Asymptotic arbitrage in large financial markets with friction

In the modern version of arbitrage pricing theory suggested by Kabanov and Kramkov the fundamental financially meaningful concept is an asymptotic arbitrage. The ??real world?? large market is represented by a sequence of ??models?? and, though each of them is arbitrage free, investors may obtain non-risky profits in the limit. Mathematically, absence of the asymptotic arbitrage is expressed as contiguity of envelopes of the sets of equivalent martingale measures and objective probabilities. The classical theory deals with frictionless markets. In the present paper we extend it to markets with transaction costs. Assuming that each model admits consistent price systems, we relate them with families of probability measures and consider their upper and lower envelopes. The main result concerns the necessary and sufficient conditions for absence of asymptotic arbitrage opportunities of the first and second kinds expressed in terms of contiguity. We provide also more specific conditions involving Hellinger processes and give applications to particular models of large financial markets.     

Asymptotic expansion for the probability of large deviations. II

Lithuanian Mathematical Journal -     

Asymptotic behavior of constrained stochastic approximations via the theory of large deviations

Summary Let G be a bounded convex set, and _G the projection onto G, and a bounded random process. Projected algorithms of the types , where 0<a _n0, a _n =) occur frequently in applications (among other places) in control and communications theory. The asymptotic convergence properties of {X _n } as 0, n, have been well analyzed in the literature. Here, we use large deviations methods to get a more thorough understanding of the global behavior. Let be a stable point of the algorithm in the sense that X _n in distribution as 0, n. For the unconstrained case, rate of convergence results involve showing asymptotic normality of , and use linearizations about . In the constrained case is often on G, and such methods are inapplicable. But the large deviations method yields an alternative which is often more useful in the applications. The action functionals are derived and their properties (lower semicontinuity, etc.) are obtained. The statistics (mean value, etc.) of the escape times from a neighborhood of are obtained, and the global behavior on the infinite interval is described.Research has been supported in part by the US Army Research Office under Contract #DAAG 29-84-K-0082, and in part by the Office of Naval Research under Contract #N00014-83-K0542Research has been supported in part by the National Science Foundation Grant #ECS 82-11476, and the Air Force Office of Scientific Research under Contract #AF-AFOSR 81-0116     

Asymptotic behavior of partial sums of a series of probabilities of large deviations

Let {X_k} ^k=1 be a sequence of independent, symmetric random variables with characteristic functions f_k(t), The asymptotic behavior of the sum (for arbitrary > 0) is investigated under the assumption that f_k(t) belong to the domain of attraction of a stable law with exponent (0 < 2).Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituta im. V. A. Steklova AN SSSR, Vol. 85, pp. 225–236, 1979.I wish to use this opportunity to thank Professor V. V. Petrov for his constant attention to the work and for major assistance in preparation of the paper for publication.     

Extended large deviations

LetX ₁,X ₂,..., be a sequence ofi.i.d. random variables with a moment generating function finite in a neighborhood of 0. Further, for each integern1, letS _n denote the sum of the firstn terms in this sequence. We study the extended large deviation of such sums, meaning,P{S _n >n _n }, where _n is any sequence converging to infinity. We also derive functional extended large deviation theorems and then apply them to obtain functional versions of the Erdös-Rényi strong law of large numbers.Research partially supported by an NSF Grant.     

Nonconventional large deviations theorems

We obtain large deviations theorems for both discrete time expressions of the form $\sum _{n=1}^NF\big (X(q_1(n)),\ldots ,X(q_\ell (n))\big )$ and similar expressions of the form $\int _0^TF\big ( X(q_1(t)),\ldots , X(q_\ell (t))\big )dt$ in continuous time. Here $X(n),n\ge 0$ or $X(t), t\ge 0$ is a Markov process satisfying Doeblin’s condition, $F$ is a bounded continuous function and $q_i(n)=in$ for $i\le k$ while for $i>k$ they are positive functions taking on integer values on integers with some growth conditions which are satisfied, for instance, when $q_i$ ’s are polynomials of increasing degrees. Applications to some types of dynamical systems such as mixing subshifts of finite type and hyperbolic and expanding transformations will be obtained, as well.     

Criteria for large deviations

We give the general variational form of

for any bounded above Borel measurable function on a topological space , where is a net of Borel probability measures on , and a net in converging to . When is normal, we obtain a criterion in order to have a limit in the above expression for all continuous bounded, and deduce new criteria of a large deviation principle with not necessarily tight rate function; this allows us to remove the tightness hypothesis in various classical theorems.

     

On large deviations and density functions

Suppose thatX ₁,X ₂, ... is a sequence of absolutely continuous or integer valued random variables with corresponding probability density functionsf _n (x). Let {φ _n } _n=1 ^∞ be a sequence of real numbers, then necessary and sufficient conditions are given forn ⁻¹ logf _n (φ _n )-n ⁻¹ log P (X _n >φ _n )=0(1) asn→∞.     

Multilevel large deviations and interacting diffusions

Summary Let ( ^N ) be a sequence of random variables with values in a topological space which satisfy the large deviation principle. For eachM and eachN, let ^{M, N} denote the empirical measure associated withM independent copies of ^N . As a main result, we show that ( ^{M, N} ) also satisfies the large deviation principle asM,N. We derive several representations of the associated rate function. These results are then applied to empirical measure processes ^{M, N} (t) =M ^–1 _i=1 ^N _i ^N (t) 0tT, where ( ₁ ^N ,..., _M ^N (t)) is a system of weakly interacting diffusions with noise intensity 1/N. This is a continuation of our previous work on the McKean-Vlasov limit and related hierarchical models ([4], [5]).Research partially supported by a Natural Science and Engineering Research Council of Canada operating grant     

The large deviations theorem and ergodicity

In this paper, some relationships between stochastic and topological properties of dynamical systems are studied. For a continuous map f from a compact metric space X into itself, we show that if f satisfies the large deviations theorem then it is topologically ergodic. Moreover, we introduce the topologically strong ergodicity, and prove that if f is a topologically strongly ergodic map satisfying the large deviations theorem then it is sensitively dependent on initial conditions.     

Poisson approximation for large deviations

Upper and lower bounds are given for P(S ≤ k), 0 ≤ k ≤ ES, where S is a sum of indicator variables with a special structure, which appears, for example, in subgraph counts in random graphs. in typical cases, these bounds are close to the corresponding probabilities for a Poisson distribution with the same mean as S. There are no corresponding general bounds for P(S ≥ k), k > ES, but some partial results are given.     

Densities of idempotent measures and large deviations

Considering measure theory in which the semifield of positive real numbers is replaced by an idempotent semiring leads to the notion of idempotent measure introduced by Maslov. Then, idempotent measures or integrals with density correspond to supremums of functions for the partial order relation induced by the idempotent structure. In this paper, we give conditions under which an idempotent measure has a density and show by many examples that they are often satisfied. These conditions depend on the lattice structure of the semiring and on the Boolean algebra in which the measure is defined. As an application, we obtain a necessary and sufficient condition for a family of probabilities to satisfy the large deviation principle.

     

Averaging in dynamical systems and large deviations

Summary The paper treats ordinary differential equations of the form wheref ^t is a hyperbolic flow. Large deviations bounds for the averaging principle are obtained here in the form appeared previously in [F₁, F₂] for the case when the flowf ^t is replaced by a Markov process.Oblatum 4-XII-1991Partially supported by US-Israel BSF and the Landau Center for Research in Mathematical Analysis, supported by Minerva Foundation (Germany)