Composite quantile regression estimation for P-GARCH processes

Biao Zhao, Zhao Chen, Gui Ping Tao, Min Chen

Research output: Contribution to journalArticle

Abstract

We consider the periodic generalized autoregressive conditional heteroskedasticity (P-GARCH) process and propose a robust estimator by composite quantile regression. We study some useful properties about the P-GARCH model. Under some mild conditions, we establish the asymptotic results of proposed estimator. The Monte Carlo simulation is presented to assess the performance of proposed estimator. Numerical study results show that our proposed estimation outperforms other existing methods for heavy tailed distributions. The proposed methodology is also illustrated by VaR on stock price data.

Original languageEnglish (US)
Pages (from-to)977-998
Number of pages22
JournalScience China Mathematics
Volume59
Issue number5
DOIs
StatePublished - May 1 2016

Fingerprint

Conditional Heteroskedasticity
Quantile Estimation
Regression Estimation
Quantile Regression
Composite
Estimator
Heavy-tailed Distribution
Robust Estimators
Stock Prices
Numerical Study
Monte Carlo Simulation
Methodology
Model

All Science Journal Classification (ASJC) codes

  • Mathematics(all)

Cite this

Zhao, Biao ; Chen, Zhao ; Tao, Gui Ping ; Chen, Min. / Composite quantile regression estimation for P-GARCH processes. In: Science China Mathematics. 2016 ; Vol. 59, No. 5. pp. 977-998.
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Composite quantile regression estimation for P-GARCH processes. / Zhao, Biao; Chen, Zhao; Tao, Gui Ping; Chen, Min.

In: Science China Mathematics, Vol. 59, No. 5, 01.05.2016, p. 977-998.

Research output: Contribution to journalArticle

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