IDEAS home Printed from https://ideas.repec.org/p/zbw/iwqwdp/012011.html
   My bibliography  Save this paper

Weighted power mean copulas: Theory and application

Author

Listed:
  • Klein, Ingo
  • Fischer, Matthias J.
  • Pleier, Thomas

Abstract

It is well known that the arithmetic mean of two possibly different copulas forms a copula, again. More general, we focus on the weighted power mean (WPM) of two arbitrary copulas which is not necessary a copula again, as different counterexamples reveal. However, various conditions regarding the mean function and the underlying copula are given which guarantee that a proper copula (so-called WPM copula) results. In this case, we also derive dependence properties of WPM copulas and give some brief application to financial return series.

Suggested Citation

  • Klein, Ingo & Fischer, Matthias J. & Pleier, Thomas, 2011. "Weighted power mean copulas: Theory and application," FAU Discussion Papers in Economics 01/2011, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, revised 2011.
    • Handle: RePEc:zbw:iwqwdp:012011
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/50916/1/671661159.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Feicht, Robert & Stummer, Wolfgang, 2010. "Complete closed-form solution to a stochastic growth model and corresponding speed of economic recovery," FAU Discussion Papers in Economics 05/2010, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    2. Matthias Fischer & Christian Kock & Stephan Schluter & Florian Weigert, 2009. "An empirical analysis of multivariate copula models," Quantitative Finance, Taylor & Francis Journals, vol. 9(7), pages 839-854.
    3. Mosthaf, Alexander & Schnabel, Claus & Stephani, Jens, 2011. "Low-wage careers: Are there dead-end firms and dead-end jobs?," Zeitschrift für ArbeitsmarktForschung - Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 43(3), pages 231-249.
    4. Fischer, Matthias J. & Gao, Yang & Herrmann, Klaus, 2010. "Volatility models with innovations from new maximum entropy densities at work," FAU Discussion Papers in Economics 03/2010, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    5. Fischer, Matthias J. & Klein, Ingo, 2007. "Some results on weak and strong tail dependence coefficients for means of copulas," Discussion Papers 78/2007, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Statistics and Econometrics.
    6. Robert Feicht & Wolfgang Stummer, 2010. "Complete Closed-form Solution to a Stochastic Growth Model and Corresponding Speed of Economic Recovery preliminary," DEGIT Conference Papers c015_041, DEGIT, Dynamics, Economic Growth, and International Trade.
    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. Klein, Ingo & Christa, Florian, 2011. "Families of copulas closed under the construction of generalized linear means," FAU Discussion Papers in Economics 04/2011, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    2. Schnitzlein, Daniel D., 2012. "How important is cultural background for the level of intergenerational mobility?," Economics Letters, Elsevier, vol. 114(3), pages 335-337.
    3. Hakim Bekrizadeh & Babak Jamshidi, 2017. "A new class of bivariate copulas: dependence measures and properties," METRON, Springer;Sapienza Università di Roma, vol. 75(1), pages 31-50, April.

    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. Schnitzlein, Daniel D., 2012. "How important is cultural background for the level of intergenerational mobility?," Economics Letters, Elsevier, vol. 114(3), pages 335-337.
    2. Tinkl, Fabian, 2010. "A note on Hadamard differentiability and differentiability in quadratic mean," FAU Discussion Papers in Economics 08/2010, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    3. Herbst, Anthony F. & Wu, Joseph S.K. & Ho, Chi Pui, 2012. "Relationship between risk attitude and economic recovery in optimal growth theory," Global Finance Journal, Elsevier, vol. 23(3), pages 141-150.
    4. Nagler Thomas & Schellhase Christian & Czado Claudia, 2017. "Nonparametric estimation of simplified vine copula models: comparison of methods," Dependence Modeling, De Gruyter, vol. 5(1), pages 99-120, January.
    5. Weiß, Gregor N.F. & Scheffer, Marcus, 2015. "Mixture pair-copula-constructions," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 175-191.
    6. Koliai, Lyes, 2016. "Extreme risk modeling: An EVT–pair-copulas approach for financial stress tests," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 1-22.
    7. Hemei Li & Zhenya Liu & Shixuan Wang, 2022. "Vines climbing higher: Risk management for commodity futures markets using a regular vine copula approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2438-2457, April.
    8. Hirsch, Boris & Schnabel, Claus, 2011. "Let's Take Bargaining Models Seriously: The Decline in Union Power in Germany, 1992-2009," IZA Discussion Papers 5875, Institute of Labor Economics (IZA).
    9. Grothe, Oliver & Schnieders, Julius, 2011. "Spatial Dependence in Wind and Optimal Wind Power Allocation: A Copula Based Analysis," EWI Working Papers 2011-5, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    10. Gregor Weiß, 2013. "Copula-GARCH versus dynamic conditional correlation: an empirical study on VaR and ES forecasting accuracy," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 179-202, August.
    11. He, Fuli & Yarahmadi, Ali & Soleymani, Fazlollah, 2024. "Investigation of multivariate pairs trading under copula approach with mixture distribution," Applied Mathematics and Computation, Elsevier, vol. 472(C).
    12. Jianxi Su & Edward Furman, 2016. "Multiple risk factor dependence structures: Copulas and related properties," Papers 1610.02126, arXiv.org.
    13. Siburg, Karl Friedrich & Stoimenov, Pavel & Weiß, Gregor N.F., 2015. "Forecasting portfolio-Value-at-Risk with nonparametric lower tail dependence estimates," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 129-140.
    14. Alexander Mosthaf, 2014. "Do Scarring Effects of Low-Wage Employment and Non-Employment Differ BETWEEN Levels of Qualification?," Scottish Journal of Political Economy, Scottish Economic Society, vol. 61(2), pages 154-177, May.
    15. Matthias Fischer & Daniel Kraus & Marius Pfeuffer & Claudia Czado, 2017. "Stress Testing German Industry Sectors: Results from a Vine Copula Based Quantile Regression," Risks, MDPI, vol. 5(3), pages 1-13, July.
    16. Anna Baranowska-Rataj & Zoltán Elekes & Rikard Eriksson, 2021. "Escaping from Low-Wage Employment: The Role of Co-worker Networks," CERS-IE WORKING PAPERS 2123, Institute of Economics, Centre for Economic and Regional Studies.
    17. Righi, Marcelo Brutti & Ceretta, Paulo Sergio, 2013. "Risk prediction management and weak form market efficiency in Eurozone financial crisis," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 384-393.
    18. Zhou, Rui & Ji, Min, 2021. "Modelling mortality dependence: An application of dynamic vine copula," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 241-255.
    19. Sun Meng & Yan Chen, 2023. "Market Volatility Spillover, Network Diffusion, and Financial Systemic Risk Management: Financial Modeling and Empirical Study," Mathematics, MDPI, vol. 11(6), pages 1-16, March.
    20. Boris Hirsch & Steffen Mueller, 2014. "Firm leadership and the gender pay gap: do active owners discriminate more than hired managers?," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 47(1), pages 129-142, March.

    More about this item

    Keywords

    Copulas; generalized power mean; max id; left tail decreasing; tail dependence;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:zbw:iwqwdp:012011. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/vierlde.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.