A Nonparametric Dynamic Network via Multivariate Quantile Autoregressions

Zongwu Cai and Xiyuan Liu
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Zongwu Cai: Department of Economics, The University of Kansas, Lawrence, KS 66045, USA
Xiyuan Liu: Department of Economics, The University of Kansas, Lawrence, KS 66045, USA

No 202209, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics

Abstract: In this article, we propose a vector autoregressive model for conditional quantiles with functional coefficients to construct a novel class of nonparametric dynamic network systems, of which the interdependences among tail risks such as Value-at-Risk are allowed to vary smoothly with a variable of general economy. Methodologically, we develop an easy-to-implement two-stage procedure to estimate functionals in the dynamic network system by the local linear smoothing technique. We establish the consistency and the asymptotic normality of the proposed estimator under strongly mixing time series settings. The simulation studies are conducted to show that our new methods work fairly well. The potential of the proposed estimation procedures is demonstrated by an empirical study of constructing and estimating a new type of nonparametric dynamic financial network.

Keywords: Conditional quantile models; Dynamic financial network; Functional coefficient models; Nonparametric estimation; VAR modeling. (search for similar items in EconPapers)
JEL-codes: C14 C45 C58 G32 (search for similar items in EconPapers)
Date: 2020-10, Revised 2022-03
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-net
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