Pathwise Large Deviations of Stochastic Differential Equations

This work deals with the asymptotic behaviour of highly nonlinear stochastic differential equations, as well as linear and nonlinear functional differential equations. Both ordinary functional and neutral equations are analysed. In the first chapter, a class of nonlinear SDEs (mainly scaler equations) which satisfy the Law of the Iterated Logarithm is studied, and the results applied to a financial market model. The second chapter deals with a more general class of finite-dimensional nonlinear SDEs and SFDEs, employing comparison and time change methods, as well as martingale inequalities, to determine the almost sure rate of growth of the running maximum of functionals of the solution. The third chapter examines the exact almost sure rate of growth of the large deviations for affine SFDEs, and for equations with additive noise which are subject to relatively weak nonlinearities at infinity. The fourth chapter extends conventional conditons for existence and uniqueness of neutral functional differential equations to the stochastic case. The final chapter deals with large fluctuations of stochastic neutral functional differential equations.

Autorentext

Huizhong Wu graduated with a PhD in Mathematics from Dublin City University after completing first class honours degrees in Economics and Finance and a Higher Diploma in Mathematics from University College Dublin. John Appleby is a senior lecturer in DCU. He has more than 70 peer- reviewed publications in the area of stochastic analysis.