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Volatility Dynamics Under Duration-Dependent Mixing

  • John M. Maheu

    (University of Alberta)

  • Tom McCurdy

    (University of Toronto)

This paper proposes a new approach to modeling volatility changes and clustering. In particular, we use a parsimonious high-order Markov chain which allows for duration dependence. As in the standard 1st-order Markov-switching model, this structure can capture turning points and shifts in volatility due, for example, to policy changes or news events. However, unlike the 1st-order model, the duration-dependent Markov switching model is suited to exploiting the persistence associated with volatility clustering. To highlight the features of our model, we compare it to a popular benchmark, the GARCH model. Unlike the latter, the proposed parameterization allows time-varying persistence, includes a stochastic component for volatility, and incorporates anticipated discrete changes in the level of volatility. The empirical distribution generated by our proposed structure works well for the samples of data used in this paper. Implications for forecasts relevant for risk management are emphasized.

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Paper provided by Econometric Society in its series Econometric Society World Congress 2000 Contributed Papers with number 1427.

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Date of creation: 01 Aug 2000
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Handle: RePEc:ecm:wc2000:1427
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  1. Durland, J Michael & McCurdy, Thomas H, 1994. "Duration-Dependent Transitions in a Markov Model of U.S. GNP Growth," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 279-88, July.
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  5. Allan Timmermann, 1999. "Moments of Markov Switching Models," FMG Discussion Papers dp323, Financial Markets Group.
  6. 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.
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  11. Pok-san Lam, 1997. "A Markov switching model of GNP growth with duration dependence," Discussion Paper / Institute for Empirical Macroeconomics 124, Federal Reserve Bank of Minneapolis.
  12. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
  13. Rydén, Tobias & Teräsvirta, Timo & Åsbrink, Stefan, 1996. "Stylized Facts of Daily Return Series and the Hidden Markov Model," SSE/EFI Working Paper Series in Economics and Finance 117, Stockholm School of Economics.
  14. Chang-Jin Kim & Charles R. Nelson, 1998. "Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 188-201, May.
  15. Nieuwland, Frederick G M C & Verschoor, Willem F C & Wolff, Christian C P, 1994. "Stochastic trends and jumps in EMS exchange rates," Journal of International Money and Finance, Elsevier, vol. 13(6), pages 699-727, December.
  16. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
  17. Kim, Chang-Jin & Nelson, Charles R. & Startz, Richard, 1998. "Testing for mean reversion in heteroskedastic data based on Gibbs-sampling-augmented randomization1," Journal of Empirical Finance, Elsevier, vol. 5(2), pages 131-154, June.
  18. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-83, November.
  19. Gray, Stephen F., 1996. "Modeling the conditional distribution of interest rates as a regime-switching process," Journal of Financial Economics, Elsevier, vol. 42(1), pages 27-62, September.
  20. Engel, Charles & Hamilton, James D, 1990. "Long Swings in the Dollar: Are They in the Data and Do Markets Know It?," American Economic Review, American Economic Association, vol. 80(4), pages 689-713, September.
  21. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 299-308, July.
  22. Chatfield, Chris, 1993. "Calculating Interval Forecasts: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 143-44, April.
  23. Pagan, A.R. & Schwert, G.W., 1989. "Alternative Models For Conditional Stock Volatility," Papers 89-02, Rochester, Business - General.
  24. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
  25. Maheu, John M & McCurdy, Thomas H, 2000. "Identifying Bull and Bear Markets in Stock Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(1), pages 100-112, January.
  26. Vlaar, Peter J G & Palm, Franz C, 1993. "The Message in Weekly Exchange Rates in the European Monetary System: Mean Reversion, Conditional Heteroscedasticity, and Jumps," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(3), pages 351-60, July.
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