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Semi-Lévy driven continuous-time GARCH process

Author

Listed:
  • Mohammadi, M.
  • Rezakhah, S.
  • Modarresi, N.

Abstract

Continuous-time GARCH (COGARCH) processes are one of the influential and successful models in financial data analysis. In contrast to such stationary process, in this paper we study a class of COGARCH processes driven by semi-Lévy process (SL-COGARCH) that has periodically correlated (PC) increments. Under sufficient conditions the strictly periodically stationarity of the state and volatility processes are shown. We verify that the increments with constant length of the SL-COGARCH process constitute a discrete-time PC process. To justify this property, we use simulations of the SL-COGARCH(1,3) process and evaluate its increments. Then we provide the sample spectral coherence test to show the PC behavior of this discrete-time process. We apply the SL-COGARCH(2,2) process to the Dow Jones Industrial Average indices and show that this model provides better prediction of the squared log returns in compare to the retrieved Lévy driven COGARCH method.

Suggested Citation

  • Mohammadi, M. & Rezakhah, S. & Modarresi, N., 2020. "Semi-Lévy driven continuous-time GARCH process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
  • Handle: RePEc:eee:phsmap:v:557:y:2020:i:c:s037843712030443x
    DOI: 10.1016/j.physa.2020.124855
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    References listed on IDEAS

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    1. Broszkiewicz-Suwaj, E & Makagon, A & Weron, R & Wyłomańska, A, 2004. "On detecting and modeling periodic correlation in financial data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(1), pages 196-205.
    2. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    3. Constantinides, A. & Savel’ev, S.E., 2013. "Modelling price dynamics: A hybrid truncated Lévy Flight–GARCH approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2072-2078.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    6. Harry L. Hurd & Neil L. Gerr, 1991. "Graphical Methods For Determining The Presence Of Periodic Correlation," Journal of Time Series Analysis, Wiley Blackwell, vol. 12(4), pages 337-350, July.
    7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    8. Kang, Sang Hoon & Yoon, Seong-Min, 2008. "Long memory features in the high frequency data of the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5189-5196.
    9. P. A. W Lewis & G. S. Shedler, 1979. "Simulation of nonhomogeneous poisson processes by thinning," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 26(3), pages 403-413, September.
    10. S. Haug & C. Klüppelberg & A. Lindner & M. Zapp, 2007. "Method of moment estimation in the COGARCH(1,1) model," Econometrics Journal, Royal Economic Society, vol. 10(2), pages 320-341, July.
    11. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    12. Iacus, Stefano M. & Mercuri, Lorenzo & Rroji, Edit, 2017. "COGARCH(p, q): Simulation and Inference with the yuima Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 80(i04).
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