Abstract
In this article, we study the quantile regression estimator for GARCH models. We formulate the quantile regression problem by a reparametrization method and verify that the obtained quantile regression estimator is strongly consistent and asymptotically normal under certain regularity conditions. We also present our simulation results and a real data analysis for illustration.
Original language | English (US) |
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Pages (from-to) | 2-20 |
Number of pages | 19 |
Journal | Scandinavian Journal of Statistics |
Volume | 40 |
Issue number | 1 |
DOIs | |
State | Published - Mar 2013 |
Keywords
- Argmin sequence
- Asymptotic normality
- Bracketing method
- GARCH models
- Non-convex optimization
- Quantile regression
- Reparametrization method
- Strong consistency
- Value at risk
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty