Estimating GARCH-type models with symmetric stable innovations: Indirect inference versus maximum likelihood

Giorgio Calzolari, Roxana Halbleib () and Alessandro Parrini ()

Computational Statistics & Data Analysis, 2014, vol. 76, issue C, 158-171

Abstract: Financial returns exhibit conditional heteroscedasticity, asymmetric responses of their volatility to negative and positive returns (leverage effects) and fat tails. The α-stable distribution is a natural candidate for capturing the tail-thickness of the conditional distribution of financial returns, while the GARCH-type models are very popular in depicting the conditional heteroscedasticity and leverage effects. However, practical implementation of α-stable distribution in finance applications has been limited by its estimation difficulties. The performance of the indirect inference approach using GARCH models with Student’s t distributed errors as auxiliary models is compared to the maximum likelihood approach for estimating GARCH-type models with symmetric α-stable innovations. It is shown that the expected efficiency gains of the maximum likelihood approach come at high computational costs compared to the indirect inference method.

Keywords: Symmetric α-stable distribution; GARCH-type models; Indirect inference; Maximum likelihood; Leverage effects; Student’s t distribution (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (9)

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Related works:
Working Paper: Estimating Stable Factor Models By Indirect Inference (2014)
Working Paper: Indirect Estimation of α-Stable Garch Models (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:76:y:2014:i:c:p:158-171

DOI: 10.1016/j.csda.2013.07.028

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