IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v183y2019ic33.html
   My bibliography  Save this article

Kurtosis analysis in GARCH models with Gram–Charlier-like innovations

Author

Listed:
  • Vacca, Gianmarco
  • Zoia, Maria Grazia

Abstract

The approach based on polynomially-modified distributions, known as Gram–Charlier-like (GCl) expansions, has been proven effective to account for both excess kurtosis and skewness of financial data. In this paper, we examine GARCH models with innovations distributed as GCl expansions (GC-GARCH). The kurtosis gluts ascribable to both time-varying volatility and GCl distributed GARCH innovations is evaluated. Furthermore, a “kurtosis targeting” approach is devised to estimate the kurtosis of GCl innovations. This leads to GC-GARCH models tailored to fit the kurtosis requirements of financial data.

Suggested Citation

  • Vacca, Gianmarco & Zoia, Maria Grazia, 2019. "Kurtosis analysis in GARCH models with Gram–Charlier-like innovations," Economics Letters, Elsevier, vol. 183(C), pages 1-1.
    • Handle: RePEc:eee:ecolet:v:183:y:2019:i:c:33
    DOI: 10.1016/j.econlet.2019.108552
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S016517651930271X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2019.108552?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Allen Fleishman, 1978. "A method for simulating non-normal distributions," Psychometrika, Springer;The Psychometric Society, vol. 43(4), pages 521-532, December.
    2. Bai, Xuezheng & Russell, Jeffrey R. & Tiao, George C., 2003. "Kurtosis of GARCH and stochastic volatility models with non-normal innovations," Journal of Econometrics, Elsevier, vol. 114(2), pages 349-360, June.
    3. Luca Bagnato & Valerio Potì & Maria Zoia, 2015. "The role of orthogonal polynomials in adjusting hyperpolic secant and logistic distributions to analyse financial asset returns," Statistical Papers, Springer, vol. 56(4), pages 1205-1234, 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. Vacca, Gianmarco & Zoia, Maria Grazia & Bagnato, Luca, 2022. "Forecasting in GARCH models with polynomially modified innovations," International Journal of Forecasting, Elsevier, vol. 38(1), pages 117-141.
    2. Quatto, Piero & Vacca, Gianmarco & Zoia, Maria Grazia, 2021. "A new copula for modeling portfolios with skewed, leptokurtic and high-order dependent risk factors," The North American Journal of Economics and Finance, Elsevier, vol. 58(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. Li, Kunpeng & Lin, Wei, 2024. "Threshold spatial autoregressive model," Journal of Econometrics, Elsevier, vol. 244(1).
    2. Sabbaghi, Omid & Sabbaghi, Navid, 2011. "Carbon Financial Instruments, thin trading, and volatility: Evidence from the Chicago Climate Exchange," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(4), pages 399-407.
    3. Kai Yang & Qingqing Zhang & Xinyang Yu & Xiaogang Dong, 2023. "Bayesian inference for a mixture double autoregressive model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(2), pages 188-207, May.
    4. Schinckus, Christophe, 2015. "The valuation of social impact bonds: An introductory perspective with the Peterborough SIB," Research in International Business and Finance, Elsevier, vol. 35(C), pages 104-110.
    5. Max Auerswald & Morten Moshagen, 2015. "Generating Correlated, Non-normally Distributed Data Using a Non-linear Structural Model," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 920-937, December.
    6. Mohan D. Pant & Todd C. Headrick, 2017. "Simulating Uniform- and Triangular- Based Double Power Method Distributions," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 6(1), pages 1-1.
    7. Alin Sima, 2008. "Stylized Facts and Discrete Stochastic Volatility Models," Advances in Economic and Financial Research - DOFIN Working Paper Series 10, Bucharest University of Economics, Center for Advanced Research in Finance and Banking - CARFIB.
    8. David Daewhan Cho, 2004. "Uncertainty in Second Moments: Implications for Portfolio Allocation," Econometric Society 2004 Far Eastern Meetings 433, Econometric Society.
    9. Schinckus, Christophe, 2018. "The valuation of social impact bonds: An introductory perspective with the Peterborough SIB," Research in International Business and Finance, Elsevier, vol. 45(C), pages 1-6.
    10. Cheung, Yin-Wong & Chung, Sang-Kuck, 2009. "A Long Memory Model with Mixed Normal GARCH for US Inflation Data," Santa Cruz Department of Economics, Working Paper Series qt2202s99q, Department of Economics, UC Santa Cruz.
    11. Pelagatti, Matteo M. & Sen, Pranab K., 2013. "Rank tests for short memory stationarity," Journal of Econometrics, Elsevier, vol. 172(1), pages 90-105.
    12. Bali, Rakesh & Guirguis, Hany, 2007. "Extreme observations and non-normality in ARCH and GARCH," International Review of Economics & Finance, Elsevier, vol. 16(3), pages 332-346.
    13. Emanuela Raffinetti & Pier Alda Ferrari, 2021. "A dependence measure flow tree through Monte Carlo simulations," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(2), pages 467-496, April.
    14. Malmsten, Hans & Teräsvirta, Timo, 2004. "Stylized Facts of Financial Time Series and Three Popular Models of Volatility," SSE/EFI Working Paper Series in Economics and Finance 563, Stockholm School of Economics, revised 03 Sep 2004.
    15. Xiao, Xiao & Zhou, Chen, 2018. "The decomposition of jump risks in individual stock returns," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 207-228.
    16. Zhu, Ke & Li, Wai Keung, 2013. "A new Pearson-type QMLE for conditionally heteroskedastic models," MPRA Paper 52344, University Library of Munich, Germany.
    17. Audrone Virbickaite & M. Concepción Ausín & Pedro Galeano, 2015. "Bayesian Inference Methods For Univariate And Multivariate Garch Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 76-96, February.
    18. Zoia, Maria Grazia & Biffi, Paola & Nicolussi, Federica, 2018. "Value at risk and expected shortfall based on Gram-Charlier-like expansions," Journal of Banking & Finance, Elsevier, vol. 93(C), pages 92-104.
    19. Carol Alexander & Emese Lazar, 2009. "Modelling Regime‐Specific Stock Price Volatility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 761-797, December.
    20. Yen Lee & David Kaplan, 2018. "Generating Multivariate Ordinal Data via Entropy Principles," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 156-181, March.

    More about this item

    Keywords

    Orthogonal polynomials; GARCH model; Gram–Charlier-like expansions; Kurtosis;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    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:eee:ecolet:v:183:y:2019:i:c:33. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.