Estimating GARCH-type models with symmetric stable innovations: Indirect inference versus maximum likelihood
| Autor: | Giorgio Calzolari, Alessandro Parrini, Roxana Halbleib |
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| Rok vydání: | 2014 |
| Předmět: |
Statistics and Probability
Heteroscedasticity Applied Mathematics Autoregressive conditional heteroskedasticity Maximum likelihood Leverage effects Conditional probability distribution Indirect Inference Symmetric α-stable distribution Indirect inference Computational Mathematics Computational Theory and Mathematics Student's t-distribution Statistics ddc:330 Econometrics Leverage (statistics) GARCH-type models Volatility (finance) Student’s t distribution Mathematics |
| Zdroj: | Computational Statistics & Data Analysis. 76:158-171 |
| ISSN: | 0167-9473 |
| Popis: | Financial returns exhibit conditional heteroscedasticity, asymmetric responses of their volatility to negative and positive returns (leverage effects) and fat tails. The αα-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 αα-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 tt distributed errors as auxiliary models is compared to the maximum likelihood approach for estimating GARCH-type models with symmetric αα-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. |
| Databáze: | OpenAIRE |
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