IDEAS home Printed from https://ideas.repec.org/a/sbe/breart/v26y2006i1a2497.html
   My bibliography  Save this article

State Space Models for Dynamic Style Analysis of Portfolios

Author

Listed:
  • Pizzinga, Adrian
  • Fernandes, Cristiano

Abstract

This paper presents a framework and methods for the estimation of linear and non-linear state space (SS) models, occasionally subject to restrictions, to construct and estimate several models for style analysis with time varying exposures. The study is conducted by applying these models to an artificial portfolio and to return series of Brazilian investment funds. The results confirm the belief that dynamic allocations in a portfolio are a more realistic assumption for investment funds management.

Suggested Citation

  • Pizzinga, Adrian & Fernandes, Cristiano, 2006. "State Space Models for Dynamic Style Analysis of Portfolios," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 26(1), May.
    • Handle: RePEc:sbe:breart:v:26:y:2006:i:1:a:2497
    as

    Download full text from publisher

    File URL: https://periodicos.fgv.br/bre/article/view/2497
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Laurens Swinkels & Pieter Van Der Sluis, 2006. "Return-based style analysis with time-varying exposures," The European Journal of Finance, Taylor & Francis Journals, vol. 12(6-7), pages 529-552.
    2. ter Horst, Jenke R. & Nijman, Theo E. & de Roon, Frans A., 2004. "Evaluating style analysis," Journal of Empirical Finance, Elsevier, vol. 11(1), pages 29-53, January.
    3. Fuhrer, Jeffrey C, 1992. "Inferring Changes in Expectation Behavior over Time: An Application of Nonlinear Time-Varying-Parameters Estimation," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 169-177, April.
    4. Ter Horst, J.R. & Nijman, T.E. & de Roon, F.A., 2004. "Evaluating style analysis," Other publications TiSEM 8a501733-7a06-4399-8a43-0, Tilburg University, School of Economics and Management.
    5. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    6. Doran, Howard E, 1992. "Constraining Kalman Filter and Smoothing Estimates to Satisfy Time-Varying Restrictions," The Review of Economics and Statistics, MIT Press, vol. 74(3), pages 568-572, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Adrian Pizzinga, 2010. "Constrained Kalman Filtering: Additional Results," International Statistical Review, International Statistical Institute, vol. 78(2), pages 189-208, August.
    2. Pizzinga, Adrian, 2009. "Further investigation into restricted Kalman filtering," Statistics & Probability Letters, Elsevier, vol. 79(2), pages 264-269, January.

    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. Adrian Pizzinga, 2010. "Constrained Kalman Filtering: Additional Results," International Statistical Review, International Statistical Institute, vol. 78(2), pages 189-208, August.
    2. Andrew Mason & Frank McGroarty & Steve Thomas, 2012. "Style analysis for diversified US equity funds," Journal of Asset Management, Palgrave Macmillan, vol. 13(3), pages 170-185, June.
    3. Laurens Swinkels & Pieter Van Der Sluis, 2006. "Return-based style analysis with time-varying exposures," The European Journal of Finance, Taylor & Francis Journals, vol. 12(6-7), pages 529-552.
    4. Yunmi Kim & Douglas Stone & Tae-Hwan Kim, 2021. "Testing for structural breaks in return-based style regression models," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(1), pages 61-76, March.
    5. Laura Andreu & Cristina Ortiz & Jose Luis Sarto, 2009. "Herding behaviour in strategic asset allocations: new approaches on quantitative and intertemporal imitation," Applied Financial Economics, Taylor & Francis Journals, vol. 19(20), pages 1649-1659.
    6. Stephanos Papadamou & Nikolaos A. Kyriazis & Lydia Mermigka, 2017. "Japanese Mutual Funds before and after the Crisis Outburst: A Style- and Performance-Analysis," IJFS, MDPI, vol. 5(1), pages 1-20, March.
    7. Francesco Lisi, 2011. "Dicing with the market: randomized procedures for evaluation of mutual funds," Quantitative Finance, Taylor & Francis Journals, vol. 11(2), pages 163-172.
    8. Tommaso Proietti & Alessandra Luati, 2013. "Maximum likelihood estimation of time series models: the Kalman filter and beyond," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 15, pages 334-362, Edward Elgar Publishing.
    9. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2013. "On the Stratonovich – Kalman - Bucy filtering algorithm application for accurate characterization of financial time series with use of state-space model by central banks," MPRA Paper 50235, University Library of Munich, Germany.
    10. Lau, Wee Yeap & Chan, Tze-Haw, 2004. "Does Misclassification of Equity Funds Exist? Evidence from Malaysia," MPRA Paper 2029, University Library of Munich, Germany, revised 2005.
    11. Renneboog, L.D.R. & Ter Horst, J.R. & Zhang, C., 2007. "Socially Responsible Investments : Methodology, Risk Exposure and Performance," Other publications TiSEM 1ff75080-22db-4909-9f13-a, Tilburg University, School of Economics and Management.
    12. Pizzinga, Adrian & Fernandes, Cristiano & Contreras, Sergio, 2008. "Restricted Kalman filtering revisited," Journal of Econometrics, Elsevier, vol. 144(2), pages 428-429, June.
    13. Bohl, Martin T. & Kaufmann, Philipp & Stephan, Patrick M., 2013. "From hero to zero: Evidence of performance reversal and speculative bubbles in German renewable energy stocks," Energy Economics, Elsevier, vol. 37(C), pages 40-51.
    14. Rakowski, David & Shirley, Sara E. & Stark, Jeffrey R., 2017. "Tail-risk hedging, dividend chasing, and investment constraints: The use of exchange-traded notes by mutual funds," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 91-107.
    15. Martin Bohl & Philipp Kaufmann & Patrick Stephan, 2012. "From Hero to Zero: Evidence of Performance Reversal and Speculative Bubbles in German Renewable Energy Stocks," CQE Working Papers 2412, Center for Quantitative Economics (CQE), University of Muenster.
    16. Juan Matallin-Saez, 2007. "Portfolio performance: factors or benchmarks?," Applied Financial Economics, Taylor & Francis Journals, vol. 17(14), pages 1167-1178.
    17. Jun Duanmu & Yongjia Li & Alexey Malakhov, 2020. "Capturing hedge fund risk factor exposures: Hedge fund return replication with ETFs," The Financial Review, Eastern Finance Association, vol. 55(3), pages 405-431, August.
    18. Laurent Bodson & Alain Coën & Georges Hübner, 2010. "Dynamic Hedge Fund Style Analysis With Errors‐In‐Variables," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(3), pages 201-221, September.
    19. Qian, Hang, 2015. "Inequality Constrained State Space Models," MPRA Paper 66447, University Library of Munich, Germany.
    20. Enrique Sentana, 2009. "The econometrics of mean-variance efficiency tests: a survey," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 65-101, November.

    More about this item

    Statistics

    Access and download statistics

    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:sbe:breart:v:26:y:2006:i:1:a:2497. 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: Núcleo de Computação da FGV EPGE (email available below). General contact details of provider: https://edirc.repec.org/data/sbeeeea.html .

    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.