Weighted composite quantile regression estimation of DTARCH models
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&P 500 Composite index, both of which endorse our theoretical results.
Year of publication: |
2014
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Authors: | Jiang, Jiancheng ; Jiang, Xuejun ; Song, Xinyuan |
Published in: |
Econometrics Journal. - Royal Economic Society - RES. - Vol. 17.2014, 1, p. 1-23
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Publisher: |
Royal Economic Society - RES |
Saved in:
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