B0941
Title: Identifying mixed fractional stable processes from high-frequency data
Authors: Fabian Mies - Delft University of Technology (Netherlands) [presenting]
Mark Podolskij - University of Luxembourg (Luxembourg)
Abstract: The linear fractional stable motion generalizes two popular classes of stochastic processes, namely stable Levy processes, on the one hand, and fractional Brownian motion, on the other hand. Hence, it may be regarded as a basic building block for models for high-frequency economic time series. We study a stylized model consisting of a superposition of independent linear fractional stable motions. We construct estimators for all parameters and derive their asymptotic normality in a high-frequency regime. The conditions for consistency turn out to be sharp for two prominent special cases: (i) for Levy processes, i.e. for the estimation of the successive Blumenthal-Getoor indices, and (ii) for the mixed fractional Brownian motion introduced by Cheridito. In the remaining cases, our results reveal an interesting interplay between the Hurst parameter and the index of stability.
