Weighted composite quantile regression estimation of DTARCH models. Issue 1 (February 2014)

• Record Type: Journal Article
• Authors: Jiang, Jiancheng; Jiang, Xuejun; Song, Xinyuan
• Is Part Of: Econometrics journal. Volume 17:Issue 1(2014)
• Page Start: 1
• Page End: 23
• Publication Date: 2014-02
• Subjects: Econometrics -- Periodicals; 330.015195
• DOI: 10.1111/ectj.12023
• Languages: English
• ISSNs: 1368-4221

Abstract

<abstract abstract-type="main"> <title>Summary</title> <p>In modelling volatility in financial time series, the double‐threshold autoregressive conditional heteroscedastic (DTARCH) model has been demonstrated as a useful variant of the autoregressive conditional heteroscedastic (ARCH) models. In this paper, we propose a weighted composite quantile regression method for simultaneously estimating the autoregressive parameters and the ARCH parameters in the DTARCH model. This method involves a sequence of weights and takes a data‐driven weighting scheme to maximize the asymptotic efficiency of the estimators. Under regularity conditions, we establish asymptotic distributions of the proposed estimators for a variety of heavy‐ or light‐tailed error distributions. Simulations are conducted to compare the performance of different estimators, and the proposed approach is used to analyse the daily S&amp;P 500 Composite index, both of which endorse our theoretical results.</p> </abstract>