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Long Memory and Structural Breaks in Realized Volatility: An Irreversible Markov Switching Approach

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  • Nima Nonejad

    ()
    (Aarhus University and CREATES)

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    Abstract

    This paper proposes a model that simultaneously captures long memory and structural breaks. We model structural breaks through irreversible Markov switching or so-called change-point dynamics. The parameters subject to structural breaks and the unobserved states which determine the position of the structural breaks are sampled from the joint posterior density by sampling from their respective conditional posteriors using Gibbs sampling and Metropolis-Hastings. Monte Carlo simulations demonstrate that the proposed estimation approach is effective in identifying and dating structural breaks. Applied to daily S&P 500 data, one finds strong evidence of three structural breaks. The evidence of these breaks is robust to different specifications including a GARCH specification for the conditional variance of volatility.

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    File URL: ftp://ftp.econ.au.dk/creates/rp/13/rp13_26.pdf
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    Bibliographic Info

    Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2013-26.

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    Length: 26
    Date of creation: 08 2013
    Date of revision:
    Handle: RePEc:aah:create:2013-26

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    Web page: http://www.econ.au.dk/afn/

    Related research

    Keywords: Long memory; Structural breaks; Change-points; Gibbs sampling;

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    1. ERIC HILLEBRAND & MArcelo Cunha Medeiros, 2010. "Asymmetries, breaks, and long-range dependence: An estimation framework for daily realized volatility," Textos para discussão 578, Department of Economics PUC-Rio (Brazil).
    2. Siem Jan Koopman & Borus Jungbacker & Eugenie Hol, 2004. "Forecasting Daily Variability of the S&P 100 Stock Index using Historical, Realised and Implied Volatility Measurements," Tinbergen Institute Discussion Papers 04-016/4, Tinbergen Institute.
    3. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 7(2), pages 174-196, Spring.
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    6. Albert, James H & Chib, Siddhartha, 1993. "Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 1-15, January.
    7. Ole E. Barndorff-Nielsen & Neil Shephard, 2000. "Econometric analysis of realised volatility and its use in estimating stochastic volatility models," Economics Papers 2001-W4, Economics Group, Nuffield College, University of Oxford, revised 05 Jul 2001.
    8. Anderson, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Labys, Paul, 2002. "Modeling and Forecasting Realized Volatility," Working Papers 02-12, Duke University, Department of Economics.
    9. Michael McAller & Marcelo C. Medeiros, 2007. "A multiple regime smooth transition heterogeneous autoregressive model for long memory and asymmetries," Textos para discussão 544, Department of Economics PUC-Rio (Brazil).
    10. S. Bordignon & D. Raggi, 2010. "Long memory and nonlinearities in realized volatility: a Markov switching approach," Working Papers 694, Dipartimento Scienze Economiche, Universita' di Bologna.
    11. Chun Liu & John M. Maheu, 2008. "Are There Structural Breaks in Realized Volatility?," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 6(3), pages 326-360, Summer.
    12. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
    13. Neil Shephard & Ole E. Barndorff-Nielsen, 2002. "Estimating quadratic variation using realised variance," Economics Series Working Papers 2001-W20, University of Oxford, Department of Economics.
    14. Martin Martens & Dick van Dijk & Michiel de Pooter, 2004. "Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity," Tinbergen Institute Discussion Papers 04-067/4, Tinbergen Institute.
    15. Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, vol. 86(2), pages 221-241, June.
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