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Network quantile autoregression

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Network quantile autoregression. / Zhu, Xuening; Wang, Weining; Wang, Hansheng; Härdle, Wolfgang Karl .

In: Journal of Econometrics, Vol. 212, No. 1, 09.2019, p. 345-358.

Research output: Contribution to journal › Article › peer-review

Harvard

Zhu, X, Wang, W, Wang, H & Härdle, WK 2019, 'Network quantile autoregression', Journal of Econometrics, vol. 212, no. 1, pp. 345-358. https://doi.org/10.1016/j.jeconom.2019.04.034

APA

Zhu, X., Wang, W., Wang, H., & Härdle, W. K. (2019). Network quantile autoregression. Journal of Econometrics, 212(1), 345-358. https://doi.org/10.1016/j.jeconom.2019.04.034

Vancouver

Zhu X, Wang W, Wang H, Härdle WK. Network quantile autoregression. Journal of Econometrics. 2019 Sep;212(1):345-358. https://doi.org/10.1016/j.jeconom.2019.04.034

Author

Zhu, Xuening ; Wang, Weining ; Wang, Hansheng ; Härdle, Wolfgang Karl . / Network quantile autoregression. In: Journal of Econometrics. 2019 ; Vol. 212, No. 1. pp. 345-358.

Bibtex - Download

@article{402d4749754a4b1dad203e6b2619e9ab,
title = "Network quantile autoregression",
abstract = "The complex tail dependency structure in a dynamic network with a large number of nodes is an important object to study. We propose a network quantile autoregression model (NQAR), which characterizes the dynamic quantile behavior. Our NQAR model consists of a system of equations, of which we relate a response to its connected nodes and node specific characteristics in a quantile autoregression process. We show the estimation of the NQAR model and the asymptotic properties with assumptions on the network structure. For this propose we develop a network Bahadur representation that gives us direct insight into the parameter asymptotics. Moreover, innovative tail-event driven impulse functions are defined. Finally, we demonstrate the usage of our model by investigating the financial contagions in the Chinese stock market accounting for shared ownership of companies. We find higher network dependency when the market is exposed to a higher volatility level.",
author = "Xuening Zhu and Weining Wang and Hansheng Wang and H{\"a}rdle, {Wolfgang Karl}",
year = "2019",
month = sep,
doi = "10.1016/j.jeconom.2019.04.034",
language = "English",
volume = "212",
pages = "345--358",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier BV",
number = "1",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Network quantile autoregression

AU - Zhu, Xuening

AU - Wang, Weining

AU - Wang, Hansheng

AU - Härdle, Wolfgang Karl

PY - 2019/9

Y1 - 2019/9

N2 - The complex tail dependency structure in a dynamic network with a large number of nodes is an important object to study. We propose a network quantile autoregression model (NQAR), which characterizes the dynamic quantile behavior. Our NQAR model consists of a system of equations, of which we relate a response to its connected nodes and node specific characteristics in a quantile autoregression process. We show the estimation of the NQAR model and the asymptotic properties with assumptions on the network structure. For this propose we develop a network Bahadur representation that gives us direct insight into the parameter asymptotics. Moreover, innovative tail-event driven impulse functions are defined. Finally, we demonstrate the usage of our model by investigating the financial contagions in the Chinese stock market accounting for shared ownership of companies. We find higher network dependency when the market is exposed to a higher volatility level.

AB - The complex tail dependency structure in a dynamic network with a large number of nodes is an important object to study. We propose a network quantile autoregression model (NQAR), which characterizes the dynamic quantile behavior. Our NQAR model consists of a system of equations, of which we relate a response to its connected nodes and node specific characteristics in a quantile autoregression process. We show the estimation of the NQAR model and the asymptotic properties with assumptions on the network structure. For this propose we develop a network Bahadur representation that gives us direct insight into the parameter asymptotics. Moreover, innovative tail-event driven impulse functions are defined. Finally, we demonstrate the usage of our model by investigating the financial contagions in the Chinese stock market accounting for shared ownership of companies. We find higher network dependency when the market is exposed to a higher volatility level.

U2 - 10.1016/j.jeconom.2019.04.034

DO - 10.1016/j.jeconom.2019.04.034

M3 - Article

VL - 212

SP - 345

EP - 358

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 1

ER -

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