IDEAS home Printed from https://ideas.repec.org/p/bdi/wptemi/td_1096_17.html
   My bibliography  Save this paper

A goodness-of-fit test for Generalized Error Distribution

Author

Listed:
  • Daniele Coin

    (Bank of Italy)

Abstract

The Generalized Error Distribution is a widely used flexible family of symmetric probability distribution. Thanks to its properties, it is becoming more and more popular in many fields of science, and therefore it is important to determine whether a sample is drawn from a GED, usually done using a graphical approach. In this paper we present a new goodness-of-fit test for GED that performs well in detecting non-GED distribution when the alternative distribution is either skewed or a mixture. A comparison between well-known tests and this new procedure is performed through a simulation study. We have developed a function that performs the analysis described in this paper in the R environment. The computational time required to compute this procedure is negligible.

Suggested Citation

  • Daniele Coin, 2017. "A goodness-of-fit test for Generalized Error Distribution," Temi di discussione (Economic working papers) 1096, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:wptemi:td_1096_17
    as

    Download full text from publisher

    File URL: http://www.bancaditalia.it/pubblicazioni/temi-discussione/2017/2017-1096/en_tema_1096.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrew C. Harvey, 1990. "The Econometric Analysis of Time Series, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 026208189x, December.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Marsaglia, George & Marsaglia, John, 2004. "Evaluating the Anderson-Darling Distribution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 9(i02).
    4. J. P. Royston, 1982. "Expected Normal Order Statistics (Exact and Approximate)," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(2), pages 161-165, June.
    5. Chen, Carl R. & Su, Yuli & Huang, Ying, 2008. "Hourly index return autocorrelation and conditional volatility in an EAR-GJR-GARCH model with generalized error distribution," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 789-798, September.
    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. Sung Ik Kim, 2022. "ARMA–GARCH model with fractional generalized hyperbolic innovations," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-25, December.
    2. Hou, Yang & Li, Steven, 2014. "The impact of the CSI 300 stock index futures: Positive feedback trading and autocorrelation of stock returns," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 319-337.
    3. Jyri Kinnunen & Minna Martikainen, 2017. "Dynamic Autocorrelation and International Portfolio Allocation," Multinational Finance Journal, Multinational Finance Journal, vol. 21(1), pages 21-48, March.
    4. Han, Chulwoo & Park, Frank C., 2022. "A geometric framework for covariance dynamics," Journal of Banking & Finance, Elsevier, vol. 134(C).
    5. Tian, Maoxi & El Khoury, Rim & Alshater, Muneer M., 2023. "The nonlinear and negative tail dependence and risk spillovers between foreign exchange and stock markets in emerging economies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    6. Umar, Muhammad & Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Furqan, Mehreen, 2023. "Asymmetric volatility structure of equity returns: Evidence from an emerging market," The Quarterly Review of Economics and Finance, Elsevier, vol. 87(C), pages 330-336.
    7. Shively, Gerald E., 2001. "Price thresholds, price volatility, and the private costs of investment in a developing country grain market," Economic Modelling, Elsevier, vol. 18(3), pages 399-414, August.
    8. Bruno Feunou & Jean-Sébastien Fontaine & Abderrahim Taamouti & Roméo Tédongap, 2014. "Risk Premium, Variance Premium, and the Maturity Structure of Uncertainty," Review of Finance, European Finance Association, vol. 18(1), pages 219-269.
    9. Chang, Chia-Lin, 2015. "Modelling a latent daily Tourism Financial Conditions Index," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 113-126.
    10. Xu, Haifeng & Hamori, Shigeyuki, 2012. "Dynamic linkages of stock prices between the BRICs and the United States: Effects of the 2008–09 financial crisis," Journal of Asian Economics, Elsevier, vol. 23(4), pages 344-352.
    11. Norbert Funke & Akimi Matsuda, 2006. "Macroeconomic News and Stock Returns in the United States and Germany," German Economic Review, Verein für Socialpolitik, vol. 7(2), pages 189-210, May.
    12. Heidari , Hassan & Refah-Kahriz, Arash & Hashemi Berenjabadi, Nayyer, 2018. "Dynamic Relationship between Macroeconomic Variables and Stock Return Volatility in Tehran Stock Exchange: Multivariate MS ARMA GARCH Approach," Quarterly Journal of Applied Theories of Economics, Faculty of Economics, Management and Business, University of Tabriz, vol. 5(2), pages 223-250, August.
    13. 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.
    14. Bo-Hung Chiou & Shen-Ho Chang, 2020. "Influence of Investment Efficiency by Managers and Accounting Conservatism on Idiosyncratic Risks to Investors," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 10(1), pages 1-8.
    15. Ngene, Geoffrey & Tah, Kenneth A. & Darrat, Ali F., 2017. "Long memory or structural breaks: Some evidence for African stock markets," Review of Financial Economics, Elsevier, vol. 34(C), pages 61-73.
    16. Xilong Chen & Eric Ghysels, 2011. "News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons," Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 46-81, October.
    17. Caroline Jardet & Gaelle Le Fol, 2010. "Euro money market interest rate dynamics and volatility: how they respond to recent changes in the operational framework," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 316-330.
    18. Benjamin Born & Michael Ehrmann & Marcel Fratzscher, 2011. "How Should Central Banks Deal with a Financial Stability Objective? The Evolving Role of Communication as a Policy Instrument," Chapters, in: Sylvester Eijffinger & Donato Masciandaro (ed.), Handbook of Central Banking, Financial Regulation and Supervision, chapter 9, Edward Elgar Publishing.
    19. Renatas Kizys & Peter Spencer, 2007. "Assessing the Relation between Equity Risk Premium and Macroeconomic Volatilities in the UK," Discussion Papers 07/13, Department of Economics, University of York.
    20. 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.

    More about this item

    Keywords

    exponential power distribution; kurtosis; normal standardized Q-Q plot;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

    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:bdi:wptemi:td_1096_17. 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: https://edirc.repec.org/data/bdigvit.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.