Fonction de masse (probabilités)

En théorie des probabilités, la fonction de masse est la fonction qui donne la probabilité de chaque issue ( résultat élémentaire) d'une expérience aléatoire. C'est souvent ainsi que l'on définit une loi de probabilité discrète. Elle se distingue de la fonction de densité, de la densité de probabilité, en ceci que les densités de probabilité ne sont définies que pour des variables aléatoires absolument continues, et que ce sont leurs intégrales sur des domaines qui ont valeurs de probabilités (et non leurs valeurs en des points). Fonction de masse d'une loi de probabilité Soit (\Omega, \mathcal{A}, \mathbb{P}) un espace probabilisé. On appelle fonction de masse de \mathbb{P}, et on note p, la fonction de \Omega dans [0,1] définie par : : \forall \omega \in \Omega, \quad p(\omega) = \begin{cases} \mathbb{P}\big({\omega}\big) & \text{si } {\omega} \in \mathcal{A}, \ 0 & \text{si } {\omega} \no

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Fei Pu

In this thesis, we study systems of linear and/or non-linear stochastic heat equations and fractional heat equations in spatial dimension

1

driven by space-time white noise. The main topic is the study of hitting probabilities for the solutions to these systems. We first study the properties of the probability density functions of the solution to non-linear systems of stochastic fractional heat equations driven by multiplicative space-time white noise. Using the techniques of Malliavin calculus, we prove that the one-point probability density function of the solution is infinitely differentiable, uniformly bounded and positive everywhere. Moreover, a Gaussian-type upper bound on the two-point probability density function is obtained by a detailed analysis of the small eigenvalues of the Malliavin matrix. We establish an optimal lower bound on hitting probabilities for the (non-Gaussian) solution, which is as sharp as that for the Gaussian solution to a system of linear equations. We develop a new method to study the upper bound on hitting probabilities, from the perspective of probability density functions. For the solution to the linear stochastic heat equation, we prove that the random vector, which consists of the solution and the supremum of a linear increment of the solution over a time segment, has an infinitely differentiable probability density function. We derive a formula for this density and establish a Gaussian-type upper bound. The smoothness property and Gaussian-type upper bound for the density of the supremum of the solution over a space-time rectangle touching the

t = 0

axis are also studied. Furthermore, we extend these results to the solutions of systems of linear stochastic fractional heat equations. For a system of linear stochastic heat equations with Dirichlet boundary conditions, we present a sufficient condition for certain sets to be hit with probability one.

EPFL2018

On the density of the supremum of the solution to the linear stochastic heat equation

Robert Dalang, Fei Pu

We study the regularity of the probability density function of the supremum of the solution to the linear stochastic heat equation. Using a general criterion for the smoothness of densities for locally nondegenerate random variables, we establish the smoothness of the joint density of the random vector whose components are the solution and the supremum of an increment in time of the solution over an interval (at a fixed spatial position), and the smoothness of the density of the supremum of the solution over a space-time rectangle that touches thet=0 axis. Applying the properties of the divergence operator, we establish a Gaussian-type upper bound on these two densities respectively, which presents a close connection with the Holder-continuity properties of the solution.

SPRINGER2020

An optimal prediction problem in financial modelling

Violetta Bernyk

The subject of the present thesis is an optimal prediction problem concerning the ultimate maximum of a stable Lévy process over a finite interval of time. Such "optimal prediction" problems are of both theoretical and practical interest, in particular they have applications in finance. For instance, suppose that an investor has a long position in one financial asset, whose price is modelled by some stochastic process. The investor's objective is to determine a "best moment" at which to close out the position and to sell the asset at the highest possible price. This optimal decision must be based on continuous observations of the asset price performance and only on the information accumulated to date. Hence, the investor should use a prediction (forecasting) of the future evolution of the price of the financial security. We examine this problem in the case where the asset price is modelled by a Lévy process. Indeed, during the last several years, the application of Lévy processes in the modelling financial asset returns has become one of the active research directions in quantitative finance. Thus, this thesis contains suitable new results concerning Lévy processes. We derive the law of the supremum process associated with a strictly stable Lévy process with no negative jumps which is not a subordinator. We note that the latter problem dates back to 1973. In particular, we show that the probability density function of the supremum process can be expressed using an explicit power series representation or via an integral representation. We also derive the infinitesimal generator of the reflected process associated with a general strictly stable Lévy process. Throughout this thesis, we apply the theory of optimal stopping, the methods of fractional differential calculus, and some results from fluctuation theory. Implementing these theories in the context of Lévy processes requires the development of specific analytical results. In the case where the asset price is modelled by a spectrally positive stable Lévy process, we describe the optimal strategy under certain conditions on the model parameters. The optimal strategy is of the following form: the investor must stop the observation of the price process and sell the asset as soon as the associated reflected process crosses for the first time a particular stopping boundary. We also provide numerical estimates and simulation examples of the results obtained by using this strategy.

EPFL2007