IDEAS home Printed from https://ideas.repec.org/p/wat/wpaper/08006.html
   My bibliography  Save this paper

Continuous Empirical Characteristic Function Estimation of Mixtures of Normal Parameters

Author

Listed:
  • Dinghai Xu

    (Department of Economics, University of Waterloo)

  • John Knight

    (Department of Economics, University of Western Ontario)

Abstract

This paper develops an e±cient method for estimating the discrete mix- tures of normal family based on the continuous empirical characteristic function (CECF). An iterated estimation procedure based on the closed form objective distance function is proposed to improve the estimation effciency. The results from the Monte Carlo simulation reveal that the CECF estimator produces good finite sample properties. In particular, it outperforms the discrete type of methods when the maximum likelihood estimation fails to converge. An empirical example is provided for illustrative purposes.

Suggested Citation

  • Dinghai Xu & John Knight, 2008. "Continuous Empirical Characteristic Function Estimation of Mixtures of Normal Parameters," Working Papers 08006, University of Waterloo, Department of Economics.
    • Handle: RePEc:wat:wpaper:08006
    as

    Download full text from publisher

    File URL: http://economics.uwaterloo.ca/documents/Xu-MNviaCECF.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Kon, Stanley J, 1984. "Models of Stock Returns-A Comparison," Journal of Finance, American Finance Association, vol. 39(1), pages 147-165, March.
    2. Knight, John L. & Yu, Jun, 2002. "Empirical Characteristic Function In Time Series Estimation," Econometric Theory, Cambridge University Press, vol. 18(3), pages 691-721, June.
    3. Schmidt, Peter, 1982. "An Improved Version of the Quandt-Ramsey MGE Estimator for Mixtures of Normal Distributions and Switching Regressions," Econometrica, Econometric Society, vol. 50(2), pages 501-516, March.
    4. Kien Tran, 1998. "Estimating mixtures of normal distributions via empirical characteristic function," Econometric Reviews, Taylor & Francis Journals, vol. 17(2), pages 167-183.
    5. French, Kenneth R., 1980. "Stock returns and the weekend effect," Journal of Financial Economics, Elsevier, vol. 8(1), pages 55-69, March.
    6. Jiang, George J & Knight, John L, 2002. "Estimation of Continuous-Time Processes via the Empirical Characteristic Function," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 198-212, April.
    7. G. J. McLachlan, 1987. "On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 318-324, November.
    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. Hodoshima, Jiro & Yamawake, Toshiyuki, 2019. "Comparison of utility indifference pricing and mean-variance approach under a normal mixture distribution with time-varying volatility," Finance Research Letters, Elsevier, vol. 28(C), pages 74-81.
    2. Dinghai Xu & John Knight, 2013. "Stochastic volatility model under a discrete mixture-of-normal specification," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 37(2), pages 216-239, April.
    3. Dinghai Xu, 2009. "An Efficient Estimation for Switching Regression Models: A Monte Carlo Study," Working Papers 0903, University of Waterloo, Department of Economics, revised Apr 2009.
    4. Cornelis J. Potgieter & Marc G. Genton, 2013. "Characteristic Function-based Semiparametric Inference for Skew-symmetric Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(3), pages 471-490, September.

    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. Dinghai Xu, 2009. "The Applications of Mixtures of Normal Distributions in Empirical Finance: A Selected Survey," Working Papers 0904, University of Waterloo, Department of Economics, revised Sep 2009.
    2. Michael Rockinger & Maria Semenova, 2005. "Estimation of Jump-Diffusion Process vis Empirical Characteristic Function," FAME Research Paper Series rp150, International Center for Financial Asset Management and Engineering.
    3. Dinghai Xu & Tony S. Wirjanto, 2008. "An Empirical Characteristic Function Approach to VaR under a Mixture of Normal Distribution with Time-Varying Volatility," Working Papers 08008, University of Waterloo, Department of Economics.
    4. Hodoshima, Jiro & Yamawake, Toshiyuki, 2019. "Comparison of utility indifference pricing and mean-variance approach under a normal mixture distribution with time-varying volatility," Finance Research Letters, Elsevier, vol. 28(C), pages 74-81.
    5. Tsionas, Mike, 2012. "Simple techniques for likelihood analysis of univariate and multivariate stable distributions: with extensions to multivariate stochastic volatility and dynamic factor models," MPRA Paper 40966, University Library of Munich, Germany, revised 20 Aug 2012.
    6. John Knight & Stephen Satchell, 2008. "Testing for infinite order stochastic dominance with applications to finance, risk and income inequality," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 32(1), pages 35-46, January.
    7. Marine Carrasco & Jean-Pierre Florens, 2000. "Efficient GMM Estimation Using the Empirical Characteristic Function," Working Papers 2000-33, Center for Research in Economics and Statistics.
    8. Emanuele Taufer, 2008. "Characteristic function estimation of non-Gaussian Ornstein-Uhlenbeck processes," DISA Working Papers 0805, Department of Computer and Management Sciences, University of Trento, Italy, revised 07 Jul 2008.
    9. Kotchoni, Rachidi, 2012. "Applications of the characteristic function-based continuum GMM in finance," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3599-3622.
    10. Kotchoni, Rachidi, 2014. "The indirect continuous-GMM estimation," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 464-488.
    11. Robert J. Elliott & Carlton-James U. Osakwe, 2006. "Option Pricing for Pure Jump Processes with Markov Switching Compensators," Finance and Stochastics, Springer, vol. 10(2), pages 250-275, April.
    12. Todorov, Viktor & Tauchen, George & Grynkiv, Iaryna, 2011. "Realized Laplace transforms for estimation of jump diffusive volatility models," Journal of Econometrics, Elsevier, vol. 164(2), pages 367-381, October.
    13. Dinghai Xu, 2009. "An Efficient Estimation for Switching Regression Models: A Monte Carlo Study," Working Papers 0903, University of Waterloo, Department of Economics, revised Apr 2009.
    14. Stojanović, Vladica S. & Popović, Biljana Č. & Milovanović, Gradimir V., 2016. "The Split-SV model," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 560-581.
    15. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    16. Maria P. Braun & Simos G. Meintanis & Viatcheslav B. Melas, 2008. "Optimal Design Approach to GMM Estimation of Parameters Based on Empirical Transforms," International Statistical Review, International Statistical Institute, vol. 76(3), pages 387-400, December.
    17. Chihwa Kao & Yongmiao Hong, 2004. "Detecting Neglected Nonlinearity in Dynamic Panel Data with Time-Varying Conditional Heteroskedasticity," Econometric Society 2004 Far Eastern Meetings 753, Econometric Society.
    18. Kien Tran, 1998. "Estimating mixtures of normal distributions via empirical characteristic function," Econometric Reviews, Taylor & Francis Journals, vol. 17(2), pages 167-183.
    19. Narayanaswamy Balakrishnan & Chengwei Qin, 2019. "First Passage Time of a Lévy Degradation Model with Random Effects," Methodology and Computing in Applied Probability, Springer, vol. 21(1), pages 315-329, March.
    20. Taufer, Emanuele & Leonenko, Nikolai & Bee, Marco, 2011. "Characteristic function estimation of Ornstein-Uhlenbeck-based stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 55(8), pages 2525-2539, August.

    More about this item

    Keywords

    Empirical characteristic function; Mixtures of normal.;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions

    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:wat:wpaper:08006. 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: Sherri Anne Arsenault (email available below). General contact details of provider: https://edirc.repec.org/data/dewatca.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.