Publication Type
Working Paper
Version
publishedVersion
Publication Date
3-2019
Abstract
The linkage among the realized volatilities across component stocks are important when modeling and forecasting the relevant index volatility. In this paper, the linkage is measured via an extended Common Correlated Effects (CCE) 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 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
Research Areas
Econometrics
First Page
1
Last Page
40
Publisher
SMU Economics and Statistics Working Paper Series, Paper No. 07-2019
Citation
QIU, Yue; XIE, Tian; YU, Jun; and ZHOU, Qiankun.
Forecasting equity index volatility by measuring the linkage among component stocks. (2019). 1-40.
Available at: https://ink.library.smu.edu.sg/soe_research/2247
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.