Modeling Financial Volatility in the Presence of Abrupt Changes
The volatility of financial instruments is rarely constant, and usually varies over time. This creates a phenomenon called volatility clustering, where large price movements on one day are followed by similarly large movements on successive days, creating temporal clusters. The GARCH model, which treats volatility as a drift process, is commonly used to capture this behavior. However research suggests that volatility is often better described by a structural break model, where the volatility undergoes abrupt jumps in addition to drift. Most efforts to integrate these jumps into the GARCH methodology have resulted in models which are either very computationally demanding, or which make problematic assumptions about the distribution of the instruments, often assuming that they are Gaussian. We present a new approach which uses ideas from nonparametric statistics to identify structural break points without making such distributional assumptions, and then models drift separately within each identified regime. Using our method, we investigate the volatility of several major stock indexes, and find that our approach can potentially give an improved fit compared to more commonly used techniques.
|Date of creation:||Dec 2012|
|Date of revision:|
|Publication status:||Published in Physica A: Statistical Mechanics and its Applications (2013). 192(2) 350-360|
|Contact details of provider:|| Web page: http://arxiv.org/|
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- Fernandez, Viviana & Lucey, Brian M., 2007. "Portfolio management under sudden changes in volatility and heterogeneous investment horizons," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(2), pages 612-624.
- David E. Rapach & Jack K. Strauss, 2008. "Structural breaks and GARCH models of exchange rate volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-90.
- Hamilton, James D. & Susmel, Raul, 1994.
"Autoregressive conditional heteroskedasticity and changes in regime,"
Journal of Econometrics,
Elsevier, vol. 64(1-2), pages 307-333.
- Tom Doan, . "RATS programs to estimate Hamilton-Susmel Markov Switching ARCH model," Statistical Software Components RTZ00083, Boston College Department of Economics.
- Tim Bollerslev, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
EERI Research Paper Series
EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Andreu Sansó & Vicent Aragó & Josep Lluís Carrion, 2003. "Testing for Changes in the Unconditional Variance of Financial Time Series," DEA Working Papers 5, Universitat de les Illes Balears, Departament d'Economía Aplicada.
- Farooq Malik & Bradley Ewing & James Payne, 2005. "Measuring volatility persistence in the presence of sudden changes in the variance of Canadian stock returns," Canadian Journal of Economics, Canadian Economics Association, vol. 38(3), pages 1037-1056, August.
- Robert Engle, 2001. "GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 157-168, Fall.
- Covarrubias, Guillermo & Ewing, Bradley T. & Hein, Scott E. & Thompson, Mark A., 2006. "Modeling volatility changes in the 10-year Treasury," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 737-744.
- Jones, E Philip & Mason, Scott P & Rosenfeld, Eric, 1984. " Contingent Claims Analysis of Corporate Capital Structures: An Empirical Investigation," Journal of Finance, American Finance Association, vol. 39(3), pages 611-25, July.
- Stanley, H. Eugene & Plerou, Vasiliki & Gabaix, Xavier, 2008. "A statistical physics view of financial fluctuations: Evidence for scaling and universality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(15), pages 3967-3981.
- Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-47, August.
- Aggarwal, Reena & Inclan, Carla & Leal, Ricardo, 1999. "Volatility in Emerging Stock Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(01), pages 33-55, March.
- Kang, Sang Hoon & Cheong, Chongcheul & Yoon, Seong-Min, 2011. "Structural changes and volatility transmission in crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4317-4324.
- Kang, Sang Hoon & Cho, Hwan-Gue & Yoon, Seong-Min, 2009. "Modeling sudden volatility changes: Evidence from Japanese and Korean stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3543-3550.
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