A limit theorem for the moments in space of Browian local time increments
|
|
- Isaac Parker
- 5 years ago
- Views:
Transcription
1 A limit theorem for the moments in space of Browian local time increments Simon Campese LMS EPSRC Durham Symposium July 18, 2017
2 Motivation Theorem (Marcus, Rosen, 2006) As h 0, L x+h t L x t h q dx a.s. C q (L x t) q/2 dx 2 / 21
3 CLT for second moment in space Theorem (Chen, Li, Marcus, Rosen, 2010) For h 0, ( 1 ( ) ) 2 h 3/2 L x+h t L x t dx 4ht where Z N (0, 1), independent of (L x t) x R. d c 2 (L x t) 2 dx Z, 3 / 21
4 CLT for second moment in space Theorem (Chen, Li, Marcus, Rosen, 2010) For h 0, ( 1 ( ) ) 2 h 3/2 L x+h t L x t dx 4ht where Z N (0, 1), independent of (L x t) x R. Proof by Method of Moments d c 2 (L x t) 2 dx Z, 3 / 21
5 CLT for second moment in space Theorem (Chen, Li, Marcus, Rosen, 2010) For h 0, ( 1 ( ) ) 2 h 3/2 L x+h t L x t dx 4ht where Z N (0, 1), independent of (L x t) x R. Proof by Method of Moments d c 2 (L x t) 2 dx Z, Alternative proof by Hu/Nualart (2009) using Malliavin calculus 3 / 21
6 CLT for second moment in space Theorem (Chen, Li, Marcus, Rosen, 2010) For h 0, ( 1 ( ) ) 2 h 3/2 L x+h t L x t dx 4ht where Z N (0, 1), independent of (L x t) x R. Proof by Method of Moments d c 2 (L x t) 2 dx Z, Alternative proof by Hu/Nualart (2009) using Malliavin calculus Another proof by Rosen (2011) using self-intersection local times 3 / 21
7 CLT for third moment in space Theorem (Rosen, 2011) For h 0, 1 ( ) 3 h 2 L x+h t Lt x d dx c3 where Z N (0, 1), independent of (L x t) x R. (L x t) 3 dx Z, 4 / 21
8 CLT for third moment in space Theorem (Rosen, 2011) For h 0, 1 ( ) 3 h 2 L x+h t Lt x d dx c3 where Z N (0, 1), independent of (L x t) x R. Again, proof by Method of Moments (L x t) 3 dx Z, 4 / 21
9 CLT for third moment in space Theorem (Rosen, 2011) For h 0, 1 ( ) 3 h 2 L x+h t Lt x d dx c3 where Z N (0, 1), independent of (L x t) x R. Again, proof by Method of Moments Malliavin calculus proof by Hu/Nualart (2010) (L x t) 3 dx Z, 4 / 21
10 Conjecture for fourth moment in space Conjecture (Rosen, 2011) For h 0, ( 1 h 5/2 ( h L x t) 4 dx 24h ( h L x t) 2 L x t dx + 48h 2 (L x t) 2 dx where Z N (0, 1), independent of (L x t) x R. ) d c 4 (L x t) 4 dx Z, 5 / 21
11 Main result: Limit theorem for any moment Theorem (C., 2016) For h 0, ( 1 h (q+1)/2 ( h L x t) q dx q 2 ) + h k a q,k ( h L x t) q 2k (4L x t) k dx k=1 where Z N (0, 1), independent of (L x t) x R. d c q (L x t) q dx Z, 5 / 21
12 Key ingredient: Kailath-Segall identity For continuous L 2 -martingale (M x ) x R, define iterated integrals I 0 (x) = 1, I 1 (x) = x dm u and I q (x) = x I q 1 (u) dm u. 6 / 21
13 Key ingredient: Kailath-Segall identity For continuous L 2 -martingale (M x ) x R, define iterated integrals I 0 (x) = 1, I 1 (x) = x dm u and I q (x) = x I q 1 (u) dm u. Theorem (Kailath, Segall, 1976) q I q (x) = I q 1 (x) M x I q 2 (x) M, M x 6 / 21
14 Key ingredient: Kailath-Segall identity For continuous L 2 -martingale (M x ) x R, define iterated integrals I 0 (x) = 1, I 1 (x) = x dm u and I q (x) = x I q 1 (u) dm u. Theorem (Kailath, Segall, 1976) q I q (x) = I q 1 (x) M x I q 2 (x) M, M x Recursively, q 2 q! I q (x) = a q,k M q 2k x M, M k x k=0 6 / 21
15 Isolation of dominating term Perkins, 1981: (L x t) x R is continuous semimartingale with quadratic variation 4 x Lu t du 7 / 21
16 Isolation of dominating term Perkins, 1981: (L x t) x R is continuous semimartingale with quadratic variation 4 x Lu t du Write h L x t = h M x + h V x. 7 / 21
17 Isolation of dominating term Perkins, 1981: (L x t) x R is continuous semimartingale with quadratic variation 4 x Lu t du Write h L x t = h M x + h V x. Binomial theorem and stochastic analysis: ( h L x t) q dx = ( h M x ) q dx + o (h (q+1)/2) 7 / 21
18 Isolation of dominating term Perkins, 1981: (L x t) x R is continuous semimartingale with quadratic variation 4 x Lu t du Write h L x t = h M x + h V x. Binomial theorem and stochastic analysis: Kailath-Segall: ( h L x t) q dx = ( h M x ) q dx + o (h (q+1)/2) q 2 ( M x ) q = q! I q (x) a q,k ( M x ) q 2k ( M, M x ) k k=1 7 / 21
19 Isolation of dominating term Perkins, 1981: (L x t) x R is continuous semimartingale with quadratic variation 4 x Lu t du Write h L x t = h M x + h V x. Binomial theorem and stochastic analysis: ( h L x t) q dx = ( h M x ) q dx + o (h (q+1)/2) Kailath-Segall: ( consider martingale ( y x dm u at y = x + h) )y x q 2 ( h M x ) q = q! h I q (x) a q,k ( h M x ) q 2k ( h M, M x ) k k=1 7 / 21
20 CLT for dominating term Theorem (C., 2016) For h 0, 1 h (q+1)/2 q! h I q (x) dx d c q (L x t) q dx Z, where Z N (0, 1), independent of (L x t) x R. 8 / 21
21 Key ingredient: asymptotic Ray-Knight theorem (M h x), (N h x) sequences of continuous L 2 -martingales (β h x ), (γ h x ) Dambis-Dubins-Schwarz Brownian motions Theorem (Revuz, Yor, 1999) If sup x [,a] M h, N h prob. 0 x as h 0, then (β h x, γ h x ) converges in distribution to a two-dimensional standard Brownian motion. 9 / 21
22 Proof of CLT for dominating term Iterated stochastic Fubini: h I q (x) dx = = = x+h u1 x u1 u 1 h u2 u 1 h uq 1 K q,h (u 1 ) dm u1 uq u 1 h dm uq... dm u2 dm u1 dx dx dm uq... dm u2 dm u1 10 / 21
23 Proof of CLT for dominating term Iterated stochastic Fubini: h I q (x) dx = = = x+h u1 x u1 u 1 h u2 u 1 h uq 1 K q,h (u 1 ) dm u1 Define martingale ( M h x) x by M h x = uq u 1 h q! x h (q+1)/2 K q,h (u 1 ) dm u1 dm uq... dm u2 dm u1 dx dx dm uq... dm u2 dm u1 10 / 21
24 Proof of CLT for dominating term Use Burkholder-Davis-Gundy-type arguments to show that sup x (,x 0 ] M h, M 0 L1 x for x 0 and M h, M h x x L 1 c 2 q (L u t ) q du for x R {, }. 11 / 21
25 Proof of CLT for dominating term Asymptotic Ray-Knight: ( β, β h, M h, M h ) ( ) d β, β, c 2 q (L u t ) q du, where β, β h DDS-Brownian motions of M and M h, respectively 12 / 21
26 Proof of CLT for dominating term Asymptotic Ray-Knight: ( β, β h, M h, M h ) ( ) d β, β, c 2 q (L u t ) q du, where β, β h DDS-Brownian motions of M and M h, respectively Consequently: M h x = β h M h, M h x d βc 2 q x (Lu t )q du. 12 / 21
27 Proof of CLT for dominating term Asymptotic Ray-Knight: ( β, β h, M h, M h ) ( ) d β, β, c 2 q (L u t ) q du, where β, β h DDS-Brownian motions of M and M h, respectively Consequently: M h x = β h M h, M h x d βc 2 q x (Lu t )q du. Letting x finishes proof 12 / 21
28 Comparison with literature For q = 2 and q = 3: new proofs of CLT by Rosen et al. 13 / 21
29 Comparison with literature For q = 2 and q = 3: new proofs of CLT by Rosen et al. For q = 4, conjecture by Rosen confirmed 13 / 21
30 Generalizations Time variable t can be replaced by suitable stopping time 14 / 21
31 Generalizations Time variable t can be replaced by suitable stopping time In principle, Brownian motion should be replaceable with more general square-integrable martingale 14 / 21
32 Generalizations Time variable t can be replaced by suitable stopping time In principle, Brownian motion should be replaceable with more general square-integrable martingale analogously, one expects to be able to prove analogous results for more general functions than powers 14 / 21
33 Generalizations (cont.) Theorem (C., 2016) ( 1 h ( h L x t h ) q Y t,x dx q 2 + a q,k k=1 (Y t,x ) x R non-negative and nice. ( h L x t h ) q 2k (4 L x t) k Y t,x dx d c q ) (L x t) q Y t,x dx Z, 15 / 21
34 Generalizations (cont.) In particular, for n N 0 : ( 1 h ( h L x t h ) q (L x t) n dx q 2 + a q,k k=1 ( h L x t h ) q 2k (4L x t) k (L x t) n dx d c q ) (L x t) q+n dx Z, 16 / 21
35 Question Recursive argument should yield: ( 1 h ( h L x t h ) q dx C q with C 2p given by Marcus/Rosen (2006). ) (L x t) q/2 dx d C q (L x t) q dx Z, 17 / 21
36 Question Recursive argument should yield: ( 1 h ( h L x t h ) q dx C q with C 2p given by Marcus/Rosen (2006). ) (L x t) q/2 dx d C q Unfortunately, didn t succeed to make this rigorous using this approach. (L x t) q dx Z, 17 / 21
37 small/big blocks approach (w/ M. Podolskij) Theorem (C., Podolskij, 2017+) For f with polynomial growth, ( h L x ) [ ( t f dx P E f 2 )] L x t N dx, h where N N (0, 1), independent of (L x t) x R. 18 / 21
38 small/big blocks approach (w/ M. Podolskij) Theorem (C., Podolskij, 2017+) For f with polynomial growth, ( h L x ) [ ( t f dx P E f 2 )] L x t N dx, h where N N (0, 1), independent of (L x t) x R. Heuristic for proof as before: h L x t h 2L x t h B x t h 18 / 21
39 small/big blocks approach (w/ M. Podolskij) Theorem (C., Podolskij, 2017+) For f with polynomial growth, ( h L x ) [ ( t f dx P E f 2 )] L x t N dx, h where N N (0, 1), independent of (L x t) x R. Heuristic for proof as before: h L x t h 2L x t h B x t h small/big blocks technique to break asymptotic dependence of increments 18 / 21
40 small/big blocks approach (w/ M. Podolskij) In particular: with C 2p+1 = 0. ( h L x t h ) q dx P C q (L x t) q/2 dx, 19 / 21
41 small/big blocks approach (w/ M. Podolskij) Theorem (C., Podolskij, 2017+) For f C 1 (R) such that f, f have polynomial growth, ( 1 h ( h L x ) [ ( t f dx E f 2 ) ] ) L x t N L x t dx h where Z, N N (0, 1), independent of (L x t) x R. d U f (2 L x t ) dx Z 20 / 21
42 small/big blocks approach (w/ M. Podolskij) In particular: ( 1 h ( h L x t h ) q dx C q ) (L x t) q/2 dx d C q (L x t) q dx Z. 21 / 21
An Introduction to Malliavin calculus and its applications
An Introduction to Malliavin calculus and its applications Lecture 3: Clark-Ocone formula David Nualart Department of Mathematics Kansas University University of Wyoming Summer School 214 David Nualart
More informationConvergence at first and second order of some approximations of stochastic integrals
Convergence at first and second order of some approximations of stochastic integrals Bérard Bergery Blandine, Vallois Pierre IECN, Nancy-Université, CNRS, INRIA, Boulevard des Aiguillettes B.P. 239 F-5456
More informationMalliavin calculus and central limit theorems
Malliavin calculus and central limit theorems David Nualart Department of Mathematics Kansas University Seminar on Stochastic Processes 2017 University of Virginia March 8-11 2017 David Nualart (Kansas
More informationThe Azéma-Yor Embedding in Non-Singular Diffusions
Stochastic Process. Appl. Vol. 96, No. 2, 2001, 305-312 Research Report No. 406, 1999, Dept. Theoret. Statist. Aarhus The Azéma-Yor Embedding in Non-Singular Diffusions J. L. Pedersen and G. Peskir Let
More informationTopics in fractional Brownian motion
Topics in fractional Brownian motion Esko Valkeila Spring School, Jena 25.3. 2011 We plan to discuss the following items during these lectures: Fractional Brownian motion and its properties. Topics in
More informationSupplement A: Construction and properties of a reected diusion
Supplement A: Construction and properties of a reected diusion Jakub Chorowski 1 Construction Assumption 1. For given constants < d < D let the pair (σ, b) Θ, where Θ : Θ(d, D) {(σ, b) C 1 (, 1]) C 1 (,
More informationPathwise volatility in a long-memory pricing model: estimation and asymptotic behavior
Pathwise volatility in a long-memory pricing model: estimation and asymptotic behavior Ehsan Azmoodeh University of Vaasa Finland 7th General AMaMeF and Swissquote Conference September 7 1, 215 Outline
More informationLimit theorems for multipower variation in the presence of jumps
Limit theorems for multipower variation in the presence of jumps Ole E. Barndorff-Nielsen Department of Mathematical Sciences, University of Aarhus, Ny Munkegade, DK-8 Aarhus C, Denmark oebn@imf.au.dk
More informationDudley s representation theorem in infinite dimensions and weak characterizations of stochastic integrability
Dudley s representation theorem in infinite dimensions and weak characterizations of stochastic integrability Mark Veraar Delft University of Technology London, March 31th, 214 Joint work with Martin Ondreját
More informationCENTRAL LIMIT THEOREMS FOR SEQUENCES OF MULTIPLE STOCHASTIC INTEGRALS
The Annals of Probability 25, Vol. 33, No. 1, 177 193 DOI 1.1214/911794621 Institute of Mathematical Statistics, 25 CENTRAL LIMIT THEOREMS FOR SEQUENCES OF MULTIPLE STOCHASTIC INTEGRALS BY DAVID NUALART
More informationOn pathwise stochastic integration
On pathwise stochastic integration Rafa l Marcin Lochowski Afican Institute for Mathematical Sciences, Warsaw School of Economics UWC seminar Rafa l Marcin Lochowski (AIMS, WSE) On pathwise stochastic
More informationLecture 21 Representations of Martingales
Lecture 21: Representations of Martingales 1 of 11 Course: Theory of Probability II Term: Spring 215 Instructor: Gordan Zitkovic Lecture 21 Representations of Martingales Right-continuous inverses Let
More informationn E(X t T n = lim X s Tn = X s
Stochastic Calculus Example sheet - Lent 15 Michael Tehranchi Problem 1. Let X be a local martingale. Prove that X is a uniformly integrable martingale if and only X is of class D. Solution 1. If If direction:
More informationSome SDEs with distributional drift Part I : General calculus. Flandoli, Franco; Russo, Francesco; Wolf, Jochen
Title Author(s) Some SDEs with distributional drift Part I : General calculus Flandoli, Franco; Russo, Francesco; Wolf, Jochen Citation Osaka Journal of Mathematics. 4() P.493-P.54 Issue Date 3-6 Text
More informationContents. 1 Preliminaries 3. Martingales
Table of Preface PART I THE FUNDAMENTAL PRINCIPLES page xv 1 Preliminaries 3 2 Martingales 9 2.1 Martingales and examples 9 2.2 Stopping times 12 2.3 The maximum inequality 13 2.4 Doob s inequality 14
More informationAN EXTREME-VALUE ANALYSIS OF THE LIL FOR BROWNIAN MOTION 1. INTRODUCTION
AN EXTREME-VALUE ANALYSIS OF THE LIL FOR BROWNIAN MOTION DAVAR KHOSHNEVISAN, DAVID A. LEVIN, AND ZHAN SHI ABSTRACT. We present an extreme-value analysis of the classical law of the iterated logarithm LIL
More informationBURGESS JAMES DAVIS. B.S. Ohio State 1965 M.S. University of Illinois 1966 Ph.D. University of Illinois 1968
BURGESS JAMES DAVIS April 2016 EDUCATION B.S. Ohio State 1965 M.S. University of Illinois 1966 Ph.D. University of Illinois 1968 PROFESSIONAL EXPERIENCE Assistant Professor Rutgers University 1968-71 Associate
More informationOn the martingales obtained by an extension due to Saisho, Tanemura and Yor of Pitman s theorem
On the martingales obtained by an extension due to Saisho, Tanemura and Yor of Pitman s theorem Koichiro TAKAOKA Dept of Applied Physics, Tokyo Institute of Technology Abstract M Yor constructed a family
More informationThe Pedestrian s Guide to Local Time
The Pedestrian s Guide to Local Time Tomas Björk, Department of Finance, Stockholm School of Economics, Box 651, SE-113 83 Stockholm, SWEDEN tomas.bjork@hhs.se November 19, 213 Preliminary version Comments
More informationSELF-SIMILARITY PARAMETER ESTIMATION AND REPRODUCTION PROPERTY FOR NON-GAUSSIAN HERMITE PROCESSES
Communications on Stochastic Analysis Vol. 5, o. 1 11 161-185 Serials Publications www.serialspublications.com SELF-SIMILARITY PARAMETER ESTIMATIO AD REPRODUCTIO PROPERTY FOR O-GAUSSIA HERMITE PROCESSES
More informationLAN property for sde s with additive fractional noise and continuous time observation
LAN property for sde s with additive fractional noise and continuous time observation Eulalia Nualart (Universitat Pompeu Fabra, Barcelona) joint work with Samy Tindel (Purdue University) Vlad s 6th birthday,
More informationUnfolding the Skorohod reflection of a semimartingale
Unfolding the Skorohod reflection of a semimartingale Vilmos Prokaj To cite this version: Vilmos Prokaj. Unfolding the Skorohod reflection of a semimartingale. Statistics and Probability Letters, Elsevier,
More informationIntroduction to Random Diffusions
Introduction to Random Diffusions The main reason to study random diffusions is that this class of processes combines two key features of modern probability theory. On the one hand they are semi-martingales
More informationSquared Bessel Process with Delay
Southern Illinois University Carbondale OpenSIUC Articles and Preprints Department of Mathematics 216 Squared Bessel Process with Delay Harry Randolph Hughes Southern Illinois University Carbondale, hrhughes@siu.edu
More informationApplications of Ito s Formula
CHAPTER 4 Applications of Ito s Formula In this chapter, we discuss several basic theorems in stochastic analysis. Their proofs are good examples of applications of Itô s formula. 1. Lévy s martingale
More informationI forgot to mention last time: in the Ito formula for two standard processes, putting
I forgot to mention last time: in the Ito formula for two standard processes, putting dx t = a t dt + b t db t dy t = α t dt + β t db t, and taking f(x, y = xy, one has f x = y, f y = x, and f xx = f yy
More informationON THE FIRST TIME THAT AN ITO PROCESS HITS A BARRIER
ON THE FIRST TIME THAT AN ITO PROCESS HITS A BARRIER GERARDO HERNANDEZ-DEL-VALLE arxiv:1209.2411v1 [math.pr] 10 Sep 2012 Abstract. This work deals with first hitting time densities of Ito processes whose
More informationOn a class of stochastic differential equations in a financial network model
1 On a class of stochastic differential equations in a financial network model Tomoyuki Ichiba Department of Statistics & Applied Probability, Center for Financial Mathematics and Actuarial Research, University
More informationHölder continuity and occupation-time formulas for fbm self-intersection local time and its derivative
Hölder continuity and occupation-time formulas for fbm self-intersection local time and its derivative Paul Jung Greg Markowsky August 4, 212 Abstract We prove joint Hölder continuity and an occupation-time
More informationON THE STRUCTURE OF GAUSSIAN RANDOM VARIABLES
ON THE STRUCTURE OF GAUSSIAN RANDOM VARIABLES CIPRIAN A. TUDOR We study when a given Gaussian random variable on a given probability space Ω, F,P) is equal almost surely to β 1 where β is a Brownian motion
More informationPathwise Construction of Stochastic Integrals
Pathwise Construction of Stochastic Integrals Marcel Nutz First version: August 14, 211. This version: June 12, 212. Abstract We propose a method to construct the stochastic integral simultaneously under
More informationRecent results in game theoretic mathematical finance
Recent results in game theoretic mathematical finance Nicolas Perkowski Humboldt Universität zu Berlin May 31st, 2017 Thera Stochastics In Honor of Ioannis Karatzas s 65th Birthday Based on joint work
More informationAlbert N. Shiryaev Steklov Mathematical Institute. On sharp maximal inequalities for stochastic processes
Albert N. Shiryaev Steklov Mathematical Institute On sharp maximal inequalities for stochastic processes joint work with Yaroslav Lyulko, Higher School of Economics email: albertsh@mi.ras.ru 1 TOPIC I:
More informationOn Reflecting Brownian Motion with Drift
Proc. Symp. Stoch. Syst. Osaka, 25), ISCIE Kyoto, 26, 1-5) On Reflecting Brownian Motion with Drift Goran Peskir This version: 12 June 26 First version: 1 September 25 Research Report No. 3, 25, Probability
More informationSome properties of the true self-repelling motion
Some properties of the true self-repelling motion L. Dumaz Statistical laboratory, University of Cambridge Bath-Paris branching structures meeting, June the 9th, 2014 1 / 20 What is a (real-valued) self-repelling
More informationGaussian, Markov and stationary processes
Gaussian, Markov and stationary processes Gonzalo Mateos Dept. of ECE and Goergen Institute for Data Science University of Rochester gmateosb@ece.rochester.edu http://www.ece.rochester.edu/~gmateosb/ November
More informationGiovanni Peccati. 1. Introduction QUANTITATIVE CLTS ON A GAUSSIAN SPACE: A SURVEY OF RECENT DEVELOPMENTS
ESAIM: PROCEEDINGS, January 214, Vol. 44, p. 61-78 SMAI Groupe MAS Journées MAS 212 Exposé plénier QUANTITATIVE CLTS ON A GAUSSIAN SPACE: A SURVEY OF RECENT DEVELOPMENTS Giovanni Peccati Abstract. I will
More informationShort-time expansions for close-to-the-money options under a Lévy jump model with stochastic volatility
Short-time expansions for close-to-the-money options under a Lévy jump model with stochastic volatility José Enrique Figueroa-López 1 1 Department of Statistics Purdue University Statistics, Jump Processes,
More informationarxiv: v2 [math.pr] 22 Aug 2009
On the structure of Gaussian random variables arxiv:97.25v2 [math.pr] 22 Aug 29 Ciprian A. Tudor SAMOS/MATISSE, Centre d Economie de La Sorbonne, Université de Panthéon-Sorbonne Paris, 9, rue de Tolbiac,
More informationRough paths methods 1: Introduction
Rough paths methods 1: Introduction Samy Tindel Purdue University University of Aarhus 216 Samy T. (Purdue) Rough Paths 1 Aarhus 216 1 / 16 Outline 1 Motivations for rough paths techniques 2 Summary of
More informationMax stable Processes & Random Fields: Representations, Models, and Prediction
Max stable Processes & Random Fields: Representations, Models, and Prediction Stilian Stoev University of Michigan, Ann Arbor March 2, 2011 Based on joint works with Yizao Wang and Murad S. Taqqu. 1 Preliminaries
More informationOn the quantiles of the Brownian motion and their hitting times.
On the quantiles of the Brownian motion and their hitting times. Angelos Dassios London School of Economics May 23 Abstract The distribution of the α-quantile of a Brownian motion on an interval [, t]
More informationGeneralized Gaussian Bridges of Prediction-Invertible Processes
Generalized Gaussian Bridges of Prediction-Invertible Processes Tommi Sottinen 1 and Adil Yazigi University of Vaasa, Finland Modern Stochastics: Theory and Applications III September 1, 212, Kyiv, Ukraine
More informationStability of Stochastic Differential Equations
Lyapunov stability theory for ODEs s Stability of Stochastic Differential Equations Part 1: Introduction Department of Mathematics and Statistics University of Strathclyde Glasgow, G1 1XH December 2010
More informationWalsh Diffusions. Andrey Sarantsev. March 27, University of California, Santa Barbara. Andrey Sarantsev University of Washington, Seattle 1 / 1
Walsh Diffusions Andrey Sarantsev University of California, Santa Barbara March 27, 2017 Andrey Sarantsev University of Washington, Seattle 1 / 1 Walsh Brownian Motion on R d Spinning measure µ: probability
More informationCOVARIANCE IDENTITIES AND MIXING OF RANDOM TRANSFORMATIONS ON THE WIENER SPACE
Communications on Stochastic Analysis Vol. 4, No. 3 (21) 299-39 Serials Publications www.serialspublications.com COVARIANCE IDENTITIES AND MIXING OF RANDOM TRANSFORMATIONS ON THE WIENER SPACE NICOLAS PRIVAULT
More informationLecture 19 L 2 -Stochastic integration
Lecture 19: L 2 -Stochastic integration 1 of 12 Course: Theory of Probability II Term: Spring 215 Instructor: Gordan Zitkovic Lecture 19 L 2 -Stochastic integration The stochastic integral for processes
More informationLECTURE 2: LOCAL TIME FOR BROWNIAN MOTION
LECTURE 2: LOCAL TIME FOR BROWNIAN MOTION We will define local time for one-dimensional Brownian motion, and deduce some of its properties. We will then use the generalized Ray-Knight theorem proved in
More informationA Change of Variable Formula with Local Time-Space for Bounded Variation Lévy Processes with Application to Solving the American Put Option Problem 1
Chapter 3 A Change of Variable Formula with Local Time-Space for Bounded Variation Lévy Processes with Application to Solving the American Put Option Problem 1 Abstract We establish a change of variable
More informationNonlinear representation, backward SDEs, and application to the Principal-Agent problem
Nonlinear representation, backward SDEs, and application to the Principal-Agent problem Ecole Polytechnique, France April 4, 218 Outline The Principal-Agent problem Formulation 1 The Principal-Agent problem
More informationA Central Limit Theorem for Fleming-Viot Particle Systems Application to the Adaptive Multilevel Splitting Algorithm
A Central Limit Theorem for Fleming-Viot Particle Systems Application to the Algorithm F. Cérou 1,2 B. Delyon 2 A. Guyader 3 M. Rousset 1,2 1 Inria Rennes Bretagne Atlantique 2 IRMAR, Université de Rennes
More information6. Brownian Motion. Q(A) = P [ ω : x(, ω) A )
6. Brownian Motion. stochastic process can be thought of in one of many equivalent ways. We can begin with an underlying probability space (Ω, Σ, P) and a real valued stochastic process can be defined
More informationDiscrete transport problems and the concavity of entropy
Discrete transport problems and the concavity of entropy Bristol Probability and Statistics Seminar, March 2014 Funded by EPSRC Information Geometry of Graphs EP/I009450/1 Paper arxiv:1303.3381 Motivating
More informationThe Poisson Process in Quantum Stochastic Calculus
The Poisson Process in Quantum Stochastic Calculus Shayanthan Pathmanathan Exeter College University of Oxford A thesis submitted for the degree of Doctor of Philosophy Trinity Term 22 The Poisson Process
More information(B(t i+1 ) B(t i )) 2
ltcc5.tex Week 5 29 October 213 Ch. V. ITÔ (STOCHASTIC) CALCULUS. WEAK CONVERGENCE. 1. Quadratic Variation. A partition π n of [, t] is a finite set of points t ni such that = t n < t n1
More informationSolution for Problem 7.1. We argue by contradiction. If the limit were not infinite, then since τ M (ω) is nondecreasing we would have
362 Problem Hints and Solutions sup g n (ω, t) g(ω, t) sup g(ω, s) g(ω, t) µ n (ω). t T s,t: s t 1/n By the uniform continuity of t g(ω, t) on [, T], one has for each ω that µ n (ω) as n. Two applications
More informationStein s method and weak convergence on Wiener space
Stein s method and weak convergence on Wiener space Giovanni PECCATI (LSTA Paris VI) January 14, 2008 Main subject: two joint papers with I. Nourdin (Paris VI) Stein s method on Wiener chaos (ArXiv, December
More informationStochastic calculus without probability: Pathwise integration and functional calculus for functionals of paths with arbitrary Hölder regularity
Stochastic calculus without probability: Pathwise integration and functional calculus for functionals of paths with arbitrary Hölder regularity Rama Cont Joint work with: Anna ANANOVA (Imperial) Nicolas
More informationBrownian Motion Area with Generatingfunctionology
Brownian Motion Area with Generatingfunctionology Uwe Schwerdtfeger RMIT University/University of Melbourne Alexander von Humboldt Foundation Monash University 3 August, 2011 Some continuous time processes...
More informationLocal times for functions with finite variation: two versions of Stieltjes change of variables formula
Local times for functions with finite variation: two versions of Stieltjes change of variables formula Jean Bertoin and Marc Yor hal-835685, version 2-4 Jul 213 Abstract We introduce two natural notions
More informationBernardo D Auria Stochastic Processes /10. Notes. Abril 13 th, 2010
1 Stochastic Calculus Notes Abril 13 th, 1 As we have seen in previous lessons, the stochastic integral with respect to the Brownian motion shows a behavior different from the classical Riemann-Stieltjes
More informationOn Stochastic Adaptive Control & its Applications. Bozenna Pasik-Duncan University of Kansas, USA
On Stochastic Adaptive Control & its Applications Bozenna Pasik-Duncan University of Kansas, USA ASEAS Workshop, AFOSR, 23-24 March, 2009 1. Motivation: Work in the 1970's 2. Adaptive Control of Continuous
More informationNonlinear Integral Equation Formulation of Orthogonal Polynomials
Nonlinear Integral Equation Formulation of Orthogonal Polynomials Eli Ben-Naim Theory Division, Los Alamos National Laboratory with: Carl Bender (Washington University, St. Louis) C.M. Bender and E. Ben-Naim,
More informationCOPYRIGHTED MATERIAL CONTENTS. Preface Preface to the First Edition
Preface Preface to the First Edition xi xiii 1 Basic Probability Theory 1 1.1 Introduction 1 1.2 Sample Spaces and Events 3 1.3 The Axioms of Probability 7 1.4 Finite Sample Spaces and Combinatorics 15
More informationSelected Exercises on Expectations and Some Probability Inequalities
Selected Exercises on Expectations and Some Probability Inequalities # If E(X 2 ) = and E X a > 0, then P( X λa) ( λ) 2 a 2 for 0 < λ
More informationA connection between the stochastic heat equation and fractional Brownian motion, and a simple proof of a result of Talagrand
A connection between the stochastic heat equation and fractional Brownian motion, and a simple proof of a result of Talagrand Carl Mueller 1 and Zhixin Wu Abstract We give a new representation of fractional
More informationSTOCHASTIC CALCULUS JASON MILLER AND VITTORIA SILVESTRI
STOCHASTIC CALCULUS JASON MILLER AND VITTORIA SILVESTRI Contents Preface 1 1. Introduction 1 2. Preliminaries 4 3. Local martingales 1 4. The stochastic integral 16 5. Stochastic calculus 36 6. Applications
More informationOn the stochastic nonlinear Schrödinger equation
On the stochastic nonlinear Schrödinger equation Annie Millet collaboration with Z. Brzezniak SAMM, Paris 1 and PMA Workshop Women in Applied Mathematics, Heraklion - May 3 211 Outline 1 The NL Shrödinger
More informationONLINE SUPPLEMENT FOR EVALUATING FACTOR PRICING MODELS USING HIGH FREQUENCY PANELS
ONLINE SUPPLEMEN FOR EVALUAING FACOR PRICING MODELS USING HIGH FREQUENCY PANELS YOOSOON CHANG, YONGOK CHOI, HWAGYUN KIM AND JOON Y. PARK hi online upplement contain ome ueful lemma and their proof and
More informationBrownian motion. Samy Tindel. Purdue University. Probability Theory 2 - MA 539
Brownian motion Samy Tindel Purdue University Probability Theory 2 - MA 539 Mostly taken from Brownian Motion and Stochastic Calculus by I. Karatzas and S. Shreve Samy T. Brownian motion Probability Theory
More informationBrownian Motion and Stochastic Calculus
ETHZ, Spring 17 D-MATH Prof Dr Martin Larsson Coordinator A Sepúlveda Brownian Motion and Stochastic Calculus Exercise sheet 6 Please hand in your solutions during exercise class or in your assistant s
More informationWeak universality of the parabolic Anderson model
Weak universality of the parabolic Anderson model Nicolas Perkowski Humboldt Universität zu Berlin July 2017 Stochastic Analysis Symposium Durham Joint work with Jörg Martin Nicolas Perkowski Weak universality
More informationHardy-Stein identity and Square functions
Hardy-Stein identity and Square functions Daesung Kim (joint work with Rodrigo Bañuelos) Department of Mathematics Purdue University March 28, 217 Daesung Kim (Purdue) Hardy-Stein identity UIUC 217 1 /
More informationStochastic Calculus Made Easy
Stochastic Calculus Made Easy Most of us know how standard Calculus works. We know how to differentiate, how to integrate etc. But stochastic calculus is a totally different beast to tackle; we are trying
More informationMATH1190 CALCULUS 1 - NOTES AND AFTERNOTES
MATH90 CALCULUS - NOTES AND AFTERNOTES DR. JOSIP DERADO. Historical background Newton approach - from physics to calculus. Instantaneous velocity. Leibniz approach - from geometry to calculus Calculus
More informationIF B AND f(b) ARE BROWNIAN MOTIONS, THEN f IS AFFINE
ROCKY MOUNTAIN JOURNAL OF MATHEMATICS Volume 47, Number 3, 2017 IF B AND f(b) ARE BROWNIAN MOTIONS, THEN f IS AFFINE MICHAEL R. TEHRANCHI ABSTRACT. It is shown that, if the processes B and f(b) are both
More informationPATH PROPERTIES OF CAUCHY S PRINCIPAL VALUES RELATED TO LOCAL TIME
PATH PROPERTIES OF CAUCHY S PRINCIPAL VALUES RELATED TO LOCAL TIME Endre Csáki,2 Mathematical Institute of the Hungarian Academy of Sciences. Budapest, P.O.B. 27, H- 364, Hungary E-mail address: csaki@math-inst.hu
More informationBessel-like SPDEs. Lorenzo Zambotti, Sorbonne Université (joint work with Henri Elad-Altman) 15th May 2018, Luminy
Bessel-like SPDEs, Sorbonne Université (joint work with Henri Elad-Altman) Squared Bessel processes Let δ, y, and (B t ) t a BM. By Yamada-Watanabe s Theorem, there exists a unique (strong) solution (Y
More informationAnderson-Darling Type Goodness-of-fit Statistic Based on a Multifold Integrated Empirical Distribution Function
Anderson-Darling Type Goodness-of-fit Statistic Based on a Multifold Integrated Empirical Distribution Function S. Kuriki (Inst. Stat. Math., Tokyo) and H.-K. Hwang (Academia Sinica) Bernoulli Society
More informationErrata for Stochastic Calculus for Finance II Continuous-Time Models September 2006
1 Errata for Stochastic Calculus for Finance II Continuous-Time Models September 6 Page 6, lines 1, 3 and 7 from bottom. eplace A n,m by S n,m. Page 1, line 1. After Borel measurable. insert the sentence
More informationCitation Osaka Journal of Mathematics. 41(4)
TitleA non quasi-invariance of the Brown Authors Sadasue, Gaku Citation Osaka Journal of Mathematics. 414 Issue 4-1 Date Text Version publisher URL http://hdl.handle.net/1194/1174 DOI Rights Osaka University
More informationMandelbrot s cascade in a Random Environment
Mandelbrot s cascade in a Random Environment A joint work with Chunmao Huang (Ecole Polytechnique) and Xingang Liang (Beijing Business and Technology Univ) Université de Bretagne-Sud (Univ South Brittany)
More informationOptimal Prediction of the Ultimate Maximum of Brownian Motion
Optimal Prediction of the Ultimate Maximum of Brownian Motion Jesper Lund Pedersen University of Copenhagen At time start to observe a Brownian path. Based upon the information, which is continuously updated
More informationNormal approximation of Poisson functionals in Kolmogorov distance
Normal approximation of Poisson functionals in Kolmogorov distance Matthias Schulte Abstract Peccati, Solè, Taqqu, and Utzet recently combined Stein s method and Malliavin calculus to obtain a bound for
More informationApproximation of BSDEs using least-squares regression and Malliavin weights
Approximation of BSDEs using least-squares regression and Malliavin weights Plamen Turkedjiev (turkedji@math.hu-berlin.de) 3rd July, 2012 Joint work with Prof. Emmanuel Gobet (E cole Polytechnique) Plamen
More informationBernardo D Auria Stochastic Processes /12. Notes. March 29 th, 2012
1 Stochastic Calculus Notes March 9 th, 1 In 19, Bachelier proposed for the Paris stock exchange a model for the fluctuations affecting the price X(t) of an asset that was given by the Brownian motion.
More informationThe distribution of consecutive prime biases
The distribution of consecutive prime biases Robert J. Lemke Oliver 1 Tufts University (Joint with K. Soundararajan) 1 Partially supported by NSF grant DMS-1601398 Chebyshev s Bias Let π(x; q, a) := #{p
More informationPart III Stochastic Calculus and Applications
Part III Stochastic Calculus and Applications Based on lectures by R. Bauerschmidt Notes taken by Dexter Chua Lent 218 These notes are not endorsed by the lecturers, and I have modified them often significantly
More informationSifting the Primes. Gihan Marasingha University of Oxford
Sifting the Primes Gihan Marasingha University of Oxford 18 March 2005 Irreducible forms: q 1 (x, y) := a 1 x 2 + 2b 1 xy + c 1 y 2, q 2 (x, y) := a 2 x 2 + 2b 2 xy + c 2 y 2, a i, b i, c i Z. Variety
More informationOn detection of unit roots generalizing the classic Dickey-Fuller approach
On detection of unit roots generalizing the classic Dickey-Fuller approach A. Steland Ruhr-Universität Bochum Fakultät für Mathematik Building NA 3/71 D-4478 Bochum, Germany February 18, 25 1 Abstract
More informationarxiv: v1 [math.pr] 7 May 2013
The optimal fourth moment theorem Ivan Nourdin and Giovanni Peccati May 8, 2013 arxiv:1305.1527v1 [math.pr] 7 May 2013 Abstract We compute the exact rates of convergence in total variation associated with
More informationConjugate duality in stochastic optimization
Ari-Pekka Perkkiö, Institute of Mathematics, Aalto University Ph.D. instructor/joint work with Teemu Pennanen, Institute of Mathematics, Aalto University March 15th 2010 1 / 13 We study convex problems.
More informationHybrid Atlas Model. of financial equity market. Tomoyuki Ichiba 1 Ioannis Karatzas 2,3 Adrian Banner 3 Vassilios Papathanakos 3 Robert Fernholz 3
Hybrid Atlas Model of financial equity market Tomoyuki Ichiba 1 Ioannis Karatzas 2,3 Adrian Banner 3 Vassilios Papathanakos 3 Robert Fernholz 3 1 University of California, Santa Barbara 2 Columbia University,
More informationTHE SIMPLE URN PROCESS AND THE STOCHASTIC APPROXIMATION OF ITS BEHAVIOR
THE SIMPLE URN PROCESS AND THE STOCHASTIC APPROXIMATION OF ITS BEHAVIOR MICHAEL KANE As a final project for STAT 637 (Deterministic and Stochastic Optimization) the simple urn model is studied, with special
More informationGradient estimates for positive harmonic functions by stochastic analysis
Stochastic Processes and their Applications ( ) www.elsevier.com/locate/spa Gradient estimates for positive harmonic functions by stochastic analysis Marc Arnaudon a, Bruce K. Driver b, Anton Thalmaier
More informationarxiv: v2 [math.pr] 7 Mar 2014
A new proof of an Engelbert-Schmidt type zero-one law for time-homogeneous diffusions arxiv:1312.2149v2 [math.pr] 7 Mar 214 Zhenyu Cui Draft: March 1, 214 Abstract In this paper we give a new proof to
More informationDepartment of Aerospace Engineering AE602 Mathematics for Aerospace Engineers Assignment No. 6
Department of Aerospace Engineering AE Mathematics for Aerospace Engineers Assignment No.. Find the best least squares solution x to x, x 5. What error E is minimized? heck that the error vector ( x, 5
More informationConfidence Intervals of Prescribed Precision Summary
Confidence Intervals of Prescribed Precision Summary Charles Stein showed in 1945 that by using a two stage sequential procedure one could give a confidence interval for the mean of a normal distribution
More information1. Aufgabenblatt zur Vorlesung Probability Theory
24.10.17 1. Aufgabenblatt zur Vorlesung By (Ω, A, P ) we always enote the unerlying probability space, unless state otherwise. 1. Let r > 0, an efine f(x) = 1 [0, [ (x) exp( r x), x R. a) Show that p f
More informationRoth s Theorem on Arithmetic Progressions
September 25, 2014 The Theorema of Szemerédi and Roth For Λ N the (upper asymptotic) density of Λ is the number σ(λ) := lim sup N Λ [1, N] N [0, 1] The Theorema of Szemerédi and Roth For Λ N the (upper
More information