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BROWNIAN MOTION A tutorial

BROWNIAN MOTION A tutorial. Krzysztof Burdzy University of Washington. A paradox. (*) is maximized by f(t) = 0, t>0. The most likely (?!?) shape of a Brownian path:. Microsoft stock - the last 5 years. Definition of Brownian motion. Brownian motion is the unique process

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BROWNIAN MOTION A tutorial

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  1. BROWNIAN MOTIONA tutorial Krzysztof Burdzy University of Washington

  2. A paradox

  3. (*) is maximized by f(t) = 0, t>0 The most likely (?!?) shape of a Brownian path: Microsoft stock - the last 5 years

  4. Definition of Brownian motion Brownian motion is the unique process with the following properties: (i) No memory (ii) Invariance (iii) Continuity (iv)

  5. Memoryless process are independent

  6. Invariance The distribution of depends only on t.

  7. Path regularity (i) is continuous a.s. (ii) is nowhere differentiable a.s.

  8. Why Brownian motion? Brownian motion belongs to several families of well understood stochastic processes: Markov processes Martingales Gaussian processes Levy processes

  9. Markov processes • The theory of Markov processes uses • tools from several branches of analysis: • Functional analysis (transition semigroups) • Potential theory (harmonic, Green functions) • Spectral theory (eigenfunction expansion) • PDE’s (heat equation)

  10. Martingales Martingales are the only family of processes for which the theory of stochastic integrals is fully developed, successful and satisfactory.

  11. Gaussian processes is multidimensional normal (Gaussian) • Excellent bounds for tails • Second moment calculations • Extensions to unordered parameter(s)

  12. The Ito formula

  13. Random walk Independent steps, P(up)=P(down) (in distribution)

  14. Scaling Central Limit Theorem (CLT), parabolic PDE’s

  15. Cameron-Martin-Girsanov formula Multiply the probability of each Brownian path by The effect is the same as replacing with

  16. Invariance (2) Time reversal

  17. Brownian motion and the heat equation – temperature at location x at time t Heat equation: Forward representation Backward representation (Feynman-Kac formula)

  18. Multidimensional Brownian motion • independent 1-dimensional • Brownian motions - d-dimensional Brownian motion

  19. Feynman-Kac formula (2)

  20. Invariance (3) The d-dimensional Brownian motion is invariant under isometries of the d-dimensional space. It also inherits invariance properties of the 1-dimensional Brownian motion.

  21. Conformal invariance analytic has the same distribution as

  22. The Ito formula Disappearing terms (1) If then

  23. Brownian martingales Theorem (Martingale representation theorem). {Brownian martingales} = {stochastic integrals}

  24. The Ito formula Disappearing terms (2)

  25. Mild modifications of BM • Mild = the new process corresponds • to the Laplacian • Killing – Dirichlet problem • Reflection – Neumann problem • Absorption – Robin problem

  26. Related models – diffusions • Markov property – yes • Martingale – only if • Gaussian – no, but Gaussian tails

  27. Related models – stable processes Brownian motion – Stable processes – • Markov property – yes • Martingale – yes and no • Gaussian – no • Price to pay: jumps, heavy tails,

  28. Related models – fractional BM • Markov property – no • Martingale – no • Gaussian – yes • Continuous

  29. Related models – super BM Super Brownian motion is related to and to a stochastic PDE.

  30. Related models – SLE Schramm-Loewner Evolution is a model for non-crossing conformally invariant 2-dimensional paths.

  31. (i) is continuous a.s. is nowhere differentiable a.s. is Holder Local Law if Iterated Logarithm Path properties

  32. Exceptional points For any fixed s>0, a.s., There exist s>0, a.s., such that

  33. Cut points For any fixed t>0, a.s., the 2-dimensional Brownian path contains a closed loop around in every interval Almost surely, there exist such that

  34. Intersection properties

  35. Intersections of random sets The dimension of Brownian trace is 2 in every dimension.

  36. Invariance principle • Random walk converges to Brownian • motion (Donsker (1951)) • (ii) Reflected random walk converges • to reflected Brownian motion • (Stroock and Varadhan (1971) - domains, • B and Chen (2007) – uniform domains, not all • domains) • (iii) Self-avoiding random walk in 2 dimensions • converges to SLE (200?) • (open problem)

  37. Local time

  38. Local time (2)

  39. Local time (3) Inverse local time is a stable process with index ½.

  40. References • R. Bass Probabilistic Techniques in Analysis, Springer, 1995 • F. Knight Essentials of Brownian Motion and Diffusion, AMS, 1981 • I. Karatzas and S. Shreve Brownian Motion and Stochastic Calculus, Springer, 1988

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