IDEAS home Printed from https://ideas.repec.org/a/taf/eurjfi/v7y2001i4p289-311.html
   My bibliography  Save this article

Dynamic local models for segmentation and prediction of financial time series

Author

Listed:
  • Mehdi Azzouzi
  • Ian Nabney

Abstract

In the analysis and prediction of many real-world time series, the assumption of stationarity is not valid. Aspecial form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We introduce a new model which combines a dynamic switching (controlled by a hidden Markov model) and a non-linear dynamical system. We show how to train this hybrid model in a maximum likelihood approach and evaluate its performance on both synthetic and financial data.

Suggested Citation

  • Mehdi Azzouzi & Ian Nabney, 2001. "Dynamic local models for segmentation and prediction of financial time series," The European Journal of Finance, Taylor & Francis Journals, vol. 7(4), pages 289-311.
    • Handle: RePEc:taf:eurjfi:v:7:y:2001:i:4:p:289-311
    DOI: 10.1080/13518470110071155
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/13518470110071155
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13518470110071155?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
    2. Ataurima Arellano, Miguel & Rodríguez, Gabriel, 2020. "Empirical modeling of high-income and emerging stock and Forex market return volatility using Markov-switching GARCH models," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    3. Randolph & Xiao Qin & Tan Gee Kwang, 2004. "Unit Root Tests with Markov-Switching," Econometric Society 2004 Australasian Meetings 145, Econometric Society.
    4. Misiorek Adam & Trueck Stefan & Weron Rafal, 2006. "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-36, September.
    5. Mark Coppejans & Donna Gilleskie & Holger Sieg & Koleman Strumpf, 2007. "Consumer Demand under Price Uncertainty: Empirical Evidence from the Market for Cigarettes," The Review of Economics and Statistics, MIT Press, vol. 89(3), pages 510-521, August.
    6. Li, J. & Chavas, J.-P., 2018. "How Have China s Agricultural Price Support Policies Affected Market Prices?: A Quantile Regression Evaluation," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277557, International Association of Agricultural Economists.
    7. Rossen Anja, 2016. "On the Predictive Content of Nonlinear Transformations of Lagged Autoregression Residuals and Time Series Observations," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(3), pages 389-409, May.
    8. Pelletier, Denis, 2006. "Regime switching for dynamic correlations," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 445-473.
    9. Krishnamurthy, Vikram & Leoff, Elisabeth & Sass, Jörn, 2018. "Filterbased stochastic volatility in continuous-time hidden Markov models," Econometrics and Statistics, Elsevier, vol. 6(C), pages 1-21.
    10. Ariannejad , Aghil & Tehrani , Reza, 2021. "Study on Gold as a Hedge or Safe Haven for the Stock Market by a Markov Switching Approach," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 16(3), pages 377-398, September.
    11. Margherita Velucchi, 2009. "Regime switching: Italian financial markets over a century," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(1), pages 67-86, March.
    12. Bernardi, Mauro & Catania, Leopoldo, 2018. "Portfolio optimisation under flexible dynamic dependence modelling," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 1-18.
    13. Patton, Andrew J. & Timmermann, Allan, 2007. "Properties of optimal forecasts under asymmetric loss and nonlinearity," Journal of Econometrics, Elsevier, vol. 140(2), pages 884-918, October.
    14. Hematizadeh, Roksana & Tajaddini, Reza, 2024. "A state-dependent international CAPM for partially integrated markets: Using local and US risk factors," Global Finance Journal, Elsevier, vol. 62(C).
    15. Candelon, Bertrand & Piplack, Jan & Straetmans, Stefan, 2008. "On measuring synchronization of bulls and bears: The case of East Asia," Journal of Banking & Finance, Elsevier, vol. 32(6), pages 1022-1035, June.
    16. Gianna Boero & Emanuela Marrocu, 2005. "Evaluating non-linear models on point and interval forecasts: an application with exchange rates," BNL Quarterly Review, Banca Nazionale del Lavoro, vol. 58(232), pages 91-120.
    17. Mihaela Craioveanu & Eric Hillebrand, 2012. "Level changes in volatility models," Annals of Finance, Springer, vol. 8(2), pages 277-308, May.
    18. Martinez Oscar & Olmo Jose, 2012. "A Nonlinear Threshold Model for the Dependence of Extremes of Stationary Sequences," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(3), pages 1-39, September.
    19. Kosater, Peter & Mosler, Karl, 2006. "Can Markov regime-switching models improve power-price forecasts? Evidence from German daily power prices," Applied Energy, Elsevier, vol. 83(9), pages 943-958, September.
    20. Naifar, Nader, 2011. "What explains default risk premium during the financial crisis? Evidence from Japan," Journal of Economics and Business, Elsevier, vol. 63(5), pages 412-430, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:eurjfi:v:7:y:2001:i:4:p:289-311. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/REJF20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.