A0925
Title: COGARCH models: A statistical application to real data
Authors: Ilia Negri - University of Bergamo (Italy) [presenting]
Enrico Bibbona - University of Torino (Italy)
Abstract: One of the reason that suggests to use COGARCH models to fit financial log-return data is due to the fact that they are able to capture the so called stylized facts observed in real data: uncorrelated log-returns but correlated absolute log-return, time varying volatility, conditional heteroscedasticity, cluster in volatility, heavy tailed and asymmetric unconditional distributions, leverage effects. The aims are to fit the COGARCH models to a real financial data set, to estimate the parameters of the models via the prediction based estimating functions, and to look at the performance of these estimates comparing them with the estimates obtained via the method of moment estimators. Moreover, as COGARCH models can be seen as an extension of the GARCH idea to continuous in time process a comparison with the last model is also performed.
