La constatation que les prix des actifs boursiers sautent brusquement a conduit à étudier des modèles de marchés avec sauts. Cette thèse va dans cette direction. On y considère des marchés dirigés par des martingales normales qui ont la propriété de représentation chaotique: les martingales vérifiant une équation de structure déterministe, la martingale d'Azéma, etc. On trouve des stratégies de couverture pour les options européennes, asiatiques et Lookback soit par la formule d'Itô, soit par la formule de Clark-Ocone selon la plus appropriée. L'application du calcul de Malliavin au calcul des Greeks est traitée pour les options asiatiques dans le cas d'un marché dirigé par un processus de Poisson. On traite aussi de couverture dans un modèle à volatilité stochastique avec sauts où le prix de l'actif risqué est dirigé par un processus somme d'un mouvement brownien et d'un processus de Poisson 2-dimensionnels. Le marché est incomplet et il existe une infinité de mesures martingales équivalentes. On minimise l'entropie pour choisir telle mesure. Sous celle-ci on calcule la stratégie minimisant la variance.

In mathematical finance, the sensitivities of option prices to various market parameters, also known as the “Greeks”, reflect the exposure to different sources of risk. Computing these is essential to predict the impact of market moves on portfolios and to hedge them adequately. This is commonly done using Monte Carlo simulations. However, obtaining accurate estimates of the Greeks can be computationally costly. Multilevel Monte Carlo offers complexity improvements over standard Monte Carlo techniques. However the idea has never been used for the computation of Greeks. In this work we answer the following questions: can multilevel Monte Carlo be useful in this setting? If so, how can we construct efficient estimators? Finally, what computational savings can we expect from these new estimators? We develop multilevel Monte Carlo estimators for the Greeks of a range of options: European options with Lipschitz payoffs (e.g. call options), European options with discontinuous payoffs (e.g. digital options), Asian options, barrier options and lookback options. Special care is taken to construct efficient estimators for non-smooth and exotic payoffs. We obtain numerical results that demonstrate the computational benefits of our algorithms. We discuss the issues of convergence of pathwise sensitivities estimators. We show rigorously that the differentiation of common discretisation schemes for Ito processes does result in satisfactory estimators of the the exact solutions’ sensitivities. We also prove that pathwise sensitivities estimators can be used under some regularity conditions to compute the Greeks of options whose underlying asset’s price is modelled as an Ito process. We present several important results on the moments of the solutions of stochastic differential equations and their discretisations as well as the principles of the so-called “extreme path analysis”. We use these to develop a rigorous analysis of the complexity of the multilevel Monte Carlo Greeks estimators constructed earlier. The resulting complexity bounds appear to be sharp and prove that our multilevel algorithms are more efficient than those derived from standard Monte Carlo.