In this paper, we study the asymptotic behavior of the sequential empirical process and the sequential empirical copula process, both constructed from residuals of multivariate stochastic volatility models. Applications for the detection of structural changes and specification tests of the...

In this paper, we consider an investor who plays in a market that involves a risky asset whose instantaneous rate of return changes at unknown random times. This return rate is assumed to follow the law of a Compound Poisson Process. We construct optimal mathematical strategies in this context...

We consider the Euler approximation of stochastic differential equations (SDEs) driven by Lévy processes in the case where we cannot simulate the increments of the driving process exactly. In some cases, where the driving process Y is a subordinated stable process, i.e., Y=Z(V) with V a...

We study simulated annealing algorithms to maximise a function [psi] on a subset of . In classical simulated annealing, given a current state [theta]n in stage n of the algorithm, the probability to accept a proposed state z at which [psi] is smaller, is exp(-[beta]n+1([psi](z)-[psi]([theta]n))...

The Euler scheme is a well-known method of approximation of solutions of stochastic differential equations (SDEs). A lot of results are now available concerning the precision of this approximation in case of equations driven by a drift and a Brownian motion. More recently, people got interested...

Building on the work of Schweizer (1995) and Cern and Kallseny (2007), we present discrete time formulas minimizing the mean square hedging error for multidimensional assets. In particular, we give explicit formulas when a regime-switching random walk or a GARCH-type process is utilized to model...