We investigate the asymptotic behavior of the maxima of a general class of deterministic chaotic processes –including the Tent map and the Logistic map -, of noisy chaotic processes, and of the Gaussian long memory k-factor Genebauer processes.

We consider a class of linear regression model with extreme distribution noise. We show by a mean of point process technique that the asymptotic distribution of the maximum is the same as the one of the max of the noise process, under specific conditions.

We study the asymptotic behaviour of the extreme values of a stochastic volatility model when the noise follows a generalized error distribution extreme. We provide a Monte Carlo experiment to illustrate th choice of the assumptions. We deal also with the finite sample behaviour of the...