Forecasting Equity Index Volatility by Measuring the Linkage among Component Stocks
Tian Xie (),
Jun Yu () and
Additional contact information
Tian Xie: School of Economics, Singapore Management University
No 7-2019, Economics and Statistics Working Papers from Singapore Management University, School of Economics
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 (search for similar items in EconPapers)
JEL-codes: C31 C32 G12 G17 (search for similar items in EconPapers)
Pages: 40 pages
New Economics Papers: this item is included in nep-big, nep-ecm, nep-for and nep-sea
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
https://ink.library.smu.edu.sg/soe_research/2247/ Full text (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ris:smuesw:2019_007
Access Statistics for this paper
More papers in Economics and Statistics Working Papers from Singapore Management University, School of Economics 90 Stamford Road, Sigapore 178903. Contact information at EDIRC.
Bibliographic data for series maintained by Cheong Pei Qi ().