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GARCH model with cross-sectional volatility: GARCHX models

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  • Soosung Hwang
  • Steve Satchell

Abstract

This study introduces GARCH models with cross-sectional market volatility, which are called GARCHX models. The cross-sectional market volatility is a special case of common heteroscedasticity in asset specific returns, which is suggested by Connor and Linton (2001) as an important component in individual asset volatility. Using UK and US data, we find that daily return volatility can be better specified with GARCHX models, but GARCHX models do not necessarily perform better than conventional GARCH models in forecasting.

Suggested Citation

  • Soosung Hwang & Steve Satchell, 2005. "GARCH model with cross-sectional volatility: GARCHX models," Applied Financial Economics, Taylor & Francis Journals, vol. 15(3), pages 203-216.
    • Handle: RePEc:taf:apfiec:v:15:y:2005:i:3:p:203-216
    DOI: 10.1080/0960310042000314214
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    Cited by:

    1. Jiawei Du, 2020. "A Research on Cross-sectional Return Dispersion and Volatility of US Stock Market during COVID-19," Papers 2007.11546, arXiv.org, revised Mar 2021.
    2. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    3. Dufrénot, Gilles & Mignon, Valérie & Péguin-Feissolle, Anne, 2011. "The effects of the subprime crisis on the Latin American financial markets: An empirical assessment," Economic Modelling, Elsevier, vol. 28(5), pages 2342-2357, September.
    4. Bernardi, Mauro & Catania, Leopoldo, 2018. "Portfolio optimisation under flexible dynamic dependence modelling," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 1-18.
    5. Heejoon Han & Dennis Kristensen, 2014. "Asymptotic Theory for the QMLE in GARCH-X Models With Stationary and Nonstationary Covariates," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 416-429, July.
    6. Thierry Ane, 2006. "Short and long term components of volatility in Hong Kong stock returns," Applied Financial Economics, Taylor & Francis Journals, vol. 16(6), pages 439-460.
    7. Huang, Junbo & Tian, Huiting & Shen, Weibing, 2023. "Characteristics and mechanisms of the U.S. stock market spillover effects on the Chinese A-share market: Evidence from 6 A-share broad-based and 31 sector indices," International Review of Financial Analysis, Elsevier, vol. 87(C).
    8. Francq, Christian & Sucarrat, Genaro, 2017. "An equation-by-equation estimator of a multivariate log-GARCH-X model of financial returns," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 16-32.
    9. Jacobs, Michael & Karagozoglu, Ahmet K., 2014. "On the characteristics of dynamic correlations between asset pairs," Research in International Business and Finance, Elsevier, vol. 32(C), pages 60-82.
    10. Byun, Sung Je, 2016. "The usefulness of cross-sectional dispersion for forecasting aggregate stock price volatility," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 162-180.
    11. Pedro L. Valls Pereira, 2004. "How Persistent is Volatility? An Answer with Stochastic Volatility Models with Markov Regime Switching State Equations," Finance Lab Working Papers flwp_59, Finance Lab, Insper Instituto de Ensino e Pesquisa.
    12. Pineda, Julián & Cortés, Lina M. & Perote, Javier, 2022. "Financial contagion drivers during recent global crises," Economic Modelling, Elsevier, vol. 117(C).
    13. Habibeh Sherafatmand & Saeed Yazdani, 2014. "The management of price risk in Iranian dates: An application of futures instruments," Cogent Economics & Finance, Taylor & Francis Journals, vol. 2(1), pages 1-12, December.
    14. T. Kalantzis & D. Papanastassiou, 2008. "Classification of GARCH time series: an empirical investigation," Applied Financial Economics, Taylor & Francis Journals, vol. 18(9), pages 759-764.
    15. Prateek Sharma & Vipul _, 2015. "Forecasting stock index volatility with GARCH models: international evidence," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 32(4), pages 445-463, October.
    16. Mauro Bernardi & Leopoldo Catania, 2016. "Portfolio Optimisation Under Flexible Dynamic Dependence Modelling," Papers 1601.05199, arXiv.org.
    17. Fei, Tianlun & Liu, Xiaoquan & Wen, Conghua, 2019. "Cross-sectional return dispersion and volatility prediction," Pacific-Basin Finance Journal, Elsevier, vol. 58(C).

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