DOCSLIB.ORG

5: FILTRATIONS and CONDITIONING [.03In]

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
5: FILTRATIONS and CONDITIONING [.03In] 5: FILTRATIONS AND CONDITIONING Marek Rutkowski School of Mathematics and Statistics University of Sydney Semester 2, 2016 M. Rutkowski (USydney) Slides 5: Filtrations and Conditioning 1 / 37 New Features In the case of multi-period market models, we need to deal with two important new features. First, the investors can trade in assets at any specific date t ∈ {0, 1, 2,...,T } where T is the horizon date. Second, the investors can gather information over time, since the fluctuations of asset prices can be observed. We will need to introduce the concept of a self-financing trading strategy. We have to determine how the level of information available to investors evolves over time. The latter aspect leads to the probabilistic concepts of σ-fields and filtrations. M. Rutkowski (USydney) Slides 5: Filtrations and Conditioning 2 / 37 Outline We will examine the following issues: 1 Partitions and σ-fields. 2 Filtrations and adapted stochastic processes. 3 Conditional expectation with respect to a partition or a σ-field. 4 Change of a probability measure and the Radon-Nikodym density. M. Rutkowski (USydney) Slides 5: Filtrations and Conditioning 3 / 37 PART 1 PARTITIONS AND σ-FIELDS M. Rutkowski (USydney) Slides 5: Filtrations and Conditioning 4 / 37 σ-Field The concept of a σ-field can be used to describe the amount of information available at a given moment. Let N = {1, 2,... } be the set of all natural numbers. Definition (σ-Field) A collection F of subsets of Ω is called a σ-field (or a σ-algebra) whenever: 1 Ω ∈ F, c 2 if A ∈ F then A := Ω \ A ∈ F, ∞ 3 if Ai ∈ F for all i ∈ N then i=1 Ai ∈ F. M. Rutkowski (USydney) Slides 5: Filtrations and Conditioning 5 / 37 Interpretation of a σ-Field The set of information has to contain all possible states, so that we postulate that Ω belongs to any σ-field. Any set A ∈ F is interpreted as an observed event. If an event A ∈ F is given, that is, some collection of states is given, then the remaining states can also be identified and thus the complement Ac is also an event. The idea of a σ-field is to model a certain level of information. In particular, as the σ-field becomes larger, more and more events can be identified. We will later introduce a concept of an increasing flow of information, formally represented by an ordered (increasing) family of σ-fields. M. Rutkowski (USydney) Slides 5: Filtrations and Conditioning 6 / 37 Probability Measure Definition (Probability Measure) A map P : F → [0, 1] is called a probability measure if: 1 P (Ω) = 1, 2 for any sequence Ai, i ∈ N of pairwise disjoint events we have P Ai = P (Ai) . i∈N i∈N The triplet (Ω, F, P) is called a probability space. By convention, the probability of all possibilities is 1 (see 1). Probability should satisfy σ-additivity (see 2) Note that P (∅) = 0 and for an arbitrary event A ∈ F we have P (Ac) = 1 − P (A). M. Rutkowski (USydney) Slides 5: Filtrations and Conditioning 7 / 37 Example: σ-Fields Example (5.1) We take Ω= {ω1,ω2,ω3,ω4} and we define the σ-fields: F1 = {∅, Ω} F2 = ∅, Ω, {ω1,ω2} , {ω3} , {ω4} , {ω1,ω2,ω3} , {ω1,ω2,ω4} , {ω3,ω4} Ω F3 = 2 (the class of all subsets of Ω). Note that F1 ⊂F2 ⊂F3, that is, the information increases: F1: no information, except for the set Ω. F2: partial information, since we cannot distinguish between the occurrence of either ω1 or ω2. F3: full information, since {ω1}, {ω2}, {ω3} and {ω4} can be observed. M. Rutkowski (USydney) Slides 5: Filtrations and Conditioning 8 / 37 Example: Probability Measure Example (5.1 Continued) We define the probability measure P on the σ-field F2 2 1 1 P ({ω1,ω2})= , P ({ω3})= , P ({ω4})= . 3 6 6 The σ-additivity of P leads to P ({ω1,ω2}∪{ω3}∪{ω4})=1= P (Ω) . Ω Note that P is not yet defined on the σ-field F3 =2 and in fact the extension of P from F2 to F3 is not unique. For any α ∈ [0, 2/3] we may set 2 P ({ω1})= α = − P ({ω2}) . α 3 α M. Rutkowski (USydney) Slides 5: Filtrations and Conditioning 9 / 37 Partitions Definition Let I be some index set. Assume that we are given a collection (Bi)i∈I of subsets of Ω. Then the smallest σ-field containing this collection is denoted by σ ((Bi)i∈I ) and is called the σ-field generated by (Bi)i∈I . Definition (Partition) By a partition of Ω, we mean any collection P = (Ai)i∈I of non-empty subsets of Ω such that the sets Ai are pairwise disjoint, that is, Ai ∩ Aj = ∅ whenever i = j and i∈I Ai =Ω. Lemma A partition P = (Ai)i∈I generates a σ-field F if every set A ∈ F can be represented as follows: A = j∈J Aj for some subset J ⊂ I. M. Rutkowski (USydney) Slides 5: Filtrations and Conditioning 10 / 37 Partition Associated with a σ-Field Definition (Partition Associated with F) A partition of Ω associated with a σ-field F is a collection of non-empty sets Ai ∈ F for some i ∈ I such that 1 Ω= i∈I Ai. 2 The sets Ai are pairwise disjoint, i.e., Ai ∩ Aj = ∅ for i = j. 3 For each A ∈ F there exists J ⊆ I such that A = i∈J Ai. Lemma For any σ-field F of subsets of a finite state space Ω, a partition associated with this σ-field always exists and is unique. M. Rutkowski (USydney) Slides 5: Filtrations and Conditioning 11 / 37 Partition Associated with a σ-Field Further properties of partitions: If Ω is countable then for any σ-field F there exists a unique partition P of Ω associated with F. It is also clear that this partition generates F, so that F = σ(P). The sets Ai in a partition must be smallest, specifically, if F = σ(P) and A ∈ F is such that A ⊆ Ai then A = Ai. The probability of any A ∈ F equals the sum of probabilities of Ais in the partition generating F, specifically, A = Ai ⇒ P (A)= P (Ai) . i∈J i∈J M. Rutkowski (USydney) Slides 5: Filtrations and Conditioning 12 / 37 Example: Partition Associated with a σ-Field Example (5.2) Consider the σ-field F2 introduced in Example 5.1. The unique partition associated with F2 is given by P2 = {ω1,ω2} , {ω3} , {ω4} . Define the probabilities 2 1 1 P ({ω ,ω })= , P ({ω })= , P ({ω })= . 1 2 3 3 6 4 6 Then for each event A ∈ F2 the probability of A can be easily evaluated, for instance 5 P ({ω ,ω ,ω })= P ({ω ,ω })+ P ({ω })= . 1 2 4 1 2 4 6 M. Rutkowski (USydney) Slides 5: Filtrations and Conditioning 13 / 37 Random Variable Let F be an arbitrary σ-field of subsets of Ω. In the next definition, we do not assume that the sample space is discrete. Definition (F-Measurability) A map X :Ω → R is said to be F-measurable if for every closed interval [a, b] ⊂ R the preimage (i.e. the inverse image) under X belongs to F, that is, X−1([a, b]) := {ω ∈ Ω | X (ω) ∈ [a, b]} ∈ F. Equivalently, for any real number x X−1((−∞,x]) := {ω ∈ Ω | X (ω) ≤ x} ∈ F. If X is F-measurable then X is called a random variable on (Ω, F). M. Rutkowski (USydney) Slides 5: Filtrations and Conditioning 14 / 37 Partition and Random Variable Proposition (5.1) Let X :Ω → R be an arbitrary function. Let F be a σ-field and P = (Ai)i∈I be the unique partition associated with F. Then X is F-measurable if and only if for each Ai ∈ P there exists a constant ci such that X maps Ai to {ci}, that is, X (ω)= ci for all ω ∈ Fi. Proof of Proposition 5.1 (⇒). (⇒) Assume that (Ai)i∈I is a partition of the σ-field F and that X is F-measurable. Let j ∈ I be an index and let an element ω ∈ Aj be arbitrary. Define cj := X(ω). We wish to show that X(ω)= cj for all −1 ω ∈ Aj. Since X is F-measurable, X (cj) ∈ F. M. Rutkowski (USydney) Slides 5: Filtrations and Conditioning 15 / 37 Proof of Proposition 5.1 Proof of Proposition 5.1 (⇒). By properties 2. and 3. in the definition of a σ-field, we have −1 ∅ = X (cj) ∩ Aj ∈ F. It is obvious that the inclusion −1 X (cj) ∩ Aj ⊂ Aj holds. Therefore, from the aforementioned minimality property of the sets contained in the partition, we obtain −1 X (cj ) ∩ Aj = Aj. But this means that X(ω)= cj for all ω ∈ Aj and thus X is constant on Aj. By varying j, we obtain such cjs for all j ∈ I. M. Rutkowski (USydney) Slides 5: Filtrations and Conditioning 16 / 37 Proof of Proposition 5.1 Proof of Proposition 5.1 (⇐). (⇐) Assume now that X :Ω → R is a function, which is constant on all sets Aj belonging to the partition and that the cj are given as in the statement of the proposition. Let [a, b] be a closed interval in R. We define C := cj | j ∈ I and cj ∈ [a, b] . Since i∈I Ai =Ω no other elements then the cj occur as values of X. Therefore, −1 −1 −1 X ([a, b]) = X (C)= X (cj)= Aj ∈ F j |cj ∈C j |cj ∈C where the last equality holds by property 3.
Load more

Recommended publications
  • Notes on Elementary Martingale Theory 1 Conditional Expectations
    . Notes on Elementary Martingale Theory by John B. Walsh 1 Conditional Expectations 1.1 Motivation Probability is a measure of ignorance. When new information decreases that ignorance, it changes our probabilities. Suppose we roll a pair of dice, but don't look immediately at the outcome. The result is there for anyone to see, but if we haven't yet looked, as far as we are concerned, the probability that a two (\snake eyes") is showing is the same as it was before we rolled the dice, 1/36. Now suppose that we happen to see that one of the two dice shows a one, but we do not see the second. We reason that we have rolled a two if|and only if|the unseen die is a one. This has probability 1/6. The extra information has changed the probability that our roll is a two from 1/36 to 1/6. It has given us a new probability, which we call a conditional probability. In general, if A and B are events, we say the conditional probability that B occurs given that A occurs is the conditional probability of B given A. This is given by the well-known formula P A B (1) P B A = f \ g; f j g P A f g providing P A > 0. (Just to keep ourselves out of trouble if we need to apply this to a set f g of probability zero, we make the convention that P B A = 0 if P A = 0.) Conditional probabilities are familiar, but that doesn't stop themf fromj g giving risef tog many of the most puzzling paradoxes in probability.
    [Show full text]
  • The Theory of Filtrations of Subalgebras, Standardness and Independence
    The theory of filtrations of subalgebras, standardness and independence Anatoly M. Vershik∗ 24.01.2017 In memory of my friend Kolya K. (1933{2014) \Independence is the best quality, the best word in all languages." J. Brodsky. From a personal letter. Abstract The survey is devoted to the combinatorial and metric theory of fil- trations, i. e., decreasing sequences of σ-algebras in measure spaces or decreasing sequences of subalgebras of certain algebras. One of the key notions, that of standardness, plays the role of a generalization of the no- tion of the independence of a sequence of random variables. We discuss the possibility of obtaining a classification of filtrations, their invariants, and various links to problems in algebra, dynamics, and combinatorics. Bibliography: 101 titles. Contents 1 Introduction 3 1.1 A simple example and a difficult question . .3 1.2 Three languages of measure theory . .5 1.3 Where do filtrations appear? . .6 1.4 Finite and infinite in classification problems; standardness as a generalization of independence . .9 arXiv:1705.06619v1 [math.DS] 18 May 2017 1.5 A summary of the paper . 12 2 The definition of filtrations in measure spaces 13 2.1 Measure theory: a Lebesgue space, basic facts . 13 2.2 Measurable partitions, filtrations . 14 2.3 Classification of measurable partitions . 16 ∗St. Petersburg Department of Steklov Mathematical Institute; St. Petersburg State Uni- versity Institute for Information Transmission Problems. E-mail: [email protected]. Par- tially supported by the Russian Science Foundation (grant No. 14-11-00581). 1 2.4 Classification of finite filtrations . 17 2.5 Filtrations we consider and how one can define them .
    [Show full text]
  • An Application of Probability Theory in Finance: the Black-Scholes Formula
    AN APPLICATION OF PROBABILITY THEORY IN FINANCE: THE BLACK-SCHOLES FORMULA EFFY FANG Abstract. In this paper, we start from the building blocks of probability the- ory, including σ-field and measurable functions, and then proceed to a formal introduction of probability theory. Later, we introduce stochastic processes, martingales and Wiener processes in particular. Lastly, we present an appli- cation - the Black-Scholes Formula, a model used to price options in financial markets. Contents 1. The Building Blocks 1 2. Probability 4 3. Martingales 7 4. Wiener Processes 8 5. The Black-Scholes Formula 9 References 14 1. The Building Blocks Consider the set Ω of all possible outcomes of a random process. When the set is small, for example, the outcomes of tossing a coin, we can analyze case by case. However, when the set gets larger, we would need to study the collections of subsets of Ω that may be taken as a suitable set of events. We define such a collection as a σ-field. Definition 1.1. A σ-field on a set Ω is a collection F of subsets of Ω which obeys the following rules or axioms: (a) Ω 2 F (b) if A 2 F, then Ac 2 F 1 S1 (c) if fAngn=1 is a sequence in F, then n=1 An 2 F The pair (Ω; F) is called a measurable space. Proposition 1.2. Let (Ω; F) be a measurable space. Then (1) ; 2 F k SK (2) if fAngn=1 is a finite sequence of F-measurable sets, then n=1 An 2 F 1 T1 (3) if fAngn=1 is a sequence of F-measurable sets, then n=1 An 2 F Date: August 28,2015.
    [Show full text]
  • Mathematical Finance an Introduction in Discrete Time
    Jan Kallsen Mathematical Finance An introduction in discrete time CAU zu Kiel, WS 13/14, February 10, 2014 Contents 0 Some notions from stochastics 4 0.1 Basics . .4 0.1.1 Probability spaces . .4 0.1.2 Random variables . .5 0.1.3 Conditional probabilities and expectations . .6 0.2 Absolute continuity and equivalence . .7 0.3 Conditional expectation . .8 0.3.1 σ-fields . .8 0.3.2 Conditional expectation relative to a σ-field . 10 1 Discrete stochastic calculus 16 1.1 Stochastic processes . 16 1.2 Martingales . 18 1.3 Stochastic integral . 20 1.4 Intuitive summary of some notions from this chapter . 27 2 Modelling financial markets 36 2.1 Assets and trading strategies . 36 2.2 Arbitrage . 39 2.3 Dividend payments . 41 2.4 Concrete models . 43 3 Derivative pricing and hedging 48 3.1 Contingent claims . 49 3.2 Liquidly traded derivatives . 50 3.3 Individual perspective . 54 3.4 Examples . 56 3.4.1 Forward contract . 56 3.4.2 Futures contract . 57 3.4.3 European call and put options in the binomial model . 57 3.4.4 European call and put options in the standard model . 61 3.5 American options . 63 2 CONTENTS 3 4 Incomplete markets 70 4.1 Martingale modelling . 70 4.2 Variance-optimal hedging . 72 5 Portfolio optimization 73 5.1 Maximizing expected utility of terminal wealth . 73 6 Elements of continuous-time finance 80 6.1 Continuous-time theory . 80 6.2 Black-Scholes model . 84 Chapter 0 Some notions from stochastics 0.1 Basics 0.1.1 Probability spaces This course requires a decent background in stochastics which cannot be provided here.
    [Show full text]
  • 4 Martingales in Discrete-Time
    4 Martingales in Discrete-Time Suppose that (Ω, F, P) is a probability space. Definition 4.1. A sequence F = {Fn, n = 0, 1,...} is called a filtration if each Fn is a sub-σ-algebra of F, and Fn ⊆ Fn+1 for n = 0, 1, 2,.... In other words, a filtration is an increasing sequence of sub-σ-algebras of F. For nota- tional convenience, we define ∞ ! [ F∞ = σ Fn n=1 and note that F∞ ⊆ F. Definition 4.2. If F = {Fn, n = 0, 1,...} is a filtration, and X = {Xn, n = 0, 1,...} is a discrete-time stochastic process, then X is said to be adapted to F if Xn is Fn-measurable for each n. Note that by definition every process {Xn, n = 0, 1,...} is adapted to its natural filtration {Fn, n = 0, 1,...} where Fn = σ(X0,...,Xn). The intuition behind the idea of a filtration is as follows. At time n, all of the information about ω ∈ Ω that is known to us at time n is contained in Fn. As n increases, our knowledge of ω also increases (or, if you prefer, does not decrease) and this is captured in the fact that the filtration consists of an increasing sequence of sub-σ-algebras. Definition 4.3. A discrete-time stochastic process X = {Xn, n = 0, 1,...} is called a martingale (with respect to the filtration F = {Fn, n = 0, 1,...}) if for each n = 0, 1, 2,..., (a) X is adapted to F; that is, Xn is Fn-measurable, 1 (b) Xn is in L ; that is, E|Xn| < ∞, and (c) E(Xn+1|Fn) = Xn a.s.
    [Show full text]
  • MTH 664 Lectures 24 - 27
    MTH 664 0 MTH 664 Lectures 24 - 27 Yevgeniy Kovchegov Oregon State University MTH 664 1 Topics: • Martingales. • Filtration. Stopping times. • Probability harmonic functions. • Optional Stopping Theorem. • Martingale Convergence Theorem. MTH 664 2 Conditional expectation. Consider a probability space (Ω, F,P ) and a random variable X ∈ F. Let G ⊆ F be a smaller σ-algebra. Definition. Conditional expectation E[X|G] is a unique func- tion from Ω to R satisfying: 1. E[X|G] is G-measurable 2. R E[X|G] dP (ω) = R X dP (ω) for all A ∈ G A A The existence and uniqueness of E[X|G] comes from the Radon-Nikodym theorem. Lemma. If X ∈ G, Y (ω) ∈ L1(Ω,P ), and X(ω) · Y (ω) ∈ L1(Ω,P ), then E[X · Y |G] = X · E[Y |G] MTH 664 3 Conditional expectation. Consider a probability space (Ω, F,P ) and a random variable X ∈ F. Lemma. If G ⊆ F, then EE[X|G] = E[X] Let G1 ⊆ G2 ⊆ F be smaller sub-σ-algebras. Lemma. E E[X|G2] | G1 = E[X|G1] Proof. For any A ∈ G1 ⊆ G2, Z Z E E[X|G2] | G1 (ω) dP (ω) = E[X|G2](ω) dP (ω) A A Z Z = X(ω) dP (ω) = E[X|G1](ω) dP (ω) A A MTH 664 4 Filtration. Definition. Consider an arbitrary linear ordered set T :A sequence of sub-σ-algebras {Ft}t∈T of F is said to be a filtration if Fs ⊆ Ft a.s. ∀s < t ∈ T Example. Consider a sequence of random variables X1,X2,..
    [Show full text]
  • An Enlargement of Filtration Formula with Applications to Multiple Non
    Finance and Stochastics manuscript No. (will be inserted by the editor) An enlargement of filtration formula with applications to multiple non-ordered default times Monique Jeanblanc · Libo Li · Shiqi Song Received: date / Accepted: date Abstract In this work, for a reference filtration F, we develop a method for computing the semimartingale decomposition of F-martingales in a specific type of enlargement of F. As an application, we study the progressive en- largement of F with a sequence of non-ordered default times and we show how to deduce results concerning the first-to-default, k-th-to-default, k-out-of- n-to-default or the all-to-default events. In particular, using this method, we compute explicitly the semimartingale decomposition of F-martingales under the absolute continuity condition of Jacod. Keywords Enlargement of filtration · Non-ordered default times · Reduced form credit risk modeling · Semimartingale decomposition JEL classification G14 Mathematics Subject Classification (2010) 60G07 · 91G40 This research benefited from the support of the Chaire Risque de Cr´edit(F´ed´erationBan- caire Fran¸caise)and Labex ANR 11-LABX-0019. Monique Jeanblanc Univ Evry, LaMME, UMR CNRS 8071, Universit´eParis Saclay, 23 boulevard de France, 91037 Evry. E-mail: [email protected] Libo Li ( ) School of Mathematics and Statistics, University of New South Wales, NSW, Australia. E-mail: [email protected] Shiqi Song Univ Evry, LaMME, UMR CNRS 8071, Universit´eParis Saclay, 23 boulevard de France, 91037 Evry. E-mail: [email protected] 2 M. Jeanblanc, L. Li and S. Song 1 Introduction This paper is motivated by the needs to model multiple non-ordered default times in credit risk and to perform explicit computations in the pricing of credit instruments.
    [Show full text]
  • The Comparison Isomorphisms Ccris
    The Comparison Isomorphisms Ccris Fabrizio Andreatta July 9, 2009 Contents 1 Introduction 2 2 The crystalline conjecture with coe±cients 3 3 The complex case. The strategy in the p-adic setting 4 4 The p-adic case; Faltings's topology 5 5 Sheaves of periods 7 i 6 The computation of R v¤Bcris 13 7 The proof of the comparison isomorphism 16 1 1 Introduction Let p > 0 denote a prime integer and let k be a perfect ¯eld of characteristic p. Put OK := W (k) and let K be its fraction ¯eld. Denote by K a ¯xed algebraic closure of K and set GK := Gal(K=K). We write Bcris for the crystalline period ring de¯ned by J.-M. Fontaine in [Fo]. Recall that Bcris is a topological ring, endowed with a continuous action of GK , an ² exhaustive decreasing ¯ltration Fil Bcris and a Frobenius operator '. Following the paper [AI2], these notes aim at presenting a new proof of the so called crys- talline conjecture formulated by J.-M. Fontaine in [Fo]. Let X be a smooth proper scheme, geometrically irreducible, of relative dimension d over OK : Conjecture 1.1 ([Fo]). For every i ¸ 0 there is a canonical and functorial isomorphism com- muting with all the additional structures (namely, ¯ltrations, GK {actions and Frobenii) i et » i H (XK ; Qp) ­Qp Bcris = Hcris(Xk=OK ) ­OK Bcris: i » It follows from a classical theorem for crystalline cohomology, see [BO], that Hcris(Xk=OK ) = i HdR(X=OK ). The latter denotes the hypercohomology of the de Rham complex 0 ¡! OX ¡! ­1 ¡! ­2 ¡! ¢ ¢ ¢ ­d ¡! 0.
    [Show full text]
  • E1-Degeneration of the Irregular Hodge Filtration (With an Appendix by Morihiko Saito)
    E1-DEGENERATION OF THE IRREGULAR HODGE FILTRATION (WITH AN APPENDIX BY MORIHIKO SAITO) HÉLÈNE ESNAULT, CLAUDE SABBAH, AND JENG-DAW YU Abstract. For a regular function f on a smooth complex quasi-projective va- riety, J.-D. Yu introduced in [Yu14] a filtration (the irregular Hodge filtration) on the de Rham complex with twisted differential d+df, extending a definition of Deligne in the case of curves. In this article, we show the degeneration at E1 of the spectral sequence attached to the irregular Hodge filtration, by using the method of [Sab10]. We also make explicit the relation with a complex introduced by M. Kontsevich and give details on his proof of the correspond- ing E1-degeneration, by reduction to characteristic p, when the pole divisor of the function is reduced with normal crossings. In Appendix E, M. Saito gives a different proof of the latter statement with a possibly non reduced normal crossing pole divisor. Contents Introduction 2 1. The irregular Hodge filtration in the normal crossing case 3 1.1. Setup and notation 3 1.2. The irregular Hodge filtration 5 1.3. The Kontsevich complex 6 1.4. Comparison of the filtered twisted meromorphic de Rham complex and the filtered Kontsevich complex 7 1.5. Relation between Theorem 1.2.2 for α = 0 and Theorem 1.3.2 9 1.6. Deligne’s filtration: the D-module approach 11 1.7. Comparison of Yu’s filtration and Deligne’s filtration on the twisted de Rham complex 16 2. Preliminaries to a generalization of Deligne’s filtration 18 2.1.
    [Show full text]
  • LECTURES 3 and 4: MARTINGALES 1. Introduction in an Arbitrage–Free
    LECTURES 3 AND 4: MARTINGALES 1. Introduction In an arbitrage–free market, the share price of any traded asset at time t = 0 is the expected value, under an equilibrium measure, of its discounted price at market termination t = T . We shall see that the discounted share price at any time t ≤ T may also be computed by an expectation; however, this expectation is a conditional expectation, given the information about the market scenario that is revealed up to time t. Thus, the discounted share price process, as a (random) function of time, has the property that its value at any time t is the conditional expectation of the value at a future time given information available at t.A stochastic process with this property is called a martingale. The theory of martingales (initiated by Joseph Doob, following earlier work of Paul Levy´ ) is one of the central themes of modern probability. In discrete-time finance, the importance of martingales stems largely from the Optional Stopping Formula, which (a) places fundamental constraints on (discounted) share price processes, and (b) provides a computational tool of considerable utility. 2. Filtrations of Probability Spaces Filtrations. In a multiperiod market, information about the market scenario is revealed in stages. Some events may be completely determined by the end of the first trading period, others by the end of the second, and others not until the termination of all trading. This suggests the following classification of events: for each t ≤ T , (1) Ft = {all events determined in the first t trading periods}. The finite sequence (Ft)0≤t≤T is a filtration of the space Ω of market scenarios.
    [Show full text]
  • Homomorphisms of Filtered Modules
    HOMOMORPHISMS OF FILTERED MODULES BY ALEX HELLER 1. Introduction. The following simple problem appears to be without a known solution. Let A', A", B', B" be modules and suppose a eExt'04", A'), ß e ExV(B",B'). Then a and ß determine extensions A of A" by A' and B of B" by B'. How may the group Hom(/l,B) be expressed in terms of the four modules and two extension classes? We give here a partial solution of this problem. Hom(A,B) is seen to have a canonical filtration; we compute the associated graded group. Our failure to compute Hom(y4,B) itself is to be thought of as analogous to the failure of the Kiinneth theorem to compute canonically the homology of a product of chain complexes (cf. [2]). This partial solution is a special case of a solution of a more general problem. If A and B are filtered modules then how may tlom(A,B), or rather, its associated graded group, be computed in terms of the associated graded modules A" and B" of A and B, together with the "extension classes" needed to reconstruct A and B from these graded modules? Indeed a similar question arises with respect to all the groups Extq(A,B). We shall see that associated with such data there is a spectral sequence whose term E1 is just Ext(A",B"), and whose term E °° is associated with a filtration of Ext(A,B). The derivation d1 is easily computed. No attempt is made to com- pute the higher derivations in the general case.
    [Show full text]
  • Supports and Filtrations in Algebraic Geometry and Modular Representation Theory
    SUPPORTS AND FILTRATIONS IN ALGEBRAIC GEOMETRY AND MODULAR REPRESENTATION THEORY PAUL BALMER Abstract. Working with tensor triangulated categories, we prove two the- orems relative to supports and then discuss their incarnations in algebraic geometry and in modular representation theory. First, we show that an in- decomposable object has connected support. Then we consider the filtrations by dimension or codimension of the support and prove that the associated subquotients decompose as sums of local terms. Introduction We proved in [1] that schemes X in algebraic geometry and projective support varieties VG(k) in modular representation theory both appear as the spectrum of prime ideals, Spc(K), for suitable tensor triangulated categories K, namely and respectively for K = Dperf (X), the derived category of perfect complexes over X, and for K = stab(kG), the stable category of finitely generated kG-modules modulo projectives. (This is recalled in Theorem 1.4 below.) In this spectrum Spc(K), one can construct for every object a ∈ K a closed subset supp(a) ⊂ Spc(K) called the support of a. This support provides a unified approach to the homological support supph(E) ⊂ X of a perfect complex E ∈ Dperf (X) and to the projective support variety VG(M) ⊂ VG(k) of a kG-module M ∈ stab(kG). Since tensor triangulated categories also appear in topology, motivic theory, KK-theory, and other areas, general results about spectrum and support have a wide potential of application. This level of generality, that we dub tensor triangular geometry, is the one of our main results. The first one is Theorem 2.11, which says : Theorem.
    [Show full text]