On the maximum likelihood estimator for irregularly observed time series data from COGARCH models

* In this paper, we study the asymptotic properties of the maximum likelihood estimator (MLE) in COGARCH(1,1) models driven by Levy processes as proposed by Maller et al. ([13]). We show that the MLE is consistent and asymptotically normal under some conditions relevant to the moments of the driving...

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Bibliographic Details
Published inRevstat Vol. 11; no. 2; p. 135
Main Authors Kim, Moosup, Lee, Sangyeol
Format Journal Article
LanguageEnglish
Published Instituto Nacional de Estatistica 01.06.2013
Instituto Nacional de Estatística | Statistics Portugal
Subjects
asymptotic normality
COGARCH(1,1) models
consistency
irregular time spaces
Mathematical research
maximum likelihood estimation
Sampling (Statistics)
sampling scheme
Statistical models
Time series analysis
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ISSN1645-6726
2183-0371
DOI10.57805/revstat.v11i2.131

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Summary:* In this paper, we study the asymptotic properties of the maximum likelihood estimator (MLE) in COGARCH(1,1) models driven by Levy processes as proposed by Maller et al. ([13]). We show that the MLE is consistent and asymptotically normal under some conditions relevant to the moments of the driving Levy process and the sampling scheme. Key-Words: * COGARCH(1,1) models; maximum likelihood estimation; consistency; asymptotic normality; sampling scheme; irregular time spaces. AMS Subject Classification: * 62F12, 62M86.
ISSN:1645-6726
2183-0371
DOI:10.57805/revstat.v11i2.131