Absolute Continuity Results for Superprocesses with Some Applications
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On Two Dimensional Markov Processes with Branching Property^)
ON TWO DIMENSIONAL MARKOV PROCESSES WITH BRANCHING PROPERTY^) BY SHINZO WATANABE Introduction. A continuous state branching Markov process (C.B.P.) was introduced by Jirina [8] and recently Lamperti [10] determined all such processes on the half line. (A quite similar result was obtained independently by the author.) This class of Markov processes contains as a special case the diffusion processes (which we shall call Feller's diffusions) studied by Feller [2]. The main objective of the present paper is to extend Lamperti's result to multi-dimensional case. For simplicity we shall consider the case of 2-dimensions though many arguments can be carried over to the case of higher dimensions(2). In Theorem 2 below we shall characterize all C.B.P.'s in the first quadrant of a plane and construct them. Our construction is in an analytic way, by a similar construction given in Ikeda, Nagasawa and Watanabe [5], through backward equations (or in the terminology of [5] through S-equations) for a simpler case and then in the general case by a limiting procedure. A special attention will be paid to the case of diffusions. We shall show that these diffusions can be obtained as a unique solution of a stochastic equation of Ito (Theorem 3). This fact may be of some interest since the solutions of a stochastic equation with coefficients Holder continuous of exponent 1/2 (which is our case) are not known to be unique in general. Next we shall examine the behavior of sample functions near the boundaries (xj-axis or x2-axis). -
Long Term Risk: a Martingale Approach
Long Term Risk: A Martingale Approach Likuan Qin∗ and Vadim Linetskyy Department of Industrial Engineering and Management Sciences McCormick School of Engineering and Applied Sciences Northwestern University Abstract This paper extends the long-term factorization of the pricing kernel due to Alvarez and Jermann (2005) in discrete time ergodic environments and Hansen and Scheinkman (2009) in continuous ergodic Markovian environments to general semimartingale environments, with- out assuming the Markov property. An explicit and easy to verify sufficient condition is given that guarantees convergence in Emery's semimartingale topology of the trading strate- gies that invest in T -maturity zero-coupon bonds to the long bond and convergence in total variation of T -maturity forward measures to the long forward measure. As applications, we explicitly construct long-term factorizations in generally non-Markovian Heath-Jarrow- Morton (1992) models evolving the forward curve in a suitable Hilbert space and in the non-Markovian model of social discount of rates of Brody and Hughston (2013). As a fur- ther application, we extend Hansen and Jagannathan (1991), Alvarez and Jermann (2005) and Bakshi and Chabi-Yo (2012) bounds to general semimartingale environments. When Markovian and ergodicity assumptions are added to our framework, we recover the long- term factorization of Hansen and Scheinkman (2009) and explicitly identify their distorted probability measure with the long forward measure. Finally, we give an economic interpre- tation of the recovery theorem of Ross (2013) in non-Markovian economies as a structural restriction on the pricing kernel leading to the growth optimality of the long bond and iden- tification of the physical measure with the long forward measure. -
A Short History of Stochastic Integration and Mathematical Finance
A Festschrift for Herman Rubin Institute of Mathematical Statistics Lecture Notes – Monograph Series Vol. 45 (2004) 75–91 c Institute of Mathematical Statistics, 2004 A short history of stochastic integration and mathematical finance: The early years, 1880–1970 Robert Jarrow1 and Philip Protter∗1 Cornell University Abstract: We present a history of the development of the theory of Stochastic Integration, starting from its roots with Brownian motion, up to the introduc- tion of semimartingales and the independence of the theory from an underlying Markov process framework. We show how the development has influenced and in turn been influenced by the development of Mathematical Finance Theory. The calendar period is from 1880 to 1970. The history of stochastic integration and the modelling of risky asset prices both begin with Brownian motion, so let us begin there too. The earliest attempts to model Brownian motion mathematically can be traced to three sources, each of which knew nothing about the others: the first was that of T. N. Thiele of Copen- hagen, who effectively created a model of Brownian motion while studying time series in 1880 [81].2; the second was that of L. Bachelier of Paris, who created a model of Brownian motion while deriving the dynamic behavior of the Paris stock market, in 1900 (see, [1, 2, 11]); and the third was that of A. Einstein, who proposed a model of the motion of small particles suspended in a liquid, in an attempt to convince other physicists of the molecular nature of matter, in 1905 [21](See [64] for a discussion of Einstein’s model and his motivations.) Of these three models, those of Thiele and Bachelier had little impact for a long time, while that of Einstein was immediately influential. -
Interacting Particle Systems MA4H3
Interacting particle systems MA4H3 Stefan Grosskinsky Warwick, 2009 These notes and other information about the course are available on http://www.warwick.ac.uk/˜masgav/teaching/ma4h3.html Contents Introduction 2 1 Basic theory3 1.1 Continuous time Markov chains and graphical representations..........3 1.2 Two basic IPS....................................6 1.3 Semigroups and generators.............................9 1.4 Stationary measures and reversibility........................ 13 2 The asymmetric simple exclusion process 18 2.1 Stationary measures and conserved quantities................... 18 2.2 Currents and conservation laws........................... 23 2.3 Hydrodynamics and the dynamic phase transition................. 26 2.4 Open boundaries and matrix product ansatz.................... 30 3 Zero-range processes 34 3.1 From ASEP to ZRPs................................ 34 3.2 Stationary measures................................. 36 3.3 Equivalence of ensembles and relative entropy................... 39 3.4 Phase separation and condensation......................... 43 4 The contact process 46 4.1 Mean field rate equations.............................. 46 4.2 Stochastic monotonicity and coupling....................... 48 4.3 Invariant measures and critical values....................... 51 d 4.4 Results for Λ = Z ................................. 54 1 Introduction Interacting particle systems (IPS) are models for complex phenomena involving a large number of interrelated components. Examples exist within all areas of natural and social sciences, such as traffic flow on highways, pedestrians or constituents of a cell, opinion dynamics, spread of epi- demics or fires, reaction diffusion systems, crystal surface growth, chemotaxis, financial markets... Mathematically the components are modeled as particles confined to a lattice or some discrete geometry. Their motion and interaction is governed by local rules. Often microscopic influences are not accesible in full detail and are modeled as effective noise with some postulated distribution. -
Interacting Stochastic Processes
Interacting stochastic processes Stefan Grosskinsky Warwick, 2009 These notes and other information about the course are available on go.warwick.ac.uk/SGrosskinsky/teaching/ma4h3.html Contents Introduction 2 1 Basic theory5 1.1 Markov processes..................................5 1.2 Continuous time Markov chains and graphical representations..........6 1.3 Three basic IPS................................... 10 1.4 Semigroups and generators............................. 13 1.5 Stationary measures and reversibility........................ 18 1.6 Simplified theory for Markov chains........................ 21 2 The asymmetric simple exclusion process 24 2.1 Stationary measures and conserved quantities................... 24 2.2 Symmetries and conservation laws......................... 27 2.3 Currents and conservation laws........................... 31 2.4 Hydrodynamics and the dynamic phase transition................. 35 2.5 *Open boundaries and matrix product ansatz.................... 39 3 Zero-range processes 44 3.1 From ASEP to ZRPs................................ 44 3.2 Stationary measures................................. 46 3.3 Equivalence of ensembles and relative entropy................... 50 3.4 Phase separation and condensation......................... 54 4 The contact process 57 4.1 Mean-field rate equations.............................. 57 4.2 Stochastic monotonicity and coupling....................... 59 4.3 Invariant measures and critical values....................... 62 d 4.4 Results for Λ = Z ................................. 65 -
Llogl Condition for Supercritical Branching Hunt Processes
J Theor Probab (2011) 24: 170–193 DOI 10.1007/s10959-010-0322-7 L log L Condition for Supercritical Branching Hunt Processes Rong-Li Liu · Yan-Xia Ren · Renming Song Received: 17 March 2009 / Revised: 17 July 2010 / Published online: 19 October 2010 © Springer Science+Business Media, LLC 2010 Abstract In this paper we use the spine decomposition and martingale change of measure to establish a Kesten–Stigum L log L theorem for branching Hunt processes. This result is a generalization of the results in Asmussen and Hering (Z. Wahrschein- lichkeitstheor. Verw. Geb. 36:195–212, 1976) and Hering (Branching Processes, pp. 177–217, 1978) for branching diffusions. Keywords Hunt processes · Branching Hunt processes · Kesten–Stigum theorem · Martingales · Martingale change of measure Mathematics Subject Classification (2000) Primary 60J80 · 60F15 · Secondary 60J25 1 Introduction Suppose that {Zn; n ≥ 1} is a Galton–Watson process with each particle having prob- ability pn of giving birth to n offspring. Let L stand for a random variable with this := ∞ offspring distribution. Let m n=1 npn be the mean number of offspring per par- n n ticle. Then Zn/m is a non-negative martingale. Let W be the limit of Zn/m as The research of Y.-X. Ren was supported by NSFC (Grant No. 10871103 and 10971003). R.-L. Liu · Y.-X. Ren LMAM School of Mathematical Sciences, Peking University, Beijing, 100871, P.R. China R.-L. Liu e-mail: [email protected] Y.-X. Ren e-mail: [email protected] R. Song () Department of Mathematics, The University of Illinois, Urbana, IL 61801, USA e-mail: [email protected] J Theor Probab (2011) 24: 170–193 171 n →∞. -
Strong Law of Large Numbers for Supercritical Superprocesses Under Second Moment Condition
Front. Math. China 2015, 10(4): 807–838 DOI 10.1007/s11464-015-0482-y Strong law of large numbers for supercritical superprocesses under second moment condition Zhen-Qing CHEN1, Yan-Xia REN2, Renming SONG3, Rui ZHANG4 1 Department of Mathematics, University of Washington, Seattle, WA 98195, USA 2 LMAM School of Mathematical Sciences & Center for Statistical Science, Peking University, Beijing 100871, China 3 Department of Mathematics, University of Illinois, Urbana, IL 61801, USA 4 LMAM School of Mathematical Sciences, Peking University, Beijing 100871, China c Higher Education Press and Springer-Verlag Berlin Heidelberg 2015 Abstract Consider a supercritical superprocess X = {Xt,t 0} on a locally compact separable metric space (E,m). Suppose that the spatial motion of X is a Hunt process satisfying certain conditions and that the branching mechanism is of the form ψ(x, λ)=−a(x)λ + b(x)λ2 + (e−λy − 1+λy)n(x, dy),x∈ E, λ > 0, (0,+∞) ∈ B ∈ B+ ∞ where a b(E),b b (E), and n is a kernel from E to (0, + ) +∞ 2 ∞ P satisfying supx∈E 0 y n(x, dy) < + . Put Ttf(x)= δx f,Xt . Suppose that the semigroup {Tt; t 0} is compact. Let λ0 be the eigenvalue of the (possibly non-symmetric) generator L of {Tt} that has the largest real part among all the eigenvalues of L, which is known to be real-valued. Let φ0 and φ0 be the eigenfunctions of L and L (the dual of L) associated with λ0, respectively. Assume λ0 > 0. Under some conditions on the spatial motion and the φ0-transform of the semigroup {Tt}, we prove that for a large class of suitable functions f, −λ0t lim e f,Xt = W∞ φ0(y)f(y)m(dy), Pμ-a.s., t→+∞ E for any finite initial measure μ on E with compact support, where W∞ is −λ t the martingale limit defined by W∞ := limt→+∞ e 0 φ0,Xt. -
Classical Limit Theorems for Measure-Valued Markov Processes
CORE Metadata, citation and similar papers at core.ac.uk Provided by Elsevier - Publisher Connector JOURNAL OF MULTIVARIA’IX ANALYSIS 9,234-247 (1979) Classical Limit Theorems for Measure-Valued Markov Processes ALANF.KhRR The Johns Hopkins University Communicated by A. Dwretzky Laws of large numbers, central liiit theorems, and laws of the iterated logarithm are obtained for discrete and continuous time Markov processes whose state space is a set of measures. These results apply to each measure- valued stochastic process itself and not simply to its real-valued functionals. 1. INTRODUCTION In this note we derive classicallimit theorems for Markov chains and Markov processestaking values in a set of measures.The results obtained are strong laws of large numbers, central limit theorems, and laws of the iterated logarithm for the processesthemselves, rather than only for functionals thereof. Of course, the latter classof results follows from those presented here. Our notation and terminology are those of [3]. We denote by E a LCCB space, by 8 the Bore1 u-algebra on E, by C, the Banach space of continuous real-valued functions on E vanishing at infinity and by C, the family of those functions in C, with compact support. Denote by K+ the family of finite measureson B and let K = K+ - K+ . Then, cf. [3, p. 91, K = C$; .f shall denote the Bore1 u-algebra on K generated by the weak* topology induced on K by C’s. The notation m(f) = Jf d m is used frequently below. Stochastic processeswith state spaceK+ we think of as measure-valued. -
Some Investigations on Feller Processes Generated by Pseudo- Differential Operators
_________________________________________________________________________Swansea University E-Theses Some investigations on Feller processes generated by pseudo- differential operators. Bottcher, Bjorn How to cite: _________________________________________________________________________ Bottcher, Bjorn (2004) Some investigations on Feller processes generated by pseudo-differential operators.. thesis, Swansea University. http://cronfa.swan.ac.uk/Record/cronfa42541 Use policy: _________________________________________________________________________ This item is brought to you by Swansea University. Any person downloading material is agreeing to abide by the terms of the repository licence: copies of full text items may be used or reproduced in any format or medium, without prior permission for personal research or study, educational or non-commercial purposes only. The copyright for any work remains with the original author unless otherwise specified. The full-text must not be sold in any format or medium without the formal permission of the copyright holder. Permission for multiple reproductions should be obtained from the original author. Authors are personally responsible for adhering to copyright and publisher restrictions when uploading content to the repository. Please link to the metadata record in the Swansea University repository, Cronfa (link given in the citation reference above.) http://www.swansea.ac.uk/library/researchsupport/ris-support/ Some investigations on Feller processes generated by pseudo-differential operators Bjorn Bottcher Submitted to the University of Wales in fulfilment of the requirements for the Degree of Doctor of Philosophy University of Wales Swansea May 2004 ProQuest Number: 10805290 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. -
Solutions of L\'Evy-Driven Sdes with Unbounded Coefficients As Feller
Solutions of L´evy-driven SDEs with unbounded coefficients as Feller processes Franziska K¨uhn∗ Abstract Rd Rd×k Let (Lt)t≥0 be a k-dimensional L´evyprocess and σ ∶ → a continuous function such that the L´evy-driven stochastic differential equation (SDE) dXt = σ(Xt−) dLt;X0 ∼ µ has a unique weak solution. We show that the solution is a Feller process whose domain of the generator contains the smooth functions with compact support if, and only if, the L´evy measure ν of the driving L´evyprocess (Lt)t≥0 satisfies k SxS→∞ ν({y ∈ R ; Sσ(x)y + xS < r}) ÐÐÐ→ 0: This generalizes a result by Schilling & Schnurr [14] which states that the solution to the SDE has this property if σ is bounded. Keywords: Feller process, stochastic differential equation, unbounded coefficients. MSC 2010: Primary: 60J35. Secondary: 60H10, 60G51, 60J25, 60J75, 60G44. 1 Introduction Feller processes are a natural generalization of L´evyprocesses. They behave locally like L´evy processes, but { in contrast to L´evyprocesses { Feller processes are, in general, not homo- geneous in space. Although there are several techniques to prove existence results for Feller processes, many of them are restricted to Feller processes with bounded coefficients, i. e. they assume that the symbol is uniformly bounded with respect to the space variable x; see [2,3] for a survey on known results. In fact, there are only few Feller processes with unbounded coefficients which are well studied, including affine processes and the generalized Ornstein{ Uhlenbeck process, cf. [3, Example 1.3f),i)] and the references therein. -
Final Report (PDF)
Foundation of Stochastic Analysis Krzysztof Burdzy (University of Washington) Zhen-Qing Chen (University of Washington) Takashi Kumagai (Kyoto University) September 18-23, 2011 1 Scientific agenda of the conference Over the years, the foundations of stochastic analysis included various specific topics, such as the general theory of Markov processes, the general theory of stochastic integration, the theory of martingales, Malli- avin calculus, the martingale-problem approach to Markov processes, and the Dirichlet form approach to Markov processes. To create some focus for the very broad topic of the conference, we chose a few areas of concentration, including • Dirichlet forms • Analysis on fractals and percolation clusters • Jump type processes • Stochastic partial differential equations and measure-valued processes Dirichlet form theory provides a powerful tool that connects the probabilistic potential theory and ana- lytic potential theory. Recently Dirichlet forms found its use in effective study of fine properties of Markov processes on spaces with minimal smoothness, such as reflecting Brownian motion on non-smooth domains, Brownian motion and jump type processes on Euclidean spaces and fractals, and Markov processes on trees and graphs. It has been shown that Dirichlet form theory is an important tool in study of various invariance principles, such as the invariance principle for reflected Brownian motion on domains with non necessarily smooth boundaries and the invariance principle for Metropolis algorithm. Dirichlet form theory can also be used to study a certain type of SPDEs. Fractals are used as an approximation of disordered media. The analysis on fractals is motivated by the desire to understand properties of natural phenomena such as polymers, and growth of molds and crystals. -
A Course in Interacting Particle Systems
A Course in Interacting Particle Systems J.M. Swart January 14, 2020 arXiv:1703.10007v2 [math.PR] 13 Jan 2020 2 Contents 1 Introduction 7 1.1 General set-up . .7 1.2 The voter model . .9 1.3 The contact process . 11 1.4 Ising and Potts models . 14 1.5 Phase transitions . 17 1.6 Variations on the voter model . 20 1.7 Further models . 22 2 Continuous-time Markov chains 27 2.1 Poisson point sets . 27 2.2 Transition probabilities and generators . 30 2.3 Poisson construction of Markov processes . 31 2.4 Examples of Poisson representations . 33 3 The mean-field limit 35 3.1 Processes on the complete graph . 35 3.2 The mean-field limit of the Ising model . 36 3.3 Analysis of the mean-field model . 38 3.4 Functions of Markov processes . 42 3.5 The mean-field contact process . 47 3.6 The mean-field voter model . 49 3.7 Exercises . 51 4 Construction and ergodicity 53 4.1 Introduction . 53 4.2 Feller processes . 54 4.3 Poisson construction . 63 4.4 Generator construction . 72 4.5 Ergodicity . 79 4.6 Application to the Ising model . 81 4.7 Further results . 85 5 Monotonicity 89 5.1 The stochastic order . 89 5.2 The upper and lower invariant laws . 94 5.3 The contact process . 97 5.4 Other examples . 100 3 4 CONTENTS 5.5 Exercises . 101 6 Duality 105 6.1 Introduction . 105 6.2 Additive systems duality . 106 6.3 Cancellative systems duality . 113 6.4 Other dualities .