(Lasso) VAR for Expectile

34 Pages Posted: 30 Nov 2020

See all articles by Hui-Ching Chuang

Hui-Ching Chuang

Yuan Ze University - College of Management

O-Chia Chuang

affiliation not provided to SSRN

Zaichao Du

Fudan

Zhenhong Huang

affiliation not provided to SSRN

Date Written: October 1, 2022

Abstract

Expectile has recently received considerable attention in risk management. This paper extends the vector autoregressive (VAR) for conditional means to VAR for conditional expectiles (MCARE) to capture the interdependencies among the expectiles of multiple units. We further generalize MCARE to high-dimensional cases by imposing an L1-penalization (L-MCARE). We demonstrate how to estimate the model parameters and derive the estimators' asymptotic properties. As an empirical application, we apply MCARE and L-MCARE to the 2020 list of global systemically important banks. Formal out-of-sample model evaluation tests document our models significantly outperform single equation models. We also find clear asymmetric effects of past positive and negative shocks of different units as well as their volatilities on the expectiles. Moreover, network analyses based on L-MCARE report a clear decrease in the connectedness of most banks during the recent pandemic and an increase for several banks with exceptional performance during the pandemic.

Keywords: risk spillover, expectile, Lasso, network analysis, model evaluation

JEL Classification: G21, C32, C51

Suggested Citation

Chuang, Hui-Ching and Chuang, O-Chia and Du, Zaichao and Huang, Zhenhong, (Lasso) VAR for Expectile (October 1, 2022). Available at SSRN: https://ssrn.com/abstract= or http://dx.doi.org/10.2139/ssrn.3706200

Hui-Ching Chuang (Contact Author)

Yuan Ze University - College of Management ( email )

135, Yuan-Tung Rd.
Taoyuan, 320
Taiwan
886-972-735-021 (Phone)

O-Chia Chuang

affiliation not provided to SSRN

Zaichao Du

Fudan ( email )

600 GuoQuan Rd
School of Economics, Fudan
Shanghai, Shanghai 200433
China

Zhenhong Huang

affiliation not provided to SSRN

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