Premium
Higher Moments and Prediction‐Based Estimation for the COGARCH(1,1) Model
Author(s) -
Bibbona Enrico,
Negri Ilia
Publication year - 2015
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12142
Subject(s) - mathematics , autoregressive conditional heteroskedasticity , inference , estimation , method of moments (probability theory) , moment (physics) , function (biology) , order (exchange) , statistical inference , econometrics , statistics , computer science , artificial intelligence , estimator , finance , volatility (finance) , physics , management , classical mechanics , evolutionary biology , economics , biology
COGARCH models are continuous time versions of the well‐known GARCH models of financial returns. The first aim of this paper is to show how the method of prediction‐based estimating functions can be applied to draw statistical inference from observations of a COGARCH(1,1) model if the higher‐order structure of the process is clarified. A second aim of the paper is to provide recursive expressions for the joint moments of any fixed order of the process. Asymptotic results are given, and a simulation study shows that the method of prediction‐based estimating function outperforms the other available estimation methods.