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

2014/01/03 by Jungsik Noh, Noh, Jungsik, Sangyeol Lee +1
Economics, Econometrics and Finance · Environmental Science · Mathematics · #62F12 #62M10 #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Hydrology and Drought Analysis #Methodology (stat.ME) #Statistical Distribution Estimation and Applications #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1401.0688

openalex publication_date 2014/01/03 · openalex created_date 2022/10/04 · openalex updated_date 2026/07/28

Abstract

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

Citations

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