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Modelling and forecasting level shifts in absolute returns

Author

Listed:
  • Richard Paap

    (Econometric Institute, Erasmus University Rotterdam, The Netherlands)

  • Philip Hans Franses

    (Econometric Institute, Erasmus University Rotterdam, The Netherlands)

  • Marco Van Der Leij

    (Tinbergen Institute, Erasmus University Rotterdam, The Netherlands)

Abstract

Due to high and low volatility periods, time series of absolute returns experience temporary level shifts which differ in length and size. In this paper we modify the basic Censored Latent Effects Autoregressive [CLEAR] model, such that it can describe and forecast the location and size of such level shifts. For our particular application, we assume that technical trading variables may have explanatory value for future level shifts, where these effects may differ across upward- or downward-tending markets. A natural competitor of the resultant switching regime CLEAR [SR-CLEAR] model is a long-memory model, which is known to pick up neglected level shifts. Hence, when we apply the SR-CLEAR model to nine stock markets and document its good fit and forecasting ability, we compare it with a long-memory model. Copyright © 2002 John Wiley & Sons, Ltd.

Suggested Citation

  • Richard Paap & Philip Hans Franses & Marco Van Der Leij, 2002. "Modelling and forecasting level shifts in absolute returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 601-616.
    • Handle: RePEc:jae:japmet:v:17:y:2002:i:5:p:601-616
    DOI: 10.1002/jae.690
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    References listed on IDEAS

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    Cited by:

    1. Broto, Carmen & Ruiz, Esther, 2006. "Unobserved component models with asymmetric conditional variances," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2146-2166, May.
    2. Boutahar, Mohamed & Mootamri, Imène & Péguin-Feissolle, Anne, 2009. "A fractionally integrated exponential STAR model applied to the US real effective exchange rate," Economic Modelling, Elsevier, vol. 26(2), pages 335-341, March.
    3. Wang, Yudong & Wei, Yu & Wu, Chongfeng, 2010. "Auto-correlated behavior of WTI crude oil volatilities: A multiscale perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5759-5768.
    4. Dufrenot, Gilles & Guegan, Dominique & Peguin-Feissolle, Anne, 2005. "Long-memory dynamics in a SETAR model - applications to stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 15(5), pages 391-406, December.
    5. Dufrenot, Gilles & Guegan, Dominique & Peguin-Feissolle, Anne, 2005. "Modelling squared returns using a SETAR model with long-memory dynamics," Economics Letters, Elsevier, vol. 86(2), pages 237-243, February.
    6. VAN BELLEGEM, Sébastien, 2011. "Locally stationary volatility modelling," LIDAM Discussion Papers CORE 2011041, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
    8. de Pooter, M.D. & van Dijk, D.J.C., 2004. "Testing for changes in volatility in heteroskedastic time series - a further examination," Econometric Institute Research Papers EI 2004-38, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    9. Mohamed Boutahar & Imene Mootamri & Anne Peguin-Feissolle, 2007. "An exponential FISTAR model applied to the US real effective exchange rate," Working Papers halshs-00353836, HAL.

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