Journal Published Online: 09 November 2015
Volume 44, Issue 5

Efficient and Robust Estimation of GARCH Models



Generalized autoregressive conditional heteroscedastic (GARCH) models have been a powerful tool for modeling volatility. In this paper, we propose an efficient and robust method for estimating the parameters of GARCH models. This method involves a sequence of weights and takes a data-driven weighting scheme to maximize the asymptotic efficiency of the estimators. Under regularity conditions, we establish asymptotic distributions of the proposed estimators for a variety of heavy- or light-tailed error distributions. Simulations endorse our theoretical results. Our approach is applied to analyze the S&P 500 Composite index in the U.S. financial market and run some regression diagnostics to validate the fitted model.

Author Information

Jiang, X.
South Univ. of Science and Technology, CN
Song, X.
The Chinese Univ. of Hong Kong, HK
Xiong, Z.
Business School of Hunan Univ. and Jishou Univ., CN
Pages: 12
Price: $25.00
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Stock #: JTE20140313
ISSN: 0090-3973
DOI: 10.1520/JTE20140313