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IGARCH models and structural breaks

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  • Guglielmo Maria Caporale
  • Nikitas Pittis
  • Nicola Spagnolo

Abstract

Using Monte Carlo simulations, it is shown that fitting a mis-specified GARCH model to a true MS-GARCH process tends to produce IGARCH parameter estimates. In other words, the presence of structural breaks can result in spuriously high estimates of the degree of persistence of shocks to the conditional variance.

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  • Guglielmo Maria Caporale & Nikitas Pittis & Nicola Spagnolo, 2003. "IGARCH models and structural breaks," Applied Economics Letters, Taylor & Francis Journals, vol. 10(12), pages 765-768.
    • Handle: RePEc:taf:apeclt:v:10:y:2003:i:12:p:765-768
    DOI: 10.1080/1350485032000138403
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    3. Shiferaw, Y., 2018. "The Bayesian MS-GARCH model and Value-at-Risk in South African agricultural commodity price markets," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 275991, International Association of Agricultural Economists.
    4. Nyoni, Thabani, 2018. "Modeling and Forecasting Inflation in Zimbabwe: a Generalized Autoregressive Conditionally Heteroskedastic (GARCH) approach," MPRA Paper 88132, University Library of Munich, Germany.
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    6. Michael Frömmel, 2010. "Volatility Regimes in Central and Eastern European Countries’ Exchange Rates," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 60(1), pages 2-21, February.
    7. Tronzano, Marco, 2009. "Assessing the Volatility of the Euro on Foreign Exchange Markets: Further Empirical Evidence and Policy Implications," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 62(1), pages 103-131.
    8. Caporale, Guglielmo Maria & Zekokh, Timur, 2019. "Modelling volatility of cryptocurrencies using Markov-Switching GARCH models," Research in International Business and Finance, Elsevier, vol. 48(C), pages 143-155.
    9. Paolella, Marc S. & Polak, Paweł, 2015. "ALRIGHT: Asymmetric LaRge-scale (I)GARCH with Hetero-Tails," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 282-297.
    10. Williams Ohemeng & Elvis Kwame Agyapong & Kenneth Ofori-Boateng, 2021. "Exchange rate and inflation dynamics: does the month or quarter of the year matter?," SN Business & Economics, Springer, vol. 1(6), pages 1-24, June.
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    12. Herrera, Ana María & Hu, Liang & Pastor, Daniel, 2018. "Forecasting crude oil price volatility," International Journal of Forecasting, Elsevier, vol. 34(4), pages 622-635.
    13. Heaney, Richard & Sriananthakumar, Sivagowry, 2012. "Time-varying correlation between stock market returns and real estate returns," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 583-594.
    14. Wang, Lu & Ma, Feng & Hao, Jianyang & Gao, Xinxin, 2021. "Forecasting crude oil volatility with geopolitical risk: Do time-varying switching probabilities play a role?," International Review of Financial Analysis, Elsevier, vol. 76(C).
    15. Dennis Koch & Vahidin Jeleskovic & Zahid I. Younas, 2024. "Modelling and Predicting the Conditional Variance of Bitcoin Daily Returns: Comparsion of Markov Switching GARCH and SV Models," Papers 2401.03393, arXiv.org, revised Jan 2024.
    16. Hu Liang & Shin Yongcheol, 2008. "Optimal Test for Markov Switching GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(3), pages 1-27, September.

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