Computable analysis of martingales and related measure-theoretic topics, with an emphasis on algorithmic randomness
|
|
- Neal Cook
- 5 years ago
- Views:
Transcription
1 Computable analysis of martingales and related measure-theoretic topics, with an emphasis on algorithmic randomness Jason Rute Carnegie Mellon University PhD Defense August, Jason Rute (CMU) Randomness, martingales, and more PhD Defense 1 / 32
2 Martingales, computable analysis, and randomness What is a martingale? Informally: A martingale is a gambling strategy. Example: Bet on a fair coin. T 0 H T -1 1 H T H d(hh) M For logicians: If d(σ) is a martingale in the computability theory sense and x 2 N, then we are considering M n (x) = d(x n). Jason Rute (CMU) Randomness, martingales, and more PhD Defense 3 / 32
3 Martingales, computable analysis, and randomness What is a martingale in general? Formally: A martingale is a sequence of integrable functions M n : (Ω,P) R such that Slogan: E[M n+1 M 0,M 1,...,M n ] = M n. The expectation of the future is the present (conditioned on the past & present). Jason Rute (CMU) Randomness, martingales, and more PhD Defense 4 / 32
4 Martingales, computable analysis, and randomness An a.e. convergence theorem Example (Doob s martingale convergence theorem) Let (M n ) be a martingale. Assume sup n M n L 1 <. Then M n converges a.e. In particular, lim n M n < a.s. 4 Questions 1 (Computable analysis) Is the rate of convergence of (M n ) computable (from the martingale (M n ))? 2 (Computable analysis) If not, what other information is needed to compute a rate of convergence? 3 (Algorithmic randomness) For which points x does M n (x) converge for all computable L 1 -bounded martingales? 4 (Algorithmic randomness) Which assumptions are needed to characterize convergence on (insert favorite randomness notion)? Jason Rute (CMU) Randomness, martingales, and more PhD Defense 5 / 32
5 Martingales, computable analysis, and randomness Computable analysis Computable reals A real number a is computable if it can be effectively approximated by rationals. Example π is computable. We can compute a sequence of rationals q n such that q n π 2 n. (This sequence is called a name for x.) Computable points, functions This definition extends to any complete separable metric space with a nice countable set of simple points {q n } n. Examples Computable L 1 -functions, computable sequences of reals, computable sequences of L 1 -functions, computable Borel measures, etc. Jason Rute (CMU) Randomness, martingales, and more PhD Defense 6 / 32
6 Martingales, computable analysis, and randomness Computable analysis Computable maps We say that y is computable from x if there is an algorithm which computes a name for y uniformly from a name for x, more formally takes in ε > 0 keeps reading the name for x: q 0,q 1,q 2,... when it has a close enough approximation q n, it returns r n such that r n y ε. Note Total computable maps are continuous. Most continuous maps in practice are computable. Jason Rute (CMU) Randomness, martingales, and more PhD Defense 7 / 32
7 Question 1 Martingales, computable analysis, and randomness Question 1. Is the rate of a.e. convergence computable? A rate of a.e. convergence is some n(ε,δ) such that { } µ x sup f n (x) f (x) ε δ, n n(ε,δ) i.e. f n f with an ε-uniform rate of convergence outside a set of measure δ. Theorem The rate of a.e. convergence of a martingale M n is not necessarily computable. Proof sketch. Code in the halting problem. Enumerate the programs {e n } that halt and bet 3 e n dollars that the e n th program halts. Any rate of convergence would compute the halting problem. Jason Rute (CMU) Randomness, martingales, and more PhD Defense 8 / 32
8 Martingales, computable analysis, and randomness Question 2 Question 2. What other information is needed to compute a rate of convergence? Theorem (R.) The rate of convergence of M n M is computable uniformly from (M n ) (as a sequence of L 1 functions), M (as an L 1 function), and sup n M n L 1. Note This is not just because we know the limit M. Without sup n M L 1 being computable, the limit could be 0 but the rate of convergence not computable. Jason Rute (CMU) Randomness, martingales, and more PhD Defense 9 / 32
9 Martingales, computable analysis, and randomness Question 2 Question 2. What other information is needed to compute a rate of convergence? Theorem (R.) The rate of convergence of M n M is computable uniformly from (M n ) (as a sequence of L 1 functions), M (as an L 1 function), and sup n M n L 1. Theorem (R.) The rate of convergence (a.e. and L 2 ) of M n M is computable uniformly from (M n ) (as a sequence of L 2 functions) and sup n M n L 2 = M L 2. Jason Rute (CMU) Randomness, martingales, and more PhD Defense 9 / 32
10 Martingales, computable analysis, and randomness Question 3 Question 3. For which points x does the following hold? M n (x) converges for all computable L 1 -bounded martingales (M n ) Theorem Every L 1 -bounded martingale converges on almost every point. Corollary For almost every point, every computable L 1 -bdd martingales converges. There are countably many computable martingales. Question What is this measure-one set of points? Jason Rute (CMU) Randomness, martingales, and more PhD Defense 10 / 32
11 Martingales, computable analysis, and randomness Algorithmic randomness π random.org Are either of these random? How can we check? 1 Are they normal? 2 Do they satisfy the law of the iterated logarithm? 3 Is the number not π? 4 It did not come from random.org? Wait! Is any bit sequence random? They all fail some statistical test! A bit sequence is algorithmically random if it passes all computable statistical tests. Jason Rute (CMU) Randomness, martingales, and more PhD Defense 11 / 32
12 Martingales, computable analysis, and randomness The randomness zoo Randomness Zoo Antoine Taveneaux 1 1 R 1 1 MLR 1 1 R... 3MLR 2MLR SR 0 There are many randomness notions. Most start out on 2 N (coin flipping). Some of the more natural ones are: 2-randomness Weak 2-randomness Difference randomness Martin-Löf randomness Computable randomness Schnorr randomness Kurtz randomness The natural ones have connections with computable analysis. The natural ones can be extended to other computable probability spaces. FBoundR CBoundR InjR PCR CR SR PolyR WR P ermr PInjR MWCStoch dim 1 compr s<1 dim s compr s 0 <s dim s0 compr LimitR W 2R DemR W DemR BalancedR DiffR MLR KLR KLStoch PMWCStoch Cdim 1 R s<1 Cdim s R s 0 <s Cdim s0 R Randomness Zoo (Antoine Taveneaux) Jason Rute (CMU) Randomness, martingales, and more PhD Defense 12 / 32
13 Martingales, computable analysis, and randomness Martin-Löf and Schnorr randomness Definition A Martin-Löf test is a computable sequence (U n ) of effectively open sets (uniform sequence of Σ 0 1 sets) such that µ(u n) 2 n. A Schnorr test is a Martin-Löf test, where µ(u n ) is uniformly computable. x is Martin-Löf/Schnorr random (for the measure µ) if x n U n for each ML/Schnorr tests. Jason Rute (CMU) Randomness, martingales, and more PhD Defense 13 / 32
14 Martingales, computable analysis, and randomness Question 3 Again Question 3. For which points x does the following hold? M n (x) converges for all computable L 1 -bounded martingales (M n ) Theorem (Takahashi; Merkle-Mihalovic-Slaman; Dean; R.) The answer is Martin-Löf randomness, even if the martingales are dyadic or nonnegative (but not both). Corollary Doob s martingale convergence theorem characterizes Martin-Löf randomness! Jason Rute (CMU) Randomness, martingales, and more PhD Defense 14 / 32
15 Question 4 Martingales, computable analysis, and randomness Question 4. Which assumptions characterize convergence on Schnorr randoms? Lemma (R.) If (f n ) and f are L 1 -computable, and f n f effectively a.e., then f n (x) f (x) on Schnorr randoms x. Here f (x) = limn p n (x) where (p n ) is a sequence of simple functions f p n L 1 < 2 n. (We need this since f is an equivalence class.) Jason Rute (CMU) Randomness, martingales, and more PhD Defense 15 / 32
16 Question 4 Martingales, computable analysis, and randomness Question 4. Which assumptions characterize convergence on Schnorr randoms? Lemma (R.) If (f n ) and f are L 1 -computable, and f n f effectively a.e., then f n (x) f (x) on Schnorr randoms x. Theorem (R.) Assume (M n ) is L 1 -computable, M is L 1 -computable, and sup n M n L 1 is computable. Then M n M effectively a.e. Therefore (for free!) M n (x) M (x) on Schnorr randoms x. Jason Rute (CMU) Randomness, martingales, and more PhD Defense 15 / 32
17 Question 4 Martingales, computable analysis, and randomness Question 4. Which assumptions characterize convergence on Schnorr randoms? Lemma (R.) If (f n ) and f are L 1 -computable, and f n f effectively a.e., then f n (x) f (x) on Schnorr randoms x. Theorem (R.) Assume (M n ) is L 1 -computable, M is L 1 -computable, and sup n M n L 1 is computable. Then M n (x) M (x) on Schnorr randoms x. Theorem (R.) If x is not Schnorr random, there is some L 1 -computable martingale (M n ), with an L 1 -computable limit M, and sup n M n L 1 = 1 such that lim n M n (x) =. Jason Rute (CMU) Randomness, martingales, and more PhD Defense 15 / 32
18 Martingales, computable analysis, and randomness Martingale convergence Theorem Assume (M n ) is L 1 -computable, M is L 1 -computable, and sup n M n L 1 is computable. Then M n M effectively a.e. Hence, M n (x) M (x) on Schnorr randoms x. Proof sketch. Decompose M n = N n + L n where N n converges in L 1 and L n converges to 0. Handle each case individually. Jason Rute (CMU) Randomness, martingales, and more PhD Defense 16 / 32
19 Martingales, computable analysis, and randomness Martingale convergence in L 1 Theorem Assume (M n ) is L 1 -computable, M is L 1 -computable and M n M in L 1. Then Proof sketch. M n M effectively a.e. and in L 1. Fix k, and find n k such that M M nk L 1 2 2k. Facts: M n M nk L 1 is increasing and (M n M nk ) L1 (M M nk ). M n M effectively in L 1 since n n k M n M nk L 1 M M nk L 1 2 2k. M n M effectively a.e. since (by Kolmogorov s inequality) { } µ sup M n M nk 2 k sup n M n M nk L 1 n 2 k 2 2k 2 k 2 k. Jason Rute (CMU) Randomness, martingales, and more PhD Defense 17 / 32
20 Other a.e. convergence theorems Similar results All these theorems can be used to characterize Schnorr randomness Differentiability of bounded variation functions Lebesgue differentiation theorem for signed measures Ergodic theorem (Avigad-Gerhardy-Towsner; Gács-Hoyrup-Rojas; Galatalo-Hoyrup-Rojas) Sub/supermartingale convergence theorem (nonnegative) Backwards martingale convergence theorem Monotone convergence theorem Strong law of large numbers De Finetti s theorem Jason Rute (CMU) Randomness, martingales, and more PhD Defense 19 / 32
21 Other a.e. convergence theorems An observation Observation In most common a.e. convergence theorems, the rate of convergence is computable from the sequence (f n ), the limit f, and the bounds sup n f n L 1 and inf n f n L 1. There are easy, but contrived, counterexamples. Can this observation be made into a theorem with a few more assumptions? Sub/supermartingales What about sub/supermartingales? This is one of the only cases I have not been able to work out. It is also one of the only cases where f n L 1 is not monotone (or nearly monotone). Jason Rute (CMU) Randomness, martingales, and more PhD Defense 20 / 32
22 Other a.e. convergence theorems Lebesgue differentiation theorem Theorem (R.; Pathak-Rojas-Simpson) Assume f is L 1 -computable on [0,1]. Then 1 2r x+r x r f (x)dx f (x) effectively a.e. r and 1 2r x+r x r f (x)dx f (x) on Schnorr random x. r Theorem (R.; Pathak-Rojas-Simpson) If x is not Schnorr random, there is some L 1 -computable f such that 1 2r x+r x r f (y)dy. Jason Rute (CMU) Randomness, martingales, and more PhD Defense 21 / 32
23 Other a.e. convergence theorems Backwards martingales with an application Theorem (R.) Assume (M n ) is an L 1 -computable backwards martingale, M is L 1 -comp. Then M n M effectively a.e. and in L 1. Hence M n M on Schnorr random x. Corollary (Variation on Kučera s theorem, R.) Let C be a closed set of positive computable measure µ(c). Let x be Schnorr random. There is some y C such that y is the same as x but with finitely many bits permuted. Proof sketch. Let M n be the average of 1 C under all permutations of the first n bits. It turns out M n is a reverse martingale with limit µ(c). Then M n (x) µ(c) by the above theorem. Hence M n (x) > 0 for some n. Hence some y C where y is a permutation of first n bits of x. Jason Rute (CMU) Randomness, martingales, and more PhD Defense 22 / 32
24 Computable randomness on other spaces The randomness zoo Randomness Zoo Antoine Taveneaux 1 1 R 1 1 MLR 1 1 R... 3MLR 2MLR SR 0 There are many randomness notions. Most start out on 2 N (coin flipping). Some of the more natural ones are: 2-randomness Weak 2-randomness Difference randomness Martin-Löf randomness Computable randomness Schnorr randomness Kurtz randomness The natural ones have connections with computable analysis. The natural ones can be extended to other computable probability spaces. FBoundR CBoundR InjR PCR CR SR PolyR WR P ermr PInjR MWCStoch dim 1 compr s<1 dim s compr s 0 <s dim s0 compr LimitR W 2R DemR W DemR BalancedR DiffR MLR KLR KLStoch PMWCStoch Cdim 1 R s<1 Cdim s R s 0 <s Cdim s0 R Randomness Zoo (Antoine Taveneaux) Jason Rute (CMU) Randomness, martingales, and more PhD Defense 24 / 32
25 Computable randomness on other spaces Computable randomness Definition A test for computable randomness is a nonnegative dyadic martingale M : 2 <ω R + such that µ(σ0)m(σ0) + µ(σ1)m(σ1) = µ(σ)m(σ) and M(σ) is computable from σ, provided that µ(σ) > 0. Definition We say that x (2 N,µ) is computably random if limsup n M(x n) < for all martingale tests M. Jason Rute (CMU) Randomness, martingales, and more PhD Defense 25 / 32
26 Computable randomness on other spaces Computably random Brownian motion? B a computably random Brownian motion. Are these also computably random? B(1) (Gaussian distribution) Last hitting time before t = 1 (arcsin distribution) Maximum/minimum values for t [0,1] argmax/argmin set of zeros {t : B(t) = 0} We need a good definition of computable randomness for Brownian motion and for reals (with, say, Gaussian distribution). Which maps preserve computable randomness? For example B B(1)? Jason Rute (CMU) Randomness, martingales, and more PhD Defense 26 / 32
27 Computable randomness on other spaces Computable randomness on [0,1]. Base invariance Say that x is random on [0,1] (with Lebesgue measure) if its binary digits are random on 2 N. its decimal digits are random on 10 N. Are these the same? What about other bases? Easy 2-randomness, weak 2-randomness, difference randomness, Martin-Löf randomness, Schnorr randomness, and Kurtz randomness are base invarient! Brattka, Miller, Nies; Silveira Computable randomness is base invariant. The proofs for comp. randomness are not trivial. (The Brattka, Miller, Nies proof uses differentiability and does not even work in multiple dimensions.) Jason Rute (CMU) Randomness, martingales, and more PhD Defense 27 / 32
28 Computable randomness on other spaces Computable randomness on Polish space X. Let X be a computable Polish metric space. Let µ be a computable measure on X. Break up X into cells (Gács; Hoyrup-Rojas; Bosserhoff) Now (X,µ) looks like a measure (2 N,ν) on Cantor space Say x X is computably random if the corresponding point in (2 N,ν) is computably random. Theorem (R.) It does not matter how we break up X into cells. Example We then have computably random Brownian motion, Gaussian distributed reals, etc. Jason Rute (CMU) Randomness, martingales, and more PhD Defense 28 / 32
29 Computable randomness on other spaces Computing randoms from randoms Almost-everwhere computable maps Let T : (2 N,µ 1 ) (2 N,µ 2 ) come from an algorithm which 1 Takes in a sequence of coin flips with distribution µ 1. 2 Outputs a sequence of coin flips with distribution µ 2. 3 Almost every input has an output. However, there is a problem with computable randomness! Theorem (Bienvenu-Porter; R.) There is an a.e. computable map T : (2 N,λ) (2 N,λ) where x is computably random, but T(x) is not computably random! Jason Rute (CMU) Randomness, martingales, and more PhD Defense 29 / 32
30 Computable randomness on other spaces Preservation of computable randomness Theorem (R.) Assume T : (2 N,µ 1 ) (2 N,µ 2 ) and T 1 : (2 N,µ 2 ) (2 N,µ 1 ) are a.e. computable such that T T 1 = id and T 1 T = id. Then T preserves computable randomness. Proof sketch. Take y (2 N,µ 2 ) not computably random. There is a martingale M which succeeds on y (lim n M 2 (y n) = ). Slow down the martingale by saving some of your money (savings trick). This gives an absolutely continuous measure ν 2 (σ) = M 2 (σ)µ 2 (σ). Since ν 2 µ 2, then T 1 is a.e. computable on ν 2. Let ν 1 = ν 2 T 1 (pushforward of ν 2 along T 1 ). Since T 1 is ν 2 -a.e. comp., then ν 1 is comp. Let M 1 (σ) = ν 1(σ) µ 1 (σ) and x = T 1 (y). It can be shown that M 1 (x n). So x is not computably random. Jason Rute (CMU) Randomness, martingales, and more PhD Defense 30 / 32
31 Computable randomness on other spaces More recent work (not in the dissertation) Theorem (R.) Assume T : (X,µ 1 ) (Y,µ 2 ) is effectively measurable with a computable conditional probability µ 1 [ T]. Then T(x) preserves computable randomness. Theorem (R.) TFAE: TFAE: (X,µ) is computable. There exists a map T : (2 N,λ) (X,µ) as above. x is computably random on (X,µ). x = T(ω) for some computably random x (2 N,λ). Theorem (R.) If (M n,f n ) is an L 1 -comp. martingale, sup n M n L 1 is comp., and f E[ f F ] is (L 1 L 1 )-computable, then M n (x) converges on computable randoms. Jason Rute (CMU) Randomness, martingales, and more PhD Defense 31 / 32
32 Thank You! Final remarks These slides will be available on my webpage: math.cmu.edu/~jrute Or just Google me, Jason Rute. Jason Rute (CMU) Randomness, martingales, and more PhD Defense 32 / 32
Computable randomness and martingales a la probability theory
Computable randomness and martingales a la probability theory Jason Rute www.math.cmu.edu/ jrute Mathematical Sciences Department Carnegie Mellon University November 13, 2012 Penn State Logic Seminar Something
More informationOutline of Lecture 1. Martin-Löf tests and martingales
Outline of Lecture 1 Martin-Löf tests and martingales The Cantor space. Lebesgue measure on Cantor space. Martin-Löf tests. Basic properties of random sequences. Betting games and martingales. Equivalence
More informationMartingales. by D. Cox December 2, 2009
Martingales by D. Cox December 2, 2009 1 Stochastic Processes. Definition 1.1 Let T be an arbitrary index set. A stochastic process indexed by T is a family of random variables (X t : t T) defined on a
More informationComparison of proof techniques in game-theoretic probability and measure-theoretic probability
Comparison of proof techniques in game-theoretic probability and measure-theoretic probability Akimichi Takemura, Univ. of Tokyo March 31, 2008 1 Outline: A.Takemura 0. Background and our contributions
More informationMASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 11 10/9/2013. Martingales and stopping times II
MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.65/15.070J Fall 013 Lecture 11 10/9/013 Martingales and stopping times II Content. 1. Second stopping theorem.. Doob-Kolmogorov inequality. 3. Applications of stopping
More informationCOMPARING NOTIONS OF RANDOMNESS
COMPARING NOTIONS OF RANDOMNESS BART KASTERMANS AND STEFFEN LEMPP Abstract. It is an open problem in the area of effective (algorithmic) randomness whether Kolmogorov-Loveland randomness coincides with
More information4 Martingales in Discrete-Time
4 Martingales in Discrete-Time Suppose that (Ω, F, P is a probability space. Definition 4.1. A sequence F = {F n, n = 0, 1,...} is called a filtration if each F n is a sub-σ-algebra of F, and F n F n+1
More informationDrunken Birds, Brownian Motion, and Other Random Fun
Drunken Birds, Brownian Motion, and Other Random Fun Michael Perlmutter Department of Mathematics Purdue University 1 M. Perlmutter(Purdue) Brownian Motion and Martingales Outline Review of Basic Probability
More informationAsymptotic results discrete time martingales and stochastic algorithms
Asymptotic results discrete time martingales and stochastic algorithms Bernard Bercu Bordeaux University, France IFCAM Summer School Bangalore, India, July 2015 Bernard Bercu Asymptotic results for discrete
More informationFinite Additivity in Dubins-Savage Gambling and Stochastic Games. Bill Sudderth University of Minnesota
Finite Additivity in Dubins-Savage Gambling and Stochastic Games Bill Sudderth University of Minnesota This talk is based on joint work with Lester Dubins, David Heath, Ashok Maitra, and Roger Purves.
More informationPROBABILISTIC ALGORITHMIC RANDOMNESS October 10, 2012
PROBABILISTIC ALGORITHMIC RANDOMNESS October 10, 2012 SAM BUSS 1 AND MIA MINNES 2 Abstract. We introduce martingales defined by probabilistic strategies, in which random bits are used to decide whether
More informationIntroduction to Probability Theory and Stochastic Processes for Finance Lecture Notes
Introduction to Probability Theory and Stochastic Processes for Finance Lecture Notes Fabio Trojani Department of Economics, University of St. Gallen, Switzerland Correspondence address: Fabio Trojani,
More informationConstructing Markov models for barrier options
Constructing Markov models for barrier options Gerard Brunick joint work with Steven Shreve Department of Mathematics University of Texas at Austin Nov. 14 th, 2009 3 rd Western Conference on Mathematical
More informationOptimal martingale transport in general dimensions
Optimal martingale transport in general dimensions Young-Heon Kim University of British Columbia Based on joint work with Nassif Ghoussoub (UBC) and Tongseok Lim (Oxford) May 1, 2017 Optimal Transport
More informationProbability without Measure!
Probability without Measure! Mark Saroufim University of California San Diego msaroufi@cs.ucsd.edu February 18, 2014 Mark Saroufim (UCSD) It s only a Game! February 18, 2014 1 / 25 Overview 1 History of
More informationEquivalence between Semimartingales and Itô Processes
International Journal of Mathematical Analysis Vol. 9, 215, no. 16, 787-791 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/1.12988/ijma.215.411358 Equivalence between Semimartingales and Itô Processes
More informationChapter 3 Common Families of Distributions. Definition 3.4.1: A family of pmfs or pdfs is called exponential family if it can be expressed as
Lecture 0 on BST 63: Statistical Theory I Kui Zhang, 09/9/008 Review for the previous lecture Definition: Several continuous distributions, including uniform, gamma, normal, Beta, Cauchy, double exponential
More informationMath-Stat-491-Fall2014-Notes-V
Math-Stat-491-Fall2014-Notes-V Hariharan Narayanan December 7, 2014 Martingales 1 Introduction Martingales were originally introduced into probability theory as a model for fair betting games. Essentially
More informationOn Existence of Equilibria. Bayesian Allocation-Mechanisms
On Existence of Equilibria in Bayesian Allocation Mechanisms Northwestern University April 23, 2014 Bayesian Allocation Mechanisms In allocation mechanisms, agents choose messages. The messages determine
More informationLaws of probabilities in efficient markets
Laws of probabilities in efficient markets Vladimir Vovk Department of Computer Science Royal Holloway, University of London Fifth Workshop on Game-Theoretic Probability and Related Topics 15 November
More informationBrownian Motion, the Gaussian Lévy Process
Brownian Motion, the Gaussian Lévy Process Deconstructing Brownian Motion: My construction of Brownian motion is based on an idea of Lévy s; and in order to exlain Lévy s idea, I will begin with the following
More informationLast Time. Martingale inequalities Martingale convergence theorem Uniformly integrable martingales. Today s lecture: Sections 4.4.1, 5.
MATH136/STAT219 Lecture 21, November 12, 2008 p. 1/11 Last Time Martingale inequalities Martingale convergence theorem Uniformly integrable martingales Today s lecture: Sections 4.4.1, 5.3 MATH136/STAT219
More informationMarkov Decision Processes II
Markov Decision Processes II Daisuke Oyama Topics in Economic Theory December 17, 2014 Review Finite state space S, finite action space A. The value of a policy σ A S : v σ = β t Q t σr σ, t=0 which satisfies
More informationStochastic calculus Introduction I. Stochastic Finance. C. Azizieh VUB 1/91. C. Azizieh VUB Stochastic Finance
Stochastic Finance C. Azizieh VUB C. Azizieh VUB Stochastic Finance 1/91 Agenda of the course Stochastic calculus : introduction Black-Scholes model Interest rates models C. Azizieh VUB Stochastic Finance
More informationHomework Assignments
Homework Assignments Week 1 (p. 57) #4.1, 4., 4.3 Week (pp 58 6) #4.5, 4.6, 4.8(a), 4.13, 4.0, 4.6(b), 4.8, 4.31, 4.34 Week 3 (pp 15 19) #1.9, 1.1, 1.13, 1.15, 1.18 (pp 9 31) #.,.6,.9 Week 4 (pp 36 37)
More informationCopyright (C) 2001 David K. Levine This document is an open textbook; you can redistribute it and/or modify it under the terms of version 1 of the
Copyright (C) 2001 David K. Levine This document is an open textbook; you can redistribute it and/or modify it under the terms of version 1 of the open text license amendment to version 2 of the GNU General
More informationArbitrage of the first kind and filtration enlargements in semimartingale financial models. Beatrice Acciaio
Arbitrage of the first kind and filtration enlargements in semimartingale financial models Beatrice Acciaio the London School of Economics and Political Science (based on a joint work with C. Fontana and
More informationBROWNIAN MOTION Antonella Basso, Martina Nardon
BROWNIAN MOTION Antonella Basso, Martina Nardon basso@unive.it, mnardon@unive.it Department of Applied Mathematics University Ca Foscari Venice Brownian motion p. 1 Brownian motion Brownian motion plays
More informationFunctional vs Banach space stochastic calculus & strong-viscosity solutions to semilinear parabolic path-dependent PDEs.
Functional vs Banach space stochastic calculus & strong-viscosity solutions to semilinear parabolic path-dependent PDEs Andrea Cosso LPMA, Université Paris Diderot joint work with Francesco Russo ENSTA,
More informationConvergence. Any submartingale or supermartingale (Y, F) converges almost surely if it satisfies E Y n <. STAT2004 Martingale Convergence
Convergence Martingale convergence theorem Let (Y, F) be a submartingale and suppose that for all n there exist a real value M such that E(Y + n ) M. Then there exist a random variable Y such that Y n
More informationINTERIM CORRELATED RATIONALIZABILITY IN INFINITE GAMES
INTERIM CORRELATED RATIONALIZABILITY IN INFINITE GAMES JONATHAN WEINSTEIN AND MUHAMET YILDIZ A. We show that, under the usual continuity and compactness assumptions, interim correlated rationalizability
More informationSidney I. Resnick. A Probability Path. Birkhauser Boston Basel Berlin
Sidney I. Resnick A Probability Path Birkhauser Boston Basel Berlin Preface xi 1 Sets and Events 1 1.1 Introduction 1 1.2 Basic Set Theory 2 1.2.1 Indicator functions 5 1.3 Limits of Sets 6 1.4 Monotone
More informationCONVERGENCE OF OPTION REWARDS FOR MARKOV TYPE PRICE PROCESSES MODULATED BY STOCHASTIC INDICES
CONVERGENCE OF OPTION REWARDS FOR MARKOV TYPE PRICE PROCESSES MODULATED BY STOCHASTIC INDICES D. S. SILVESTROV, H. JÖNSSON, AND F. STENBERG Abstract. A general price process represented by a two-component
More informationAn Introduction to Stochastic Calculus
An Introduction to Stochastic Calculus Haijun Li lih@math.wsu.edu Department of Mathematics Washington State University Week 2-3 Haijun Li An Introduction to Stochastic Calculus Week 2-3 1 / 24 Outline
More informationCHOICE THEORY, UTILITY FUNCTIONS AND RISK AVERSION
CHOICE THEORY, UTILITY FUNCTIONS AND RISK AVERSION Szabolcs Sebestyén szabolcs.sebestyen@iscte.pt Master in Finance INVESTMENTS Sebestyén (ISCTE-IUL) Choice Theory Investments 1 / 65 Outline 1 An Introduction
More informationFE 5204 Stochastic Differential Equations
Instructor: Jim Zhu e-mail:zhu@wmich.edu http://homepages.wmich.edu/ zhu/ January 13, 2009 Stochastic differential equations deal with continuous random processes. They are idealization of discrete stochastic
More informationStability in geometric & functional inequalities
Stability in geometric & functional inequalities A. Figalli The University of Texas at Austin www.ma.utexas.edu/users/figalli/ Alessio Figalli (UT Austin) Stability in geom. & funct. ineq. Krakow, July
More informationX i = 124 MARTINGALES
124 MARTINGALES 5.4. Optimal Sampling Theorem (OST). First I stated it a little vaguely: Theorem 5.12. Suppose that (1) T is a stopping time (2) M n is a martingale wrt the filtration F n (3) certain other
More informationIntroduction to Stochastic Calculus With Applications
Introduction to Stochastic Calculus With Applications Fima C Klebaner University of Melbourne \ Imperial College Press Contents Preliminaries From Calculus 1 1.1 Continuous and Differentiable Functions.
More informationarxiv: v1 [cs.lg] 21 May 2011
Calibration with Changing Checking Rules and Its Application to Short-Term Trading Vladimir Trunov and Vladimir V yugin arxiv:1105.4272v1 [cs.lg] 21 May 2011 Institute for Information Transmission Problems,
More informationOutline Brownian Process Continuity of Sample Paths Differentiability of Sample Paths Simulating Sample Paths Hitting times and Maximum
Normal Distribution and Brownian Process Page 1 Outline Brownian Process Continuity of Sample Paths Differentiability of Sample Paths Simulating Sample Paths Hitting times and Maximum Searching for a Continuous-time
More informationConvergence of Discretized Stochastic (Interest Rate) Processes with Stochastic Drift Term.
Convergence of Discretized Stochastic (Interest Rate) Processes with Stochastic Drift Term. G. Deelstra F. Delbaen Free University of Brussels, Department of Mathematics, Pleinlaan 2, B-15 Brussels, Belgium
More informationSTAT/MATH 395 PROBABILITY II
STAT/MATH 395 PROBABILITY II Distribution of Random Samples & Limit Theorems Néhémy Lim University of Washington Winter 2017 Outline Distribution of i.i.d. Samples Convergence of random variables The Laws
More informationHedging under arbitrage
Hedging under arbitrage Johannes Ruf Columbia University, Department of Statistics AnStAp10 August 12, 2010 Motivation Usually, there are several trading strategies at one s disposal to obtain a given
More informationS t d with probability (1 p), where
Stochastic Calculus Week 3 Topics: Towards Black-Scholes Stochastic Processes Brownian Motion Conditional Expectations Continuous-time Martingales Towards Black Scholes Suppose again that S t+δt equals
More informationMinimal Variance Hedging in Large Financial Markets: random fields approach
Minimal Variance Hedging in Large Financial Markets: random fields approach Giulia Di Nunno Third AMaMeF Conference: Advances in Mathematical Finance Pitesti, May 5-1 28 based on a work in progress with
More informationProbability. An intro for calculus students P= Figure 1: A normal integral
Probability An intro for calculus students.8.6.4.2 P=.87 2 3 4 Figure : A normal integral Suppose we flip a coin 2 times; what is the probability that we get more than 2 heads? Suppose we roll a six-sided
More informationStochastic Calculus, Application of Real Analysis in Finance
, Application of Real Analysis in Finance Workshop for Young Mathematicians in Korea Seungkyu Lee Pohang University of Science and Technology August 4th, 2010 Contents 1 BINOMIAL ASSET PRICING MODEL Contents
More informationMath489/889 Stochastic Processes and Advanced Mathematical Finance Homework 5
Math489/889 Stochastic Processes and Advanced Mathematical Finance Homework 5 Steve Dunbar Due Fri, October 9, 7. Calculate the m.g.f. of the random variable with uniform distribution on [, ] and then
More informationRandom Variables Handout. Xavier Vilà
Random Variables Handout Xavier Vilà Course 2004-2005 1 Discrete Random Variables. 1.1 Introduction 1.1.1 Definition of Random Variable A random variable X is a function that maps each possible outcome
More informationOptimal Stopping. Nick Hay (presentation follows Thomas Ferguson s Optimal Stopping and Applications) November 6, 2008
(presentation follows Thomas Ferguson s and Applications) November 6, 2008 1 / 35 Contents: Introduction Problems Markov Models Monotone Stopping Problems Summary 2 / 35 The Secretary problem You have
More informationbased on two joint papers with Sara Biagini Scuola Normale Superiore di Pisa, Università degli Studi di Perugia
Marco Frittelli Università degli Studi di Firenze Winter School on Mathematical Finance January 24, 2005 Lunteren. On Utility Maximization in Incomplete Markets. based on two joint papers with Sara Biagini
More informationSimulation Wrap-up, Statistics COS 323
Simulation Wrap-up, Statistics COS 323 Today Simulation Re-cap Statistics Variance and confidence intervals for simulations Simulation wrap-up FYI: No class or office hours Thursday Simulation wrap-up
More informationTheoretical Statistics. Lecture 4. Peter Bartlett
1. Concentration inequalities. Theoretical Statistics. Lecture 4. Peter Bartlett 1 Outline of today s lecture We have been looking at deviation inequalities, i.e., bounds on tail probabilities likep(x
More informationRough paths methods 2: Young integration
Rough paths methods 2: Young integration Samy Tindel Purdue University University of Aarhus 2016 Samy T. (Purdue) Rough Paths 2 Aarhus 2016 1 / 75 Outline 1 Some basic properties of fbm 2 Simple Young
More informationYao s Minimax Principle
Complexity of algorithms The complexity of an algorithm is usually measured with respect to the size of the input, where size may for example refer to the length of a binary word describing the input,
More informationLogarithmic derivatives of densities for jump processes
Logarithmic derivatives of densities for jump processes Atsushi AKEUCHI Osaka City University (JAPAN) June 3, 29 City University of Hong Kong Workshop on Stochastic Analysis and Finance (June 29 - July
More informationStochastic Dynamical Systems and SDE s. An Informal Introduction
Stochastic Dynamical Systems and SDE s An Informal Introduction Olav Kallenberg Graduate Student Seminar, April 18, 2012 1 / 33 2 / 33 Simple recursion: Deterministic system, discrete time x n+1 = f (x
More informationBROWNIAN MOTION II. D.Majumdar
BROWNIAN MOTION II D.Majumdar DEFINITION Let (Ω, F, P) be a probability space. For each ω Ω, suppose there is a continuous function W(t) of t 0 that satisfies W(0) = 0 and that depends on ω. Then W(t),
More informationValue of Flexibility in Managing R&D Projects Revisited
Value of Flexibility in Managing R&D Projects Revisited Leonardo P. Santiago & Pirooz Vakili November 2004 Abstract In this paper we consider the question of whether an increase in uncertainty increases
More informationIEOR E4703: Monte-Carlo Simulation
IEOR E4703: Monte-Carlo Simulation Other Miscellaneous Topics and Applications of Monte-Carlo Martin Haugh Department of Industrial Engineering and Operations Research Columbia University Email: martin.b.haugh@gmail.com
More informationAn Introduction to Stochastic Calculus
An Introduction to Stochastic Calculus Haijun Li lih@math.wsu.edu Department of Mathematics Washington State University Week 5 Haijun Li An Introduction to Stochastic Calculus Week 5 1 / 20 Outline 1 Martingales
More informationForecast Horizons for Production Planning with Stochastic Demand
Forecast Horizons for Production Planning with Stochastic Demand Alfredo Garcia and Robert L. Smith Department of Industrial and Operations Engineering Universityof Michigan, Ann Arbor MI 48109 December
More informationUnderstanding Deep Learning Requires Rethinking Generalization
Understanding Deep Learning Requires Rethinking Generalization ChiyuanZhang 1 Samy Bengio 3 Moritz Hardt 3 Benjamin Recht 2 Oriol Vinyals 4 1 Massachusetts Institute of Technology 2 University of California,
More informationCredit Risk in Lévy Libor Modeling: Rating Based Approach
Credit Risk in Lévy Libor Modeling: Rating Based Approach Zorana Grbac Department of Math. Stochastics, University of Freiburg Joint work with Ernst Eberlein Croatian Quants Day University of Zagreb, 9th
More informationThe Game-Theoretic Framework for Probability
11th IPMU International Conference The Game-Theoretic Framework for Probability Glenn Shafer July 5, 2006 Part I. A new mathematical foundation for probability theory. Game theory replaces measure theory.
More informationLecture 14: Examples of Martingales and Azuma s Inequality. Concentration
Lecture 14: Examples of Martingales and Azuma s Inequality A Short Summary of Bounds I Chernoff (First Bound). Let X be a random variable over {0, 1} such that P [X = 1] = p and P [X = 0] = 1 p. n P X
More informationRandom Time Change with Some Applications. Amy Peterson
Random Time Change with Some Applications by Amy Peterson A thesis submitted to the Graduate Faculty of Auburn University in partial fulfillment of the requirements for the Degree of Master of Science
More informationVersion A. Problem 1. Let X be the continuous random variable defined by the following pdf: 1 x/2 when 0 x 2, f(x) = 0 otherwise.
Math 224 Q Exam 3A Fall 217 Tues Dec 12 Version A Problem 1. Let X be the continuous random variable defined by the following pdf: { 1 x/2 when x 2, f(x) otherwise. (a) Compute the mean µ E[X]. E[X] x
More informationWeak Convergence to Stochastic Integrals
Weak Convergence to Stochastic Integrals Zhengyan Lin Zhejiang University Join work with Hanchao Wang Outline 1 Introduction 2 Convergence to Stochastic Integral Driven by Brownian Motion 3 Convergence
More informationBasic Data Analysis. Stephen Turnbull Business Administration and Public Policy Lecture 4: May 2, Abstract
Basic Data Analysis Stephen Turnbull Business Administration and Public Policy Lecture 4: May 2, 2013 Abstract Introduct the normal distribution. Introduce basic notions of uncertainty, probability, events,
More informationA note on the existence of unique equivalent martingale measures in a Markovian setting
Finance Stochast. 1, 251 257 1997 c Springer-Verlag 1997 A note on the existence of unique equivalent martingale measures in a Markovian setting Tina Hviid Rydberg University of Aarhus, Department of Theoretical
More informationNumerical valuation for option pricing under jump-diffusion models by finite differences
Numerical valuation for option pricing under jump-diffusion models by finite differences YongHoon Kwon Younhee Lee Department of Mathematics Pohang University of Science and Technology June 23, 2010 Table
More information5.7 Probability Distributions and Variance
160 CHAPTER 5. PROBABILITY 5.7 Probability Distributions and Variance 5.7.1 Distributions of random variables We have given meaning to the phrase expected value. For example, if we flip a coin 100 times,
More informationAmerican Option Pricing Formula for Uncertain Financial Market
American Option Pricing Formula for Uncertain Financial Market Xiaowei Chen Uncertainty Theory Laboratory, Department of Mathematical Sciences Tsinghua University, Beijing 184, China chenxw7@mailstsinghuaeducn
More information3 Arbitrage pricing theory in discrete time.
3 Arbitrage pricing theory in discrete time. Orientation. In the examples studied in Chapter 1, we worked with a single period model and Gaussian returns; in this Chapter, we shall drop these assumptions
More informationProblem Set 3. Thomas Philippon. April 19, Human Wealth, Financial Wealth and Consumption
Problem Set 3 Thomas Philippon April 19, 2002 1 Human Wealth, Financial Wealth and Consumption The goal of the question is to derive the formulas on p13 of Topic 2. This is a partial equilibrium analysis
More informationChange of Measure (Cameron-Martin-Girsanov Theorem)
Change of Measure Cameron-Martin-Girsanov Theorem Radon-Nikodym derivative: Taking again our intuition from the discrete world, we know that, in the context of option pricing, we need to price the claim
More informationA Note on the No Arbitrage Condition for International Financial Markets
A Note on the No Arbitrage Condition for International Financial Markets FREDDY DELBAEN 1 Department of Mathematics Vrije Universiteit Brussel and HIROSHI SHIRAKAWA 2 Department of Industrial and Systems
More informationThe ruin probabilities of a multidimensional perturbed risk model
MATHEMATICAL COMMUNICATIONS 231 Math. Commun. 18(2013, 231 239 The ruin probabilities of a multidimensional perturbed risk model Tatjana Slijepčević-Manger 1, 1 Faculty of Civil Engineering, University
More informationHedging under Arbitrage
Hedging under Arbitrage Johannes Ruf Columbia University, Department of Statistics Modeling and Managing Financial Risks January 12, 2011 Motivation Given: a frictionless market of stocks with continuous
More informationPath-properties of the tree-valued Fleming-Viot process
Path-properties of the tree-valued Fleming-Viot process Peter Pfaffelhuber Joint work with Andrej Depperschmidt and Andreas Greven Luminy, 492012 The Moran model time t As every population model, the Moran
More informationINTERIM CORRELATED RATIONALIZABILITY IN INFINITE GAMES
INTERIM CORRELATED RATIONALIZABILITY IN INFINITE GAMES JONATHAN WEINSTEIN AND MUHAMET YILDIZ A. In a Bayesian game, assume that the type space is a complete, separable metric space, the action space is
More informationLimit Theorems for the Empirical Distribution Function of Scaled Increments of Itô Semimartingales at high frequencies
Limit Theorems for the Empirical Distribution Function of Scaled Increments of Itô Semimartingales at high frequencies George Tauchen Duke University Viktor Todorov Northwestern University 2013 Motivation
More informationModes of Convergence
Moes of Convergence Electrical Engineering 126 (UC Berkeley Spring 2018 There is only one sense in which a sequence of real numbers (a n n N is sai to converge to a limit. Namely, a n a if for every ε
More informationIntroduction to Stochastic Calculus
Introduction to Stochastic Calculus Director Chennai Mathematical Institute rlk@cmi.ac.in rkarandikar@gmail.com Introduction to Stochastic Calculus - 1 A Game Consider a gambling house. A fair coin is
More informationQuasi-Monte Carlo for Finance
Quasi-Monte Carlo for Finance Peter Kritzer Johann Radon Institute for Computational and Applied Mathematics (RICAM) Austrian Academy of Sciences Linz, Austria NCTS, Taipei, November 2016 Peter Kritzer
More informationSchnorr trivial sets and truth-table reducibility
Schnorr trivial sets and truth-table reducibility Johanna N. Y. Franklin and Frank Stephan Abstract In this paper, we give several characterizations of Schnorr trivial sets, including a new lowness notion
More informationHedging of Contingent Claims under Incomplete Information
Projektbereich B Discussion Paper No. B 166 Hedging of Contingent Claims under Incomplete Information by Hans Föllmer ) Martin Schweizer ) October 199 ) Financial support by Deutsche Forschungsgemeinschaft,
More informationNon-semimartingales in finance
Non-semimartingales in finance Pricing and Hedging Options with Quadratic Variation Tommi Sottinen University of Vaasa 1st Northern Triangular Seminar 9-11 March 2009, Helsinki University of Technology
More informationOn the Lower Arbitrage Bound of American Contingent Claims
On the Lower Arbitrage Bound of American Contingent Claims Beatrice Acciaio Gregor Svindland December 2011 Abstract We prove that in a discrete-time market model the lower arbitrage bound of an American
More informationExponential martingales and the UI martingale property
u n i v e r s i t y o f c o p e n h a g e n d e p a r t m e n t o f m a t h e m a t i c a l s c i e n c e s Faculty of Science Exponential martingales and the UI martingale property Alexander Sokol Department
More informationEuropean Contingent Claims
European Contingent Claims Seminar: Financial Modelling in Life Insurance organized by Dr. Nikolic and Dr. Meyhöfer Zhiwen Ning 13.05.2016 Zhiwen Ning European Contingent Claims 13.05.2016 1 / 23 outline
More informationMESURES DE RISQUE DYNAMIQUES DYNAMIC RISK MEASURES
from BMO martingales MESURES DE RISQUE DYNAMIQUES DYNAMIC RISK MEASURES CNRS - CMAP Ecole Polytechnique March 1, 2007 1/ 45 OUTLINE from BMO martingales 1 INTRODUCTION 2 DYNAMIC RISK MEASURES Time Consistency
More informationPARTITIONS OF 2 ω AND COMPLETELY ULTRAMETRIZABLE SPACES
PARTITIONS OF 2 ω AND COMPLETELY ULTRAMETRIZABLE SPACES WILLIAM R. BRIAN AND ARNOLD W. MILLER Abstract. We prove that, for every n, the topological space ω ω n (where ω n has the discrete topology) can
More informationThe Stigler-Luckock model with market makers
Prague, January 7th, 2017. Order book Nowadays, demand and supply is often realized by electronic trading systems storing the information in databases. Traders with access to these databases quote their
More informationLecture 4. Finite difference and finite element methods
Finite difference and finite element methods Lecture 4 Outline Black-Scholes equation From expectation to PDE Goal: compute the value of European option with payoff g which is the conditional expectation
More informationOn the pricing equations in local / stochastic volatility models
On the pricing equations in local / stochastic volatility models Hao Xing Fields Institute/Boston University joint work with Erhan Bayraktar, University of Michigan Kostas Kardaras, Boston University Probability
More informationHedging of Contingent Claims in Incomplete Markets
STAT25 Project Report Spring 22 Hedging of Contingent Claims in Incomplete Markets XuanLong Nguyen Email: xuanlong@cs.berkeley.edu 1 Introduction This report surveys important results in the literature
More informationGame Theory: Normal Form Games
Game Theory: Normal Form Games Michael Levet June 23, 2016 1 Introduction Game Theory is a mathematical field that studies how rational agents make decisions in both competitive and cooperative situations.
More information