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GARCH Model with Cross-sectional Volatility; GARCHX Models

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  • Steve Satchell
  • Soosung Hwang

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.
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Suggested Citation

  • Steve Satchell & Soosung Hwang, 2001. "GARCH Model with Cross-sectional Volatility; GARCHX Models," Working Papers wp01-16, Warwick Business School, Finance Group.
  • Handle: RePEc:wbs:wpaper:wp01-16
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    3. 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.
    4. 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.
    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. 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.
    7. Monica Billio & Massimiliano Caporin & Michele Gobbo, 2006. "Flexible Dynamic Conditional Correlation multivariate GARCH models for asset allocation," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 2(2), pages 123-130, March.
    8. 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.
    9. 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.
    10. Francq, Christian & Sucarrat, Genaro, 2015. "Equation-by-Equation Estimation of a Multivariate Log-GARCH-X Model of Financial Returns," MPRA Paper 67140, University Library of Munich, Germany.
    11. Prateek Sharma & Vipul _, 2015. "Forecasting stock index volatility with GARCH models: international evidence," Studies in Economics and Finance, Emerald Group Publishing, vol. 32(4), pages 445-463, October.
    12. Mauro Bernardi & Leopoldo Catania, 2016. "Portfolio Optimisation Under Flexible Dynamic Dependence Modelling," Papers 1601.05199, arXiv.org.

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