IDEAS home Printed from https://ideas.repec.org/p/hal/journl/halshs-01439860.html
   My bibliography  Save this paper

Three-stage estimation method for non-linear multiple time-series

Author

Listed:
  • Dominique Guegan

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

  • Giovanni de Luca

    (Parthenope University - PARTHENOPE - Università degli Studi di Napoli “Parthenope” = University of Naples)

  • Giorgia Rivieccio

    (Parthenope University - PARTHENOPE - Università degli Studi di Napoli “Parthenope” = University of Naples)

Abstract

We present the three-stage pseudo maximum likelihood estimation in order to reduce the computational burdens when a copula-based model is applied to multiple time-series in high dimensions. The method is applied to general stationary Markov time series, under some assumptions which include a time-invariant copula as well as marginal distributions, extending the results of Yi and Liao [2010]. We explore, via simulated and real data, the performance of the model compared to the classical vectorial autoregressive model, giving the implications of misspecified assumptions for margins and/or joint distribution and providing tail dependence measures of economic variables involved in the analysis.

Suggested Citation

  • Dominique Guegan & Giovanni de Luca & Giorgia Rivieccio, 2017. "Three-stage estimation method for non-linear multiple time-series," Post-Print halshs-01439860, HAL.
    • Handle: RePEc:hal:journl:halshs-01439860
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01439860
    as

    Download full text from publisher

    File URL: https://shs.hal.science/halshs-01439860/document
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Victor DeMiguel & Francisco J. Nogales & Raman Uppal, 2014. "Stock Return Serial Dependence and Out-of-Sample Portfolio Performance," The Review of Financial Studies, Society for Financial Studies, vol. 27(4), pages 1031-1073.
    2. Lo, Andrew W & MacKinlay, A Craig, 1990. "When Are Contrarian Profits Due to Stock Market Overreaction?," The Review of Financial Studies, Society for Financial Studies, vol. 3(2), pages 175-205.
    3. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    4. Jadran Dobric & Friedrich Schmid, 2005. "Nonparametric estimation of the lower tail dependence λL in bivariate copulas," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(4), pages 387-407.
    5. Eike Christian Brechmann & Claudia Czado, 2015. "COPAR—multivariate time series modeling using the copula autoregressive model," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 31(4), pages 495-514, July.
    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. Dominique Guegan & Giovanni De Luca & Giorgia Rivieccio, 2017. "Three-stage estimation method for non-linear multiple time-series," Documents de travail du Centre d'Economie de la Sorbonne 17001, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    2. Dominique Guegan & Giovanni de Luca & Giorgia Rivieccio, 2017. "Three-stage estimation method for non-linear multiple time-series," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01439860, HAL.
    3. Luca, Giovanni De & Guégan, Dominique & Rivieccio, Giorgia, 2019. "Assessing tail risk for nonlinear dependence of MSCI sector indices: A copula three-stage approach," Finance Research Letters, Elsevier, vol. 30(C), pages 327-333.
    4. Rivieccio, Giorgia & De Luca, Giovanni, 2016. "Copula function approaches for the analysis of serial and cross dependence in stock returns," Finance Research Letters, Elsevier, vol. 17(C), pages 55-61.
    5. Kwon, Oh Kang & Satchell, Stephen, 2018. "The distribution of cross sectional momentum returns," Journal of Economic Dynamics and Control, Elsevier, vol. 94(C), pages 225-241.
    6. Shynkevich, Andrei, 2013. "Time-series momentum as an intra- and inter-industry effect: Implications for market efficiency," Journal of Economics and Business, Elsevier, vol. 69(C), pages 64-85.
    7. Takano, Yuichi & Gotoh, Jun-ya, 2023. "Dynamic portfolio selection with linear control policies for coherent risk minimization," Operations Research Perspectives, Elsevier, vol. 10(C).
    8. Nikoloulopoulos, Aristidis K. & Joe, Harry & Li, Haijun, 2012. "Vine copulas with asymmetric tail dependence and applications to financial return data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3659-3673.
    9. Yiuman Tse, 2018. "Return predictability and contrarian profits of international index futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(7), pages 788-803, July.
    10. Del Brio, Esther B. & Mora-Valencia, Andrés & Perote, Javier, 2017. "The kidnapping of Europe: High-order moments' transmission between developed and emerging markets," Emerging Markets Review, Elsevier, vol. 31(C), pages 96-115.
    11. Naranjo, Andy & Porter, Burt, 2010. "Risk factor and industry effects in the cross-country comovement of momentum returns," Journal of International Money and Finance, Elsevier, vol. 29(2), pages 275-299, March.
    12. Immo Stadtmüller & Benjamin R. Auer & Frank Schuhmacher, 2024. "Core-satellite investing with commodity futures momentum," Journal of Asset Management, Palgrave Macmillan, vol. 25(3), pages 261-287, May.
    13. Aityan, Sergey K. & Ivanov-Schitz, Alexey K. & Izotov, Sergey S., 2010. "Time-shift asymmetric correlation analysis of global stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(5), pages 590-605, December.
    14. Smith, Michael Stanley, 2023. "Implicit Copulas: An Overview," Econometrics and Statistics, Elsevier, vol. 28(C), pages 81-104.
    15. Alexander M. Chinco & Adam D. Clark-Joseph & Mao Ye, 2017. "Sparse Signals in the Cross-Section of Returns," NBER Working Papers 23933, National Bureau of Economic Research, Inc.
    16. Gregory Connor & Lisa R. Goldberg & Robert A. Korajczyk, 2010. "Portfolio Risk Analysis," Economics Books, Princeton University Press, edition 1, number 9224.
    17. Stanislav S Borysov & Alexander V Balatsky, 2014. "Cross-Correlation Asymmetries and Causal Relationships between Stock and Market Risk," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-11, August.
    18. Vargas, Gregorio A., 2006. "An Asymmetric Block Dynamic Conditional Correlation Multivariate GARCH Model," MPRA Paper 189, University Library of Munich, Germany, revised Aug 2006.
    19. Anna Scherbina & Bernd Schlusche, 2016. "Economic linkages inferred from news stories and the predictability of stock returns," AEI Economics Working Papers 873600, American Enterprise Institute.
    20. Xue-Zhong He & Kai Li & Chuncheng Wang, 2018. "Time-varying economic dominance in financial markets: A bistable dynamics approach," Published Paper Series 2018-1, Finance Discipline Group, UTS Business School, University of Technology, Sydney.

    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:hal:journl:halshs-01439860. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.