Itô calculus, named after Kiyosi Itô, extends the methods of calculus to stochastic processes such as Brownian motion (see Wiener process). It has important applications in mathematical finance and stochastic differential equations.
The central concept is the Itô stochastic integral, a stochastic generalization of the Riemann–Stieltjes integral in analysis. The integrands and the integrators are now stochastic processes:
where H is a locally square-integrable process adapted to the filtration generated by X , which is a Brownian motion or, more generally, a semimartingale. The result of the integration is then another stochastic process. Concretely, the integral from 0 to any particular t is a random variable, defined as a limit of a certain sequence of random variables. The paths of Brownian motion fail to satisfy the requirements to be able to apply the standard techniques of calculus. So with the integrand a stochastic process, the Itô stochastic integral amounts to an integral with respect to a function which is not differentiable at any point and has infinite variation over every time interval.
The main insight is that the integral can be defined as long as the integrand H is adapted, which loosely speaking means that its value at time t can only depend on information available up until this time. Roughly speaking, one chooses a sequence of partitions of the interval from 0 to t and constructs Riemann sums. Every time we are computing a Riemann sum, we are using a particular instantiation of the integrator. It is crucial which point in each of the small intervals is used to compute the value of the function. The limit then is taken in probability as the mesh of the partition is going to zero. Numerous technical details have to be taken care of to show that this limit exists and is independent of the particular sequence of partitions. Typically, the left end of the interval is used.
Important results of Itô calculus include the integration by parts formula and Itô's lemma, which is a change of variables formula.
Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.
This course will provide a basic knowledge of the stochastic calculus of variations with respect to the Brownian motion. A variety of applications will be presented including the regularity of probabi
In this course we will introduce and study numerical integrators for stochastic differential equations. These numerical methods are important for many applications.
This course gives an introduction to probability theory and stochastic calculus in discrete and continuous time. We study fundamental notions and techniques necessary for applications in finance such
Couvre le calcul stochastique, en se concentrant sur la formule d'Itô, les équations différentielles stochastiques, les propriétés martingales et le prix d'option.
Le calcul est l’étude des phénomènes aléatoires dépendant du temps. À ce titre, c'est une extension de la théorie des probabilités. Ne pas confondre avec la technique des calculateurs stochastiques. Le domaine d’application du calcul stochastique comprend la mécanique quantique, le traitement du signal, la chimie, les mathématiques financières, la météorologie et même la musique. Un processus aléatoire est une famille de variables aléatoires indexée par un sous-ensemble de ou , souvent assimilé au temps (voir aussi Processus stochastique).
Dans la théorie des probabilités, le théorème de Girsanov indique comment un processus stochastique change si l'on change de mesure. Ce théorème est particulièrement important dans la théorie des mathématiques financières dans le sens où il donne la manière de passer de la probabilité historique qui décrit la probabilité qu'un actif sous-jacent (comme le prix d'une action ou un taux d'intérêt) prenne dans le futur une valeur donnée à la probabilité risque neutre qui est un outil très utile pour évaluer la valeur d'un dérivé du sous-jacent.
In probability theory, a real valued stochastic process X is called a semimartingale if it can be decomposed as the sum of a local martingale and a càdlàg adapted finite-variation process. Semimartingales are "good integrators", forming the largest class of processes with respect to which the Itô integral and the Stratonovich integral can be defined. The class of semimartingales is quite large (including, for example, all continuously differentiable processes, Brownian motion and Poisson processes).
Epsilon-near-zero (ENZ) materialshave attracted great interestdue to their exotic linear and nonlinear responses, which makes itsignificant to tune ENZ wavelengths for wavelength-dependent applications.However, studies to achieve tunability in a wide spect ...
We explore statistical physics in both classical and open quantum systems. Additionally, we will cover probabilistic data analysis that is extremely useful in many applications.
We explore statistical physics in both classical and open quantum systems. Additionally, we will cover probabilistic data analysis that is extremely useful in many applications.
This course gives you an easy introduction to interest rates and related contracts. These include the LIBOR, bonds, forward rate agreements, swaps, interest rate futures, caps, floors, and swaptions.
In this paper we derive quantitative estimates in the context of stochastic homogenization for integral functionals defined on finite partitions, where the random surface integrand is assumed to be stationary. Requiring the integrand to satisfy in addition ...
We consider the asymmetric exclusion process with a driven tagged particle on Z which has different jump rates from other particles. When the non-tagged particles have non-nearest-neighbor jump rates , we show that the tagged particle can have a speed whic ...