Stochastic Calculus with Respect to Gaussian Processes

Potential Analysis - In the present paper, we are going to show that outside a slim set in the sense of Malliavin (or quasi-surely), the signature path (which consists of iterated path integrals in...     

Stochastic Integration of Operator-Valued Functions with Respect to Banach Space-Valued Brownian Motion

Let E be a real Banach space with property (α) and let W _Γ be an E-valued Brownian motion with distribution Γ. We show that a function is stochastically integrable with respect to W _Γ if and only if Γ-almost all orbits Ψx are stochastically integrable with respect to a real Brownian motion. This result is derived from an abstract result on existence of Γ-measurable linear extensions of γ-radonifying operators with values in spaces of γ-radonifying operators. As an application we obtain a necessary and sufficient condition for solvability of stochastic evolution equations driven by an E-valued Brownian motion. The first named author gratefully acknowledges the support by a ‘VIDI subsidie’ in the ‘Vernieuwingsimpuls’ programme of The Netherlands Organization for Scientific Research (NWO) and the Research Training Network HPRN-CT-2002–00281. The second named author was supported by grants from the Volkswagenstiftung (I/78593) and the Deutsche Forschungsgemeinschaft (We 2847/1–1).     

Integration with respect to local time

Let be the local time process of a linear Brownian motion B. We integrate the Borel functions on with respect to . This allows us to write Itôrs formula for new classes of functions, and to define a local time process of B on any borelian curve. Some results are extended from deterministic to random functions.     

A White-Noise Approach to Stochastic Calculus

During the past 15 years a new technique, called the stochastic limit of quantum theory, has been applied to deduce new, unexpected results in a variety of traditional problems of quantum physics, such as quantum electrodynamics, bosonization in higher dimensions, the emergence of the noncrossing diagrams in the Anderson model, and in the large-N-limit in QCD, interacting commutation relations, new photon statistics in strong magnetic fields, etc. These achievements required the development of a new approach to classical and quantum stochastic calculus based on white noise which has suggested a natural nonlinear extension of this calculus. The natural theoretical framework of this new approach is the white-noise calculus initiated by T. Hida as a theory of infinite-dimensional generalized functions. In this paper, we describe the main ideas of the white-noise approach to stochastic calculus and we show that, even if we limit ourselves to the first-order case (i.e. neglecting the recent developments concerning higher powers of white noise and renormalization), some nontrivial extensions of known results in classical and quantum stochastic calculus can be obtained.     

Stochastic integration with respect to q Brownian motion

 We develop a stochastic integration with respect to a q-Brownian motion (for ), i.e. a non commutative process where the operator a _t and its adjoint fulfill the q commutation relation ; under the vacuum state expectation. We show that this process enjoys a predictable representation type property. Received: 15 February 2002 / Revised version: 25 May 2002 / Published online: 30 September 2002 Mathematics Subject Classification (2000): 60H05, 46L50, 81S25     

Stochastic integration with respect to a stochastic integral

In this paper we prove first the property of integration with respect to a measure defined by density,h(fm) = (hf)mor a measure mand functions f,h, taking values in Banach spaces. Then we use this result to prove the similar “associativity” property of the stochastic integralL.(K-X)= (LK) Xfor processes X,K,Ltaking values in Banach spaces     

Singular Functions with Applications to Fractal Dimensions and Generalized Takagi Functions

We study a class of singular functions via a generalized dyadic system and Hausdorff dimensions are calculated for several sets related with these functions. Furthermore, we introduce a class of monotonic type on no-interval and almost everywhere differentiable functions that includes—as an exceptional case—the continuous nowhere differentiable Takagi function (multiplied by 2) among them.     

Extended Stochastic Integral and Wick Calculus on Spaces of Regular Generalized Functions Connected with Gamma Measure

We introduce and study an extended stochastic integral, a Wick product, and Wick versions of holomorphic functions on Kondrat'ev-type spaces of regular generalized functions. These spaces are connected with the Gamma measure on a certain generalization of the Schwartz distribution space . As examples, we consider stochastic equations with Wick-type nonlinearity. This paper is dedicated to Professor Yu. M. Berezansky, who is one of my mentors __________ Published in Ukrains'kyi Matematychnyi Zhurnal, Vol. 57, No. 8, pp. 1030–1057, August, 2005.     

Functions excessive with respect to a nonlinear resolvent

The aim of this paper is to give a notion of functions excessive with respect to a nonlinear resolvent, to show that some properties associated with excessive functions hold in this case, and to apply these results to the resolvent of a nonlinear (even non-quasilinear) differential operator. When the resolvent is T-nonexpansive we obtain more results.     

Stochastic integral with respect to set-valued square integrable martingales

In this paper, we shall firstly illustrate why we should consider integral of a stochastic process with respect to a set-valued square integrable martingale. Secondly, we shall prove the representation theorem of set-valued square integrable martingale. Thirdly, we shall give the definition of stochastic integral of a stochastic process with respect to a set-valued square integrable martingale and the representation theorem of this kind of integrals. Finally, we shall prove that the stochastic integral is a set-valued sub-martingale.     

A General Stochastic Calculus Approach to Insider Trading

The purpose of this paper is to present a general stochastic calculus approach to insider trading. We consider a market driven by a standard Brownian motion $B(t)$ on a filtered probability space $\displaystyle (\Omega,\F,\left\{\F\right\}_{t\geq 0},P)$ where the coefficients are adapted to a filtration ${\Bbb G}=\left\{\G_t\right\}_{0\leq t\leq T}$, with $\F_t\subset\G_t$ for all $t\in [0,T]$, $T>0$ being a fixed terminal time. By an {\it insider} in this market we mean a person who has access to a filtration (information) $\displaystyle{\Bbb H}=\left\{\H_t\right\}_{0\leq t\leq T}$ which is strictly bigger than the filtration $\displaystyle{\Bbb G}=\left\{\G_t\right\}_{0\leq t\leq T}$. In this context an insider strategy is represented by an $\H_t$-adapted process $\phi(t)$ and we interpret all anticipating integrals as the forward integral defined in [23] and [25]. We consider an optimal portfolio problem with general utility for an insider with access to a general information $\H_t \supset\G_t$ and show that if an optimal insider portfolio $\pi^*(t)$ of this problem exists, then $B(t)$ is an $\H_t$-semimartingale, i.e. the enlargement of filtration property holds. This is a converse of previously known results in this field. Moreover, if $\pi^*$ exists we obtain an explicit expression in terms of $\pi^*$ for the semimartingale decomposition of $B(t)$ with respect to $\H_t$. This is a generalization of results in [16], [20] and [2].     

Functions of bounded variation with respect to a Tchebycheff system

A characterization of Tchebycheff systems is given, in terms of Weak Tchebycheff systems.     

Integration with respect to operator-valued measures with applications to quantum estimation theory

Summary This paper is concerned with the development of an integration theory with respect to operator-valued measures which is required in the study of certain convex optimization problems. These convex optimization problems in their turn are rigorous formulations of detection theory in a quantum communication context, which generalise classical (Bayesian) detection theory. The integration theory which is developed in this paper is used in conjunction with convex analysis in Banach spaces to give necessary and sufficient conditions of optimality for this class of convex optimization problems.This research has been supported by the Air Force Office of Scientific Research under Grants AFOSR 77-3281D and AFOSR 82-0135 and the National Science Foundation under Grant NSF ENG 76-02860. A portion of this research was done while the first author was a CNR Visiting Professor at Istituto Matematico U. Dini, Università di Firenze, Italy.     

Stochastic calculus with respect to G-Brownian motion viewed through rough paths

We study rough path properties of stochastic integrals of Ito's type and Stratonovich's type with respect to G-Brownian motion. The roughness of G-Brownian motion is estimated and then the pathwise Norris lemma in G-framework is obtained.     

Stochastic integral with respect to a semi-Markov process of diffusion type

We consider a multidimensional semi-Markov process of diffusion type. A stochastic integral with respect to the semi-Markov process is defined in terms of asymptotics related to the first exit time from a small neighborhood of the starting point of the process, and, in particular, in terms of its characteristic operator. This integral is equal to the sum of two other integrals: the first one is a curvilinear integral with respect to an additive functional defined in terms of the expected first exit time from a small neighborhood, and the second one is a stochastic integral with respect to a martingale of special kind. To prove the existence and to derive the properties of the integral, both the method of deducing sequences and that of inscribed ellipsoids are used. For Markov processes of diffusion type, the new definition of the stochastic integral is reduced to the standard one. Bibliography: 8 titles. __________ Translated from Zapiski Nauchnykh Seminarov POMI, Vol. 328, 2005, pp. 251–276.     

Integration with respect to fractal functions and stochastic calculus. I

The classical Lebesgue–Stieltjes integral ∫ ^b _a fdg of real or complex-valued functions on a finite interval (a,b) is extended to a large class of integrands f and integrators g of unbounded variation. The key is to use composition formulas and integration-by-part rules for fractional integrals and Weyl derivatives. In the special case of H?lder continuous functions f and g of summed order greater than 1 convergence of the corresponding Riemann–Stieltjes sums is proved. The results are applied to stochastic integrals where g is replaced by the Wiener process and f by adapted as well as anticipating random functions. In the anticipating case we work within Slobodeckij spaces and introduce a stochastic integral for which the classical It? formula remains valid. Moreover, this approach enables us to derive calculation rules for pathwise defined stochastic integrals with respect to fractional Brownian motion. Received: 14 January 1998 / Revised version: 9 April 1998