Cross-sectional return dispersion and volatility prediction
We use intraday and daily data to examine the impact of cross-sectional return dispersion on volatility forecasting in the Chinese equity market. We adopt the GARCH, GJR-GARCH, and HAR models and, by augmenting them with return dispersion measures, provide empirical evidence that the return dispersi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/59531/ |
| _version_ | 1848799642846756864 |
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| author | Fei, Tianlun Liu, Xiaoquan Wen, Conghua |
| author_facet | Fei, Tianlun Liu, Xiaoquan Wen, Conghua |
| author_sort | Fei, Tianlun |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | We use intraday and daily data to examine the impact of cross-sectional return dispersion on volatility forecasting in the Chinese equity market. We adopt the GARCH, GJR-GARCH, and HAR models and, by augmenting them with return dispersion measures, provide empirical evidence that the return dispersion exhibits substantial information in describing the volatility dynamics by generating signicantly lower forecasting errors at market and industry levels. Furthermore, the information content of the return dispersion tends to o er economic gain to a mean-variance
utility investor. The ndings are robust with respect to alternative volatility proxies, subsample analysis, and alternative market-wide stock indices. |
| first_indexed | 2025-11-14T20:38:55Z |
| format | Article |
| id | nottingham-59531 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:38:55Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-595312019-12-11T01:33:56Z https://eprints.nottingham.ac.uk/59531/ Cross-sectional return dispersion and volatility prediction Fei, Tianlun Liu, Xiaoquan Wen, Conghua We use intraday and daily data to examine the impact of cross-sectional return dispersion on volatility forecasting in the Chinese equity market. We adopt the GARCH, GJR-GARCH, and HAR models and, by augmenting them with return dispersion measures, provide empirical evidence that the return dispersion exhibits substantial information in describing the volatility dynamics by generating signicantly lower forecasting errors at market and industry levels. Furthermore, the information content of the return dispersion tends to o er economic gain to a mean-variance utility investor. The ndings are robust with respect to alternative volatility proxies, subsample analysis, and alternative market-wide stock indices. 2019-12-31 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/59531/1/CSD.pdf Fei, Tianlun, Liu, Xiaoquan and Wen, Conghua (2019) Cross-sectional return dispersion and volatility prediction. Pacific-Basin Finance Journal, 58 . p. 101218. ISSN 0927-538X Industry Effect; Chinese CSI Index; Herding; Financial Markets. http://dx.doi.org/10.1016/j.pacfin.2019.101218 doi:10.1016/j.pacfin.2019.101218 doi:10.1016/j.pacfin.2019.101218 |
| spellingShingle | Industry Effect; Chinese CSI Index; Herding; Financial Markets. Fei, Tianlun Liu, Xiaoquan Wen, Conghua Cross-sectional return dispersion and volatility prediction |
| title | Cross-sectional return dispersion and volatility prediction |
| title_full | Cross-sectional return dispersion and volatility prediction |
| title_fullStr | Cross-sectional return dispersion and volatility prediction |
| title_full_unstemmed | Cross-sectional return dispersion and volatility prediction |
| title_short | Cross-sectional return dispersion and volatility prediction |
| title_sort | cross-sectional return dispersion and volatility prediction |
| topic | Industry Effect; Chinese CSI Index; Herding; Financial Markets. |
| url | https://eprints.nottingham.ac.uk/59531/ https://eprints.nottingham.ac.uk/59531/ https://eprints.nottingham.ac.uk/59531/ |