In this thesis, we consider computational methods of finding exit probabilities for a class of multivariate stochastic processes. While there is an abundance of results for one-dimensional processes, for multivariate processes one has to rely on approximations or simulation methods. We adopt a Large Deviations approach in order to estimate barrier crossing probabilities of a multivariate Brownian Bridge. We use this approach in conjunction with numerical techniques to propose an efficient method of obtaining barrier crossing probabilities of a multivariate Brownian motion. Using numerical examples, we demonstrate that our method works better than other existing methods. We present applications of the proposed method in addressing problems in finance such as estimating default probabilities of several credit risky entities and pricing credit default swaps. We also extend our computational method to efficiently estimate a barrier crossing probability of a sum of Geometric Brownian motions. This allows us to perform a portfolio selection by maximizing a path-dependent utility function.

In this thesis, we consider computational methods of finding exit probabilities for a class of multivariate stochastic processes. While there is an abundance of results for one-dimensional processes, for multivariate processes one has to rely on approximations or simulation methods. We adopt a Large Deviations approach in order to estimate barrier crossing probabilities of a multivariate Brownian Bridge. We use this approach in conjunction with numerical techniques to propose an efficient method of obtaining barrier crossing probabilities of a multivariate Brownian motion. Using numerical examples, we demonstrate that our method works better than other existing methods. We present applications of the proposed method in addressing problems in finance such as estimating default probabilities of several credit risky entities and pricing credit default swaps. We also extend our computational method to efficiently estimate a barrier crossing probability of a sum of Geometric Brownian motions. This allows us to perform a portfolio selection by maximizing a path-dependent utility function.

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The problem of guessing a random string is studied. It arises in the analysis of the strength of secret-key cryptosystems against guessing attacks. Expected number of guesses, or more generally moments of the number of guesses needed to break the cryptosystem grow exponentially with the length of the string. This thesis studies the rate of exponential growth of these moments using the theory of large deviations. A closer elation between guessing and compression is first established. For systems with large key rates, it is shown that if the source’s sequence of so-called information spectrum random variables satisfies the large deviation property with a certain rate function, then the limiting guessing exponent exists and is a scalar multiple of the Legendre-Fenchel dual of the rate function. This is then used to rederive several prior results. The large deviations approach brings to light the relevance of information spectrum in determining guessing exponents. For systems with key-rate constraints, bounds are derived on the limiting guessing exponents for general sources. The obtained bounds are shown to be tight for stationary memoryless, Markov, and unifilar sources, thus recovering some known results. The bounds are obtained by establishing a close relationship between error exponents and correct decoding exponents for fixed rate source compression on the one hand and exponents for guessing moments on the other.

Les petites machines, comme les moteurs moléculaires ou les particules actives, fonctionnent dans un environnement fortement fluctuant qui affecte leur efficacité ou leur puissance. L'objectif de cette thèse est de décrire les petites machines à l'aide de la thermodynamique stochastique et de la théorie des grandes déviations. En reliant localement puis globalement les courants aux forces thermodynamiques, on introduit une matrice de conductance hors d'équilibre, qui généralise la matrice d'Onsager pour un système stationnaire hors d'équilibre. Cela permet de majorer l'efficacité des machines par une fonction universelle qui ne dépend que du degré de couplage entre les courants d'entrée et de sortie. On obtient aussi de nouvelles relations générales entre puissance et efficacité. Du point de vue des fluctuations, la matrice de conductance hors d'équilibre est reliée à une borne quadratique pour les fonctions de grande déviation des courants. Cette borne permet d'obtenir des bornes pour les fonctions de grande déviation de l'efficacité, mais aussi de revisiter le théorème de fluctuation-dissipation comme une inégalité dans le cas des systèmes loin de l'équilibre. Pour terminer, on étudie l'effet d'une brisure d'ergodicité sur les fluctuations d'observables comme l'activité, les courants ou l'efficacité. En particulier, on calcule la fonction de grande déviation de l'efficacité pour un ensemble de nanomachines en interaction pour lesquelles un couplage fort et une brisure d'ergodicité apparaissent à la limite thermodynamique. / Small machines -- like molecular motors or active particles -- operate in highly fluctuating environments that affect their efficiency and power. This thesis aims at describing small machines using stochastic thermodynamics and large deviation theory. By relating mean currents to thermodynamic forces, locally first and then at the global level, we introduce the non-equilibrium conductance matrix that generalizes the Onsager matrix for stationary non-equilibrium systems. We use it to bound machine efficiency by a universal function depending only on the degree of coupling between input and output currents and to find new general power-efficiency trade-offs. On the fluctuations side, the non-equilibrium conductance matrix can be used to find a quadratic bound on the large deviation function of currents. This enables to revisit the fluctuation-dissipation theorem as an inequality when dealing with far-from-equilibrium systems, but also to derive bounds on the efficiency large deviation function. Finally, we study the effects of ergodicity breaking on the fluctuations of observables like activity, currents or efficiency. In particular, we derive the efficiency large deviation function for a model of interacting nanomachines, for which tight coupling and ergodicity breaking emerge in the thermodynamic limit.