IDEAS home Printed from https://ideas.repec.org/a/taf/quantf/v12y2012i10p1533-1546.html
   My bibliography  Save this article

Trending time-varying coefficient market models

Author

Listed:
  • Chongshan Zhang
  • Xiangrong Yin

Abstract

In this paper we study time-varying coefficient (beta coefficient) models with a time trend function to characterize the nonlinear, non-stationary and trending phenomenon in time series and to explain the behavior of asset returns. The general local polynomial method is developed to estimate the time trend and coefficient functions. More importantly, a graphical tool, the plot of the k th-order derivative of the parameter versus time, is proposed to select the proper order of the local polynomial so that the best estimate can be obtained. Finally, we conduct Monte Carlo experiments and a real data analysis to examine the finite sample performance of the proposed modeling procedure and compare it with the Nadaraya--Watson method as well as the local linear method.

Suggested Citation

  • Chongshan Zhang & Xiangrong Yin, 2012. "Trending time-varying coefficient market models," Quantitative Finance, Taylor & Francis Journals, vol. 12(10), pages 1533-1546, October.
    • Handle: RePEc:taf:quantf:v:12:y:2012:i:10:p:1533-1546
    DOI: 10.1080/14697688.2011.552918
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/14697688.2011.552918
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/14697688.2011.552918?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. Franses, Philip Hans, 1996. "Periodicity and Stochastic Trends in Economic Time Series," OUP Catalogue, Oxford University Press, number 9780198774549.
    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. Yue, Mu & Li, Jialiang & Cheng, Ming-Yen, 2019. "Two-step sparse boosting for high-dimensional longitudinal data with varying coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 222-234.

    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. Franses, P.H. & McAleer, M., 1995. "Testing Nested and Non-Nested Periodically Integrated Autoregressive Models," Papers 9510, Tilburg - Center for Economic Research.
    2. Elizabeth Bucacos, 2008. "Real (effective) exchange rate in Uruguay: a periodic cointegration approach," Monetaria, CEMLA, vol. 0(2), pages 265-289, julio-sep.
    3. Jonathan Aylen & Kevin Albertson & Gina Cavan, 2014. "The impact of weather and climate on tourist demand: the case of Chester Zoo," Climatic Change, Springer, vol. 127(2), pages 183-197, November.
    4. Beenstock, Michael & Reingewertz, Yaniv & Paldor, Nathan, 2016. "Testing the historic tracking of climate models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1234-1246.
    5. Łukasz Lenart & Błażej Mazur, 2016. "On Bayesian Inference for Almost Periodic in Mean Autoregressive Models," FindEcon Chapters: Forecasting Financial Markets and Economic Decision-Making, in: Magdalena Osińska (ed.), Statistical Review, vol. 63, 2016, 3, edition 1, volume 63, chapter 1, pages 255-272, University of Lodz.
    6. Yorghos Tripodis & Jeremy Penzer, 2009. "Modelling time series with season-dependent autocorrelation structure," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 559-574.
    7. Man, K. S., 2004. "Linear prediction of temporal aggregates under model misspecification," International Journal of Forecasting, Elsevier, vol. 20(4), pages 659-670.
    8. Aknouche, Abdelhakim, 2024. "Periodically homogeneous Markov chains: The discrete state space case," MPRA Paper 122287, University Library of Munich, Germany.
    9. A. Christian Silva & Ju-Yi Yen, 2010. "Stochastic resonance and the trade arrival rate of stocks," Quantitative Finance, Taylor & Francis Journals, vol. 10(5), pages 461-466.
    10. M. Angeles Carnero & Siem Jan Koopman & Marius Ooms, 2003. "Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices," Tinbergen Institute Discussion Papers 03-071/4, Tinbergen Institute.
    11. Artur Silva Lopes, 2006. "Deterministic seasonality in Dickey–Fuller tests: should we care?," Empirical Economics, Springer, vol. 31(1), pages 165-182, March.
    12. Olexa, Michal, 1999. "Analysis and Econometric Modelling of the Fiscal Sector in the Slovak Republic," Transition Economics Series 2, Institute for Advanced Studies.
    13. Melard, G. & Pasteels, J. -M., 2000. "Automatic ARIMA modeling including interventions, using time series expert software," International Journal of Forecasting, Elsevier, vol. 16(4), pages 497-508.
    14. Gabriel Pons Rotger, 2004. "Seasonal Unit Root Testing Based on the Temporal Aggregation of Seasonal Cycles," Economics Working Papers 2004-1, Department of Economics and Business Economics, Aarhus University.
    15. Bradley Michael D. & Jansen Dennis W., 2000. "Are Business Cycle Dynamics the Same across Countries? Testing Linearity around the Globe," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 4(2), pages 1-23, July.
    16. Dennis Fok & Philip Hans Franses, 2013. "Testing earnings management," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(3), pages 281-292, August.
    17. Emanuela Marrocu, 2006. "An Investigation of the Effects of Data Transformation on Nonlinearity," Empirical Economics, Springer, vol. 31(4), pages 801-820, November.
    18. Kloek, T., 1998. "Loss development forecasting models: an econometrician's view," Insurance: Mathematics and Economics, Elsevier, vol. 23(3), pages 251-261, December.
    19. Svend Hylleberg, 2006. "Seasonal Adjustment," Economics Working Papers 2006-04, Department of Economics and Business Economics, Aarhus University.
    20. Clements, Michael P. & Hendry, David F., 1997. "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, Elsevier, vol. 13(3), pages 341-355, September.

    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:taf:quantf:v:12:y:2012:i:10:p:1533-1546. 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/RQUF20 .

    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.