On the Origins of Conditional Heteroscedasticity in Time Series
The volatility clustering frequently observed in financial/economic time series is often ascribed to GARCH and/or stochastic volatility models. This paper demonstrates the usefulness of re- conceptualizing the usual definition of conditional heteroscedasticity as the (h = 1) special case of h-step-ahead conditional heteroscedasticity, where the conditional volatility in period t depends on observable variables up through period t - h. Here it is shown that, for h > 1, h-step-ahead conditional heteroscedasticity arises â€“ necessarily and endogenously â€“ from nonlinear serial dependence in a time series; whereas one-step-ahead conditional heteroscedasticity (i.e., h= 1) requires multiple and heterogeneously-skedastic innovation terms. Consequently, the best response to observed volatility clustering may often be to model the nonlinear serial dependence which is likely causing it, rather than â€˜tacking onâ€™ an ad hoc volatility model. Even where such nonlinear modeling is infeasible â€“ or where volatility is quantified using, say, a model-free implied volatility measure rather than squared returns â€“ these results suggest a re-consideration of the usefulness of lag-one terms in volatility models. An application to observed daily stock returns is given.
|Date of creation:||2010|
|Contact details of provider:|| Postal: 3016 Pamplin Hall, Blacksburg, VA 24061-0316|
Web page: http://www.econ.vt.edu
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Kyrtsou, Catherine, 2008. "Re-examining the sources of heteroskedasticity: The paradigm of noisy chaotic models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(27), pages 6785-6789.
- Kyrtsou, Catherine & Serletis, Apostolos, 2006. "Univariate tests for nonlinear structure," Journal of Macroeconomics, Elsevier, vol. 28(1), pages 154-168, March.
- Hagiwara, May & Herce, Miguel A, 1999. "Endogenous Exchange Rate Volatility, Trading Volume and Interest Rate Differentials in a Model of Portfolio Selection," Review of International Economics, Wiley Blackwell, vol. 7(2), pages 202-218, May.
- Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 119-136, Suppl. De.
- Kyrtsou, Catherine & Labys, Walter C., 2006. "Evidence for chaotic dependence between US inflation and commodity prices," Journal of Macroeconomics, Elsevier, vol. 28(1), pages 256-266, March.
- Weiss, Andrew A, 1986. "ARCH and Bilinear Time Series Models: Comparison and Combination," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 59-70, January.
- Bera, Anil K & Higgins, Matthew L, 1997. "ARCH and Bilinearity as Competing Models for Nonlinear Dependence," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 43-50, January.
- Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, number 8355.
- Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
- Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
- Catherine Kyrtsou & Michel Terraza, 2003. "Is it Possible to Study Chaotic and ARCH Behaviour Jointly? Application of a Noisy Mackey–Glass Equation with Heteroskedastic Errors to the Paris Stock Exchange Returns Series," Computational Economics, Springer;Society for Computational Economics, vol. 21(3), pages 257-276, June.
- Ashley, Richard A. & Patterson, Douglas M., 2006. "Evaluating the Effectiveness of State-Switching Time Series Models for U.S. Real Output," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 266-277, July.