Conditional Quantile Estimation for Generalized Autoregressive Conditional Heteroscedasticity Models

By Zhijie Xiao, Roger W. Koenker. 2009

149 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/4e0f9023b515a1a7484fb956ca31f17973d2d46a

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Citations in the corpus

Frontiers in VaR forecasting and backtesting (2016, methodology): “The bias strongly depends upon the VaR level and, consequently, Boucher et al. (2014) do not recommend using procedures that use information on different quantiles to estimate the VaR as those used by, for example, Xiao and Koenker (2009), Wang et al. (2012) and Yi et al. (2014).”
Quantile Correlations and Quantile Autoregressive Modeling (2012, unknown): “For example, the diagnosis of nonlinear quantile autoregression models (e.g., Chen, Koenker, and Xiao 2009) and quantile GARCH models (Xiao and Koenker 2009) can be considered; variable screening and selection (e.g., Fan and Lv 2008; Wang 2009) in quantile regressions is another important topic for…”
Measuring Sovereign Contagion in Europe (2014, unknown): “Following Chen, Gerlack, and Wei (2009) and Xiao and Koenker (2009) we assume that the conditional variance of the residuals follows a GARCH(1,1) specification.”
Quantile Regression Estimator for GARCH Models (2013, background): “Later, Xiao & Koenker (2009) studied the quantile regression estimation for linear GARCH models.”
Risk-parameter estimation in volatility models (2012, methodology): “…is similar to those introduced in the quantile regression literature (see Koenker (2005) for a comprehensive book on quantile regression, and see Xiao and Koenker (2009), Xiao and Wan (2010) for recent applications to linear GARCH models) and in the least-absolute deviations (LAD) time series…”