Quantile Regression for Location-Scale Time Series Models with Conditional Heteroscedasticity

Jungsik Noh; Sangyeol Lee

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arXiv · 1401.0688

Quantile Regression for Location-Scale Time Series Models with Conditional Heteroscedasticity

Jungsik Noh·Sangyeol Lee

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Abstract

This paper considers quantile regression for a wide class of time series models including ARMA models with asymmetric GARCH (AGARCH) errors. The classical mean-variance models are reinterpreted as conditional location-scale models so that the quantile regression method can be naturally geared into the considered models. The consistency and asymptotic normality of the quantile regression estimator is established in location-scale time series models under mild conditions. In the application of this result to ARMA-AGARCH models, more primitive conditions are deduced to obtain the asymptotic properties. For illustration, a simulation study and a real data analysis are provided.

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BibTeX RIS

Jungsik Noh, Sangyeol Lee. 2014-01-03. Quantile Regression for Location-Scale Time Series Models with Conditional Heteroscedasticity. https://arxiv.org/abs/1401.0688

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