IDEAS home Printed from https://ideas.repec.org/a/taf/emetrv/v38y2019i5p487-508.html
   My bibliography  Save this article

Multivariate Return Decomposition: Theory and Implications

Author

Listed:
  • Stanislav Anatolyev
  • Nikolay Gospodinov

Abstract

In this paper, we propose a model based on multivariate decomposition of multiplicative – absolute values and signs – components of asset returns. In the m-variate case, the marginals for the m absolute values and the binary marginals for the m directions are linked through a 2m-dimensional copula. The approach is detailed in the case of a bivariate decomposition. We outline the construction of the likelihood function and the computation of different conditional measures. The finite-sample properties of the maximum likelihood estimator are assessed by simulation. An application to predicting bond returns illustrates the usefulness of the proposed method.

Suggested Citation

  • Stanislav Anatolyev & Nikolay Gospodinov, 2019. "Multivariate Return Decomposition: Theory and Implications," Econometric Reviews, Taylor & Francis Journals, vol. 38(5), pages 487-508, May.
    • Handle: RePEc:taf:emetrv:v:38:y:2019:i:5:p:487-508
    DOI: 10.1080/07474938.2017.1348677
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/07474938.2017.1348677
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/07474938.2017.1348677?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Scott Joslin & Marcel Priebsch & Kenneth J. Singleton, 2014. "Risk Premiums in Dynamic Term Structure Models with Unspanned Macro Risks," Journal of Finance, American Finance Association, vol. 69(3), pages 1197-1233, June.
    2. John H. Cochrane & Monika Piazzesi, 2005. "Bond Risk Premia," American Economic Review, American Economic Association, vol. 95(1), pages 138-160, March.
    3. Nyberg, Henri, 2014. "A Bivariate Autoregressive Probit Model: Business Cycle Linkages And Transmission Of Recession Probabilities," Macroeconomic Dynamics, Cambridge University Press, vol. 18(4), pages 838-862, June.
    4. Sydney C. Ludvigson & Serena Ng, 2009. "Macro Factors in Bond Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 22(12), pages 5027-5067, December.
    5. Anna Cieslak & Pavol Povala, 2015. "Expected Returns in Treasury Bonds," The Review of Financial Studies, Society for Financial Studies, vol. 28(10), pages 2859-2901.
    6. Anatolyev Stanislav, 2009. "Multi-Market Direction-of-Change Modeling Using Dependence Ratios," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(1), pages 1-24, March.
    7. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, April.
    8. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2007. "A Model for Multivariate Non-negative Valued Processes in Financial Econometrics," Econometrics Working Papers Archive wp2007_16, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    9. Chernov, Mikhail & Mueller, Philippe, 2012. "The term structure of inflation expectations," Journal of Financial Economics, Elsevier, vol. 106(2), pages 367-394.
    10. Anatolyev, Stanislav & Gospodinov, Nikolay, 2010. "Modeling Financial Return Dynamics via Decomposition," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 232-245.
    11. Liu, Xiaochun & Luger, Richard, 2015. "Unfolded GARCH models," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 186-217.
    12. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    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. Stanislav Anatolyev, 2021. "Directional news impact curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 94-107, January.
    2. Chen, Nan-Kuang & Chen, Shiu-Sheng & Chou, Yu-Hsi, 2017. "Further evidence on bear market predictability: The role of the external finance premium," International Review of Economics & Finance, Elsevier, vol. 50(C), pages 106-121.
    3. Nikolay Gospodinov, 2017. "Asset Co-movements: Features and Challenges," FRB Atlanta Working Paper 2017-11, Federal Reserve Bank of Atlanta.

    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. Michael D. Bauer & Glenn D. Rudebusch, 2020. "Interest Rates under Falling Stars," American Economic Review, American Economic Association, vol. 110(5), pages 1316-1354, May.
    2. Guo, Bin & Huang, Fuzhe & Li, Kai, 2022. "Time to build and bond risk premia," Journal of Economic Dynamics and Control, Elsevier, vol. 136(C).
    3. Liu, Yan & Wu, Jing Cynthia, 2021. "Reconstructing the yield curve," Journal of Financial Economics, Elsevier, vol. 142(3), pages 1395-1425.
    4. Michael D. Bauer & James D. Hamilton, 2018. "Robust Bond Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 31(2), pages 399-448.
    5. Jing-Zhi Huang & Zhan Shi, 2023. "Machine-Learning-Based Return Predictors and the Spanning Controversy in Macro-Finance," Management Science, INFORMS, vol. 69(3), pages 1780-1804, March.
    6. Rui Liu, 2019. "Forecasting Bond Risk Premia with Unspanned Macroeconomic Information," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 1-62, March.
    7. Doshi, Hitesh & Jacobs, Kris & Liu, Rui, 2018. "Macroeconomic determinants of the term structure: Long-run and short-run dynamics," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 99-122.
    8. Zhang, Yugui & Zhu, Jie & Zhu, Xiaoneng, 2021. "Global bond risk premia under falling stars," Finance Research Letters, Elsevier, vol. 42(C).
    9. Chiarella, Carl & Hsiao, Chih-Ying & Tô, Thuy-Duong, 2016. "Stochastic correlation and risk premia in term structure models," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 59-78.
    10. Valentin Haddad & David Sraer, 2020. "The Banking View of Bond Risk Premia," Journal of Finance, American Finance Association, vol. 75(5), pages 2465-2502, October.
    11. Feng Zhao & Guofu Zhou & Xiaoneng Zhu, 2021. "Unspanned Global Macro Risks in Bond Returns," Management Science, INFORMS, vol. 67(12), pages 7825-7843, December.
    12. Bekaert, Geert & Engstrom, Eric & Ermolov, Andrey, 2021. "Macro risks and the term structure of interest rates," Journal of Financial Economics, Elsevier, vol. 141(2), pages 479-504.
    13. Riccardo Rebonato, 2017. "Affine Models With Stochastic Market Price Of Risk," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(04), pages 1-38, June.
    14. Kučera, Adam, 2020. "Identification of triggers of U.S. yield curve movements," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    15. Valentin Haddad & Serhiy Kozak & Shrihari Santosh, 2017. "Predicting Relative Returns," NBER Working Papers 23886, National Bureau of Economic Research, Inc.
    16. Petar Sabtchevsky & Paul Whelan & Andrea Vedolin & Philippe Mueller, 2017. "Variance Risk Premia on Stocks and Bonds," 2017 Meeting Papers 1161, Society for Economic Dynamics.
    17. Richard K. Crump & Stefano Eusepi & Emanuel Moench, 2016. "The term structure of expectations and bond yields," Staff Reports 775, Federal Reserve Bank of New York.
    18. Nyberg, Henri & Pönkä, Harri, 2016. "International sign predictability of stock returns: The role of the United States," Economic Modelling, Elsevier, vol. 58(C), pages 323-338.
    19. Stanislav Anatolyev, 2021. "Directional news impact curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 94-107, January.
    20. Mirco Rubin & Dario Ruzzi, 2020. "Equity tail risk in the treasury bond market," Temi di discussione (Economic working papers) 1311, Bank of Italy, Economic Research and International Relations Area.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:emetrv:v:38:y:2019:i:5:p:487-508. 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: the person in charge (email available below). General contact details of provider: http://www.tandfonline.com/LECR20 .

    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.