Publication Type
Journal Article
Version
submittedVersion
Publication Date
1-2022
Abstract
The linkage among the realized volatilities of component stocks is important when modeling and forecasting the relevant index volatility. In this article, the linkage is measured via an extended Common Correlated Effects (CCEs) approach under a panel heterogeneous autoregression model where unobserved common factors in errors are assumed. Consistency of the CCE estimator is obtained. The common factors are extracted using the principal component analysis. Empirical studies show that realized volatility models exploiting the linkage effects lead to significantly better out-of-sample forecast performance, for example, an up to 32% increase in the pseudo R2. We also conduct various forecasting exercises on the linkage variables that compare conventional regression methods with popular machine learning techniques.
Keywords
volatility forecasting, heterogeneous autoregression, common correlated effect, factor analysis, random forest
Discipline
Econometrics | Portfolio and Security Analysis
Research Areas
Econometrics
Publication
Journal of Financial Econometrics
Volume
20
Issue
1
First Page
160
Last Page
186
ISSN
1479-8409
Identifier
10.1093/jjfinec/nbaa005
Publisher
Oxford University Press
Citation
QIU, Yue; XIE, Tian; Jun YU; and ZHOU, Qiankun.
Forecasting equity index volatility by measuring the linkage among component stocks. (2022). Journal of Financial Econometrics. 20, (1), 160-186.
Available at: https://ink.library.smu.edu.sg/soe_research/2611
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1093/jjfinec/nbaa005