On 1-point densities for Arratia flows with drift

IF 1.1 2区 经济学 Q3 BUSINESS, FINANCE Finance and Stochastics Pub Date : 2023-05-12 DOI:10.1080/17442508.2023.2211189
A. Dorogovtsev, Mykola B. Vovchanskyi
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Abstract

We show that if drift coefficients of Arratia flows converge in L1(R) or L∞(R) then the 1-point densities associated with these flows converge to the density for the flow with the limit drift. The Arratia flow, a continual system of coalescing Wiener processes that are independent until they meet, was introduced independently as a limit of coalescing random walks in [1], as a system of reflecting Wiener processes in [2] and as a limit of stochastic homeomorphic flows in [3]. If interpreted as a collection of particles started at 0, it is a part of the Brownian web [4]. At the same time, one can construct the Arratia flow with drift using flows of kernels defined in [5] (see [6, §6] for a short explanation) or directly via martingale problems by adapting the method used in [7] to build coalescing stochastic flows with more general dependence between particles (see [8, Chapter 7] for this approach). Following [8, Chapter 7], we consider a modification of the Arratia flow that introduces drift affecting the motion of a particle within the flow. So by an Arratia flow X ≡ {Xa(u, t) | u ∈ R, t ∈ R+} with bounded measurable drift a we understand a collection of random variables such that (1) for every u the process X(u, ·) is an Itô process with diffusion coefficient 1 and drift a; (2) for all t ≥ 0 the mapping Xa(·, t) is monotonically increasing; (3) for any u1, u2 the joint quadratic covariation of the martingale parts of X (u1, ·) and X(u2, ·) equals (t − inf{s | X(u1, s) = X(u2, s)})+, with inf ∅ being equal ∞ by definition. Since the set {Xa(u, t) | u ∈ R} is known to be locally finite for all t > 0 [8, Chapter 7], one defines for any t > 0 the point process {|Xa(A, t)| | A ∈ B(R)}. Studying of such a point process can be performed using point densities (see: [9, 10] for a definition and a representation in terms of Pfaffians in the case of zero drift, respectively; [11, 12] for representations in the case of non-trivial drift; [13, 14] for applications to the study of the above-mentioned point process). In accordance with [9, Appendix B], the following definition is adopted in the present paper: the 1-point density at time t of the point 2020 Mathematics Subject Classification. Primary 60H10; Secondary 60K35, 60G55, 35C10.
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带漂移的Arratia流的1点密度
我们证明了如果Arratia流的漂移系数收敛于L1(R)或L∞(R),则与这些流相关的1点密度收敛于具有极限漂移的流的密度。Arratia流是一个连续的Wiener过程的合并系统,在它们相遇之前是独立的,它在[1]中作为合并随机游动的极限,在[2]中作为反映Wiener过程的系统,在[3]中作为随机同胚流的极限被独立引入。如果把它解释为从0开始的粒子集合,它就是布朗网[4]的一部分。与此同时,我们可以使用[5]中定义的核流来构建带有漂移的Arratia流(参见[6,§6]作简短解释),或者直接通过鞅问题,采用[7]中使用的方法来构建具有更普遍的粒子间依赖性的聚结随机流(参见[8,第7章])。在[8,第7章]之后,我们考虑对Arratia流的修改,引入影响流中粒子运动的漂移。因此,通过具有有界可测量漂移a的Arratia流X≡{Xa(u, t) | u∈R, t∈R+},我们可以理解一个随机变量的集合,使得(1)对于每一个u,过程X(u,·)是一个扩散系数为1且漂移a的Itô过程;(2)对于所有t≥0,映射Xa(·,t)单调递增;(3)对于任意u1, u2, X(u1,·)和X(u2,·)的鞅部分的联合二次协变等于(t - inf{s | X(u1, s) = X(u2, s)})+,根据定义,inf∅=∞。由于已知集合{Xa(u, t)| u∈R}对于所有的t > 0都是局部有限的[8,7章],因此对于任意t > 0定义点过程{|Xa(A, t)| | A∈B(R)}。对这种点过程的研究可以使用点密度(见[9,10],分别得到零漂移情况下用Pfaffians表示的定义和表示)来进行;[11,12]表示非平凡漂移的情况;[13,14]应用于上述点过程的研究)。根据[9,附录B],本文采用如下定义:2020数学学科分类点在时刻t的1点密度。主要60 h10;次级60K35、60G55、35C10。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Finance and Stochastics
Finance and Stochastics 管理科学-数学跨学科应用
CiteScore
2.90
自引率
5.90%
发文量
20
审稿时长
>12 weeks
期刊介绍: The purpose of Finance and Stochastics is to provide a high standard publication forum for research - in all areas of finance based on stochastic methods - on specific topics in mathematics (in particular probability theory, statistics and stochastic analysis) motivated by the analysis of problems in finance. Finance and Stochastics encompasses - but is not limited to - the following fields: - theory and analysis of financial markets - continuous time finance - derivatives research - insurance in relation to finance - portfolio selection - credit and market risks - term structure models - statistical and empirical financial studies based on advanced stochastic methods - numerical and stochastic solution techniques for problems in finance - intertemporal economics, uncertainty and information in relation to finance.
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