基础,在三值R_0命题逻辑系统中给出了三种不同的近似推理模式并讨论了它们之间的关系,其次利用根的性质得出误差定义的若干推理结果.     

关于一般马氏过程遍历性的一个注记

本文讨论了一般保守右过程的遍达性和不可约性,作为我们证明了一类Ｄｉｒｉｃｈｌｅｔ过程的不可约性及遍历性。     

The Largest Class of Closed Convex Valued Multifunctions for which Effros Measurability and Scalar Measurability Coincide

This paper deals with the comparison of Effros measurability and scalar measurability for multifunctions whose values lie in C(X), the set of closed convex subsets of a normed linear space X. An introductory counter-example shows that, on C(X), the Effros measurability is strictly stronger than the scalar measurability. Then, we introduce the notion of countably supported subspace of C(X). After some preparatory results and examples about this class of convex subsets, we show that on an analytic countably supported subspace of C(X), the Effros and the scalar -fields coincide. Conversely, we show that, if on a subspace of C(X), nonnecessarily analytic, the Effros and scalar -fields are identical, then is countably supported. This leads us to exhibit and study a wide class of subspaces of C(X) both countably supported and analytic. At last, we compare our results with the already existing ones and we briefly show how our main results can be extended to the case where X is a locally convex vector space.     

Stochastic Taylor Expansions for the Expectation of Functionals of Diffusion Processes

Abstract

Stochastic Taylor expansions of the expectation of functionals applied to diffusion processes which are solutions of stochastic differential equation systems are introduced. Taylor formulas w.r.t. increments of the time are presented for both, Itô and Stratonovich stochastic differential equation systems with multi-dimensional Wiener processes. Due to the very complex formulas arising for higher order expansions, an advantageous graphical representation by coloured trees is developed. The convergence of truncated formulas is analyzed and estimates for the truncation error are calculated. Finally, the stochastic Taylor formulas based on coloured trees turn out to be a generalization of the deterministic Taylor formulas using plain trees as recommended by Butcher for the solutions of ordinary differential equations.     

Lp空间中随机过程泛函的某些收敛性

随机变量之和的收敛性问题已有许多人在研究,并取得很多很好的结果.本文则进一步讨论了随机过程之和在Lp空间中依联合测度收敛的情况.     

Max-Plus Stochastic Processes

This paper is concerned with processes which are max-plus counterparts of Markov diffusion processes governed by Ito sense stochastic differential equations. Concepts of max-plus martingale and max-plus stochastic differential equation are introduced. The max-plus counterparts of backward and forward PDEs for Markov diffusions turn out to be first-order PDEs of Hamilton–Jacobi–Bellman type. Max-plus additive integrals and a max-plus additive dynamic programming principle are considered. This leads to variational inequalities of Hamilton–Jacobi–Bellman type.     

A Note on the Discontinuity Problem in Heston's Stochastic Volatility Model

Although quasi‐analytic formulas can be derived for European‐style financial claims in Heston's stochastic volatility model, the inverse Fourier integration involved makes the calculation somewhat complicated. This challenge has puzzled practitioners for many years because most implementations of Heston's formula are not robust, even for customarily‐used Heston parameters, as time to maturity is increased. In this article, a simplified approach is proposed to solve the numerical instability problem inherent to the fundamental solution of the Heston model. Specifically, the solution does not require any additional function or a particular mechanism for most software packages or programming library routines to correctly evaluate Heston's analytics.     

Study of Dependence for Some Stochastic Processes

Abstract

This article is concerned with studying the following problem: Consider a multivariate stochastic process whose law is characterized in terms of some infinitesimal characteristics, such as the infinitesimal generator in case of finite Markov chains. Under what conditions imposed on these infinitesimal characteristics of this multivariate process, the univariate components of the process agree in law with given univariate stochastic processes. Thus, in a sense, we study a stochastic processe' counterpart of the stochastic dependence problem, which in case of real valued random variables is solved in terms of Sklar's theorem.     

A Note on the Solution of a Stochastic Partial Differential Equation

Abstract

A physical model is described which justifies the appearance of a stochastic term in the two-dimensional Navier–Stokes equations. In this model, a linear oppositional control term accrues as well. The resulting stochastic partial differential equation is shown to have a unique stationary solution.     

A Note on Maximal Inequality for Stochastic Convolutions

Using unitary dilations we give a very simple proof of the maximal inequality for a stochastic convolution

A Note on Differentiability in a Markov Chain Market Using Stochastic Flows

Using stochastic flows of diffeomorphisms relating to a Markov chain together with the Itô's differentiation rule, the differentiability of the price of a European-style contingent claim with respect to the underlying state variables is proved in a continuous-time Markov chain market. The differentiability results are also used to calculate the Greeks for hedging.