Characteristic function-based inference for GARCH models with heavy-tailed innovations
| Autor: | Violetta Dalla, Yannis Bassiakos, Simos G. Meintanis |
|---|---|
| Přispěvatelé: | 21262977 - Meintanis, Simos George |
| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Distribution (number theory) Characteristic function (probability theory) Autoregressive conditional heteroskedasticity Characteristic function 05 social sciences Inference Minimum distance estimation 01 natural sciences GARCH model Variance-gamma distribution Normal-inverse Gaussian distribution 010104 statistics & probability Heavy-tailed distribution Modeling and Simulation 0502 economics and business Statistics Econometrics 0101 mathematics 050205 econometrics Mathematics |
| Zdroj: | Communications in Statistics - Simulation and Computation. 46:2733-2755 |
| ISSN: | 1532-4141 0361-0918 |
| DOI: | 10.1080/03610918.2015.1060332 |
| Popis: | We consider estimation and goodness-of-fit tests in GARCH models with innovations following a heavy-tailed and possibly asymmetric distribution. Although the method is fairly general and applies to GARCH models with arbitrary innovation distribution, we consider as special instances the stable Paretian, the variance gamma, and the normal inverse Gaussian distribution. Exploiting the simple structure of the characteristic function of these distributions, we propose minimum distance estimation based on the empirical characteristic function of properly standardized GARCH-residuals. The finite-sample results presented facilitate comparison with existing methods, while the new procedures are also applied to real data from the financial market. |
| Databáze: | OpenAIRE |
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