Semiparametric efficient adaptive estimation of the GJR-GARCH model

Ciccarelli Nicola ()
Additional contact information
Ciccarelli Nicola: Center and Department of Econometrics and Operations Research, Tilburg University, P.O. Box 90153, 5000 LETilburg, Netherlands

Statistics & Risk Modeling, 2018, vol. 35, issue 3-4, 141-160

Abstract: In this paper we derive a semiparametric efficient adaptive estimator for the GJR-GARCH(1,1){(1,1)} model. We first show that the quasi-maximum likelihood estimator is consistent and asymptotically normal for the model used in analysis, and we secondly derive a semiparametric estimator that is more efficient than the quasi-maximum likelihood estimator. Through Monte Carlo simulations, we show that the semiparametric estimator is adaptive for the parameters included in the conditional variance of the GJR-GARCH(1,1){(1,1)} model with respect to the unknown distribution of the innovation.

Keywords: Semiparametric estimation; nonparametric inference; asymmetric volatility; GJR-GARCH (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/strm-2017-0015 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:strimo:v:35:y:2018:i:3-4:p:141-160:n:3

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/strm/html

DOI: 10.1515/strm-2017-0015

Access Statistics for this article

Statistics & Risk Modeling is currently edited by Robert Stelzer

More articles in Statistics & Risk Modeling from De Gruyter
Bibliographic data for series maintained by Peter Golla ().