IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2107.06979.html
   My bibliography  Save this paper

Generalized Covariance Estimator

Author

Listed:
  • Christian Gourieroux
  • Joann Jasiak

Abstract

We consider a class of semi-parametric dynamic models with strong white noise errors. This class of processes includes the standard Vector Autoregressive (VAR) model, the nonfundamental structural VAR, the mixed causal-noncausal models, as well as nonlinear dynamic models such as the (multivariate) ARCH-M model. For estimation of processes in this class, we propose the Generalized Covariance (GCov) estimator, which is obtained by minimizing a residual-based multivariate portmanteau statistic as an alternative to the Generalized Method of Moments. We derive the asymptotic properties of the GCov estimator and of the associated residual-based portmanteau statistic. Moreover, we show that the GCov estimators are semi-parametrically efficient and the residual-based portmanteau statistics are asymptotically chi-square distributed. The finite sample performance of the GCov estimator is illustrated in a simulation study. The estimator is also applied to a dynamic model of cryptocurrency prices.

Suggested Citation

  • Christian Gourieroux & Joann Jasiak, 2021. "Generalized Covariance Estimator," Papers 2107.06979, arXiv.org.
    • Handle: RePEc:arx:papers:2107.06979
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2107.06979
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Alain Hecq & Lenard Lieb & Sean Telg, 2016. "Identification of Mixed Causal-Noncausal Models in Finite Samples," Annals of Economics and Statistics, GENES, issue 123-124, pages 307-331.
    2. Niu, Lu & Liu, Xiumin & Zhao, Junlong, 2020. "Robust estimator of the correlation matrix with sparse Kronecker structure for a high-dimensional matrix-variate," Journal of Multivariate Analysis, Elsevier, vol. 177(C).
    3. Pena D. & Rodriguez J., 2002. "A Powerful Portmanteau Test of Lack of Fit for Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 601-610, June.
    4. Jin, Ze & Matteson, David S., 2018. "Generalizing distance covariance to measure and test multivariate mutual dependence via complete and incomplete V-statistics," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 304-322.
    5. Gourieroux, Christian & Jasiak, Joann, 2017. "Noncausal vector autoregressive process: Representation, identification and semi-parametric estimation," Journal of Econometrics, Elsevier, vol. 200(1), pages 118-134.
    6. Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
    7. Anderson, T. W., 1999. "Asymptotic Theory for Canonical Correlation Analysis," Journal of Multivariate Analysis, Elsevier, vol. 70(1), pages 1-29, July.
    8. Thomas J. Fisher & Colin M. Gallagher, 2012. "New Weighted Portmanteau Statistics for Time Series Goodness of Fit Testing," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 777-787, June.
    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. Hall, Mauri K. & Jasiak, Joann, 2024. "Modelling common bubbles in cryptocurrency prices," Economic Modelling, Elsevier, vol. 139(C).

    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. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2022. "Data-driven portmanteau tests for time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(3), pages 675-698, September.
    2. Kokoszka, Piotr & Reimherr, Matthew & Wölfing, Nikolas, 2016. "A randomness test for functional panels," Journal of Multivariate Analysis, Elsevier, vol. 151(C), pages 37-53.
    3. Gianluca Cubadda & Alain Hecq & Sean Telg, 2019. "Detecting Co‐Movements in Non‐Causal Time Series," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(3), pages 697-715, June.
    4. Ke Zhu, 2016. "Bootstrapping the portmanteau tests in weak auto-regressive moving average models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 463-485, March.
    5. Kramkov, Viacheslav & Maksimov, Andrey, 2020. "Loan market markups and noncausal autoregressions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 60, pages 48-69.
    6. Pierdomenico Duttilo & Stefano Antonio Gattone & Tonio Di Battista, 2021. "Volatility Modeling: An Overview of Equity Markets in the Euro Area during COVID-19 Pandemic," Mathematics, MDPI, vol. 9(11), pages 1-18, May.
    7. Alex Ilek & Tanya Suchoy & Nir Klein, 2006. "Estimating the premium implicit in the yields of Treasury Bills," Israel Economic Review, Bank of Israel, vol. 4(2), pages 53-83.
    8. Li, Yuming, 1998. "Expected stock returns, risk premiums and volatilities of economic factors1," Journal of Empirical Finance, Elsevier, vol. 5(2), pages 69-97, June.
    9. Alagidede, Paul & Panagiotidis, Theodore, 2009. "Modelling stock returns in Africa's emerging equity markets," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 1-11, March.
    10. Delis, Manthos & Savva, Christos & Theodossiou, Panayiotis, 2020. "A Coronavirus Asset Pricing Model: The Role of Skewness," MPRA Paper 100877, University Library of Munich, Germany.
    11. Chang, Chia-Lin & Hsu, Hui-Kuang, 2013. "Modelling Volatility Size Effects for Firm Performance: The Impact of Chinese Tourists to Taiwan," MPRA Paper 45691, University Library of Munich, Germany.
    12. Broze, Laurence & Gourieroux, Christian, 1998. "Pseudo-maximum likelihood method, adjusted pseudo-maximum likelihood method and covariance estimators," Journal of Econometrics, Elsevier, vol. 85(1), pages 75-98, July.
    13. Nguyen, Tien-Trung & Wu, Yang-Che & Ke, Mei-Chu & Liao, Tung Liang, 2022. "Can direct government intervention save the stock market?," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 271-284.
    14. repec:wyi:journl:002087 is not listed on IDEAS
    15. Asai, Manabu & McAleer, Michael, 2015. "Leverage and feedback effects on multifactor Wishart stochastic volatility for option pricing," Journal of Econometrics, Elsevier, vol. 187(2), pages 436-446.
    16. Joya, Omar, 2015. "Growth and volatility in resource-rich countries: Does diversification help?," Structural Change and Economic Dynamics, Elsevier, vol. 35(C), pages 38-55.
    17. Frederick van der Ploeg & Steven Poelhekke, 2007. "Volatility, Financial Development and the Natural Resource Curse," Economics Working Papers ECO2007/36, European University Institute.
    18. Zaka Ratsimalahelo, 2003. "Strongly Consistent Determination of the Rank of Matrix," Econometrics 0307007, University Library of Munich, Germany.
    19. D Büttner & B. Hayo, 2012. "EMU-related news and financial markets in the Czech Republic, Hungary and Poland," Applied Economics, Taylor & Francis Journals, vol. 44(31), pages 4037-4053, November.
    20. Czudaj Robert L., 2020. "The role of uncertainty on agricultural futures markets momentum trading and volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(3), pages 1-39, June.
    21. Ngozi G. Emenogu & Monday Osagie Adenomon & Nwaze Obini Nweze, 2020. "On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-25, December.

    More about this item

    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:arx:papers:2107.06979. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.