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

DOI10.1111/sjos.12199zbMath1468.62274arXiv1401.0688OpenAlexW2096361008MaRDI QIDQ2821474

Jungsik Noh, Sangyeol Lee

Publication date: 21 September 2016

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1401.0688

zbMATH Keywords

asymptotic normalityquantile regressionidentifiability conditionARMA-AGARCH modelsconditional autoregressive value-at-risk modelsconditional location-scale time series models

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (9)

Monitoring parameter change for time series models with application to location-Scale heteroscedastic models ⋮ Estimation of value-at-risk using single index quantile regression ⋮ Test for conditional quantile change in GARCH models ⋮ Risk measurement for conditionally heteroscedastic location-scale time series models with ASTD and AEPD innovations ⋮ Conditional quantile change test for time series based on support vector regression ⋮ Test for conditional quantile change in general conditional heteroscedastic time series models ⋮ Averaged Autoregression Quantiles in Autoregressive Model ⋮ Quantile Estimation of Regression Models with GARCH-X Errors ⋮ Bootstrap entropy test for general location-scale time series models with heteroscedasticity

Uses Software

• CAViaR

Cites Work

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