IDEAS home Printed from https://ideas.repec.org/a/cuf/journl/y2007v8i1p21-31.html
   My bibliography  Save this article

Empirical Analyses of Industry Stock Index Return Distributions for the Taiwan Stock Exchange

Author

Listed:
  • Svetlozar T. Rachev

    (School of Economics and Business Engineering, University of Karlsruhe)

  • Chufang Wu

    (Department of Applied Mathematics, National Donghua University of Taiwan)

  • Frank J. Fabozzi

    (Finance and Becton Fellow, School of Management, Yale University)

Abstract

We study the daily return distributions for 22 industry stock indexes on the Tai-wan Stock Exchange under the unconditional homoskedastic independent, identically distributed and the conditional heteroskedastic GARCH models. Two distribution hypotheses are tested: the Gaussian and the stable Paretian distributions. The performance of the stable Paretian distribution is better than that of the Gaussian distribution. A back-testing example is provided to give evidence on the superiority of the stable ARMA-GARCH to the normal ARMA-GARCH.

Suggested Citation

  • Svetlozar T. Rachev & Chufang Wu & Frank J. Fabozzi, 2007. "Empirical Analyses of Industry Stock Index Return Distributions for the Taiwan Stock Exchange," Annals of Economics and Finance, Society for AEF, vol. 8(1), pages 21-31, May.
    • Handle: RePEc:cuf:journl:y:2007:v:8:i:1:p:21-31
    as

    Download full text from publisher

    File URL: http://aeconf.com/Articles/May2007/aef080102.pdf
    Download Restriction: no
    File URL: http://down.aefweb.net/AefArticles/aef080102.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rachev, S.T (ed.), 2003. "Handbook of Heavy Tailed Distributions in Finance," Elsevier Monographs, Elsevier, edition 1, number 9780444508966.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Yu Chuan Huang & Bor-Jing Lin, 2004. "Value-at-Risk Analysis for Taiwan Stock Index Futures: Fat Tails and Conditional Asymmetries in Return Innovations," Review of Quantitative Finance and Accounting, Springer, vol. 22(2), pages 79-95, March.
    4. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    5. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    6. Chiang, Thomas C. & Doong, Shuh-Chyi, 1999. "Empirical analysis of real and financial volatilities on stock excess returns: evidence from Taiwan industrial data," Global Finance Journal, Elsevier, vol. 10(2), pages 187-200.
    7. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    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. Yuri Heymann, 2016. "A test of financial time-series data to discriminate among lognormal, Gaussian and square-root random walks," Computational Statistics, Springer, vol. 31(4), pages 1373-1383, December.
    2. Wei Sun & Svetlozar Rachev & Frank J. Fabozzi, 2009. "A New Approach for Using Lévy Processes for Determining High‐Frequency Value‐at‐Risk Predictions," European Financial Management, European Financial Management Association, vol. 15(2), pages 340-361, March.

    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. Stavroyiannis, S. & Makris, I. & Nikolaidis, V. & Zarangas, L., 2012. "Econometric modeling and value-at-risk using the Pearson type-IV distribution," International Review of Financial Analysis, Elsevier, vol. 22(C), pages 10-17.
    2. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & George Kouretas, 2005. "Mean and variance causality between the Cyprus Stock Exchange and major equity markets," Working Papers 0501, University of Crete, Department of Economics.
    3. Ioannis D Vrontos & Loukia Meligkotsidou & Spyridon D Vrontos, 2011. "Performance evaluation of mutual fund investments: The impact of non-normality and time-varying volatility," Journal of Asset Management, Palgrave Macmillan, vol. 12(4), pages 292-307, September.
    4. Maria S. Heracleous, 2007. "Sample Kurtosis, GARCH-t and the Degrees of Freedom Issue," Economics Working Papers ECO2007/60, European University Institute.
    5. Issler, João Victor, 1999. "Estimating and forecasting the volatility of Brazilian finance series using arch models (Preliminary Version)," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 347, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    6. David Daewhan Cho, 2004. "Uncertainty in Second Moments: Implications for Portfolio Allocation," Econometric Society 2004 Far Eastern Meetings 433, Econometric Society.
    7. Debabrata Mukhopadhyay & Nityananda Sarkar, 2013. "Stock Returns Under Alternative Volatility and Distributional Assumptions: The Case for India," International Econometric Review (IER), Econometric Research Association, vol. 5(1), pages 1-19, April.
    8. Chan, Felix, 2009. "Modelling time-varying higher moments with maximum entropy density," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(9), pages 2767-2778.
    9. de Vries, Casper G., 1991. "On the relation between GARCH and stable processes," Journal of Econometrics, Elsevier, vol. 48(3), pages 313-324, June.
    10. 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.
    11. Stefan Mittnik & Marc Paolella & Svetlozar Rachev, 1998. "Unconditional and Conditional Distributional Models for the Nikkei Index," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 5(2), pages 99-128, May.
    12. Mittnik, Stefan & Paolella, Marc S. & Rachev, Svetlozar T., 2002. "Stationarity of stable power-GARCH processes," Journal of Econometrics, Elsevier, vol. 106(1), pages 97-107, January.
    13. Sylvia J. Soltyk & Felix Chan, 2023. "Modeling time‐varying higher‐order conditional moments: A survey," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 33-57, February.
    14. Dima Alberg & Haim Shalit & Rami Yosef, 2008. "Estimating stock market volatility using asymmetric GARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 18(15), pages 1201-1208.
    15. Eom, Cheoljun & Kaizoji, Taisei & Scalas, Enrico, 2019. "Fat tails in financial return distributions revisited: Evidence from the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    16. Xekalaki, Evdokia & Degiannakis, Stavros, 2005. "Evaluating volatility forecasts in option pricing in the context of a simulated options market," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 611-629, April.
    17. Almeida e Santos Nogueira, R.J. & Basturk, N. & Kaymak, U. & Costa Sousa, J.M., 2013. "Estimation of flexible fuzzy GARCH models for conditional density estimation," ERIM Report Series Research in Management ERS-2013-013-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    18. Kim, Dongcheol & Kon, Stanley J., 1999. "Structural change and time dependence in models of stock returns," Journal of Empirical Finance, Elsevier, vol. 6(3), pages 283-308, September.
    19. Hatice Erkekoglu & Aweng Peter Majok Garang & Adire Simon Deng, 2020. "Modeling and Forecasting USD/UGX Volatility through GARCH Family Models: Evidence from Gaussian, T and GED Distributions," International Journal of Economics and Financial Issues, Econjournals, vol. 10(2), pages 268-281.
    20. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, June.

    More about this item

    Keywords

    Stable distributions; ARMA-GARCH; Heavy tails; Volatility clustering; Value at risk;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

    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:cuf:journl:y:2007:v:8:i:1:p:21-31. 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: Qiang Gao (email available below). General contact details of provider: https://edirc.repec.org/data/emcufcn.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.