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Semiparametric time series models with log-concave innovations: maximum likelihood estimation and its consistency

Chen, Yining (2015) Semiparametric time series models with log-concave innovations: maximum likelihood estimation and its consistency. Scandinavian Journal of Statistics, 42 (1). pp. 1-31. ISSN 0303-6898

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Identification Number: 10.1111/sjos.12092

Abstract

We study semiparametric time series models with innovations following a log-concave distribution. We propose a general maximum likelihood framework that allows us to estimate simultaneously the parameters of the model and the density of the innovations. This framework can be easily adapted to many well-known models, including autoregressive moving average (ARMA), generalized autoregressive conditionally heteroscedastic (GARCH), and ARMA-GARCH models. Furthermore, we show that the estimator under our new framework is consistent in both ARMA and ARMA-GARCH settings. We demonstrate its finite sample performance via a thorough simulation study and apply it to model the daily log-return of the FTSE 100 index.

Item Type: Article
Official URL: http://onlinelibrary.wiley.com/journal/10.1111/(IS...
Additional Information: © 2014 Board of the Foundation of the Scandinavian Journal of Statistics
Divisions: Statistics
Subjects: Q Science > QA Mathematics
Sets: Departments > Statistics
Date Deposited: 16 Mar 2016 11:23
Last Modified: 20 Mar 2019 02:47
URI: http://eprints.lse.ac.uk/id/eprint/65753

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