IDEAS home Printed from https://ideas.repec.org/a/rej/journl/v10y2007i23p19-28.html
   My bibliography  Save this article

Testing for Heteroskedasticity on the Bucharest Stock Exchange

Author

Listed:
  • Radu Lupu

    (Academy of Economic Studies, Bucharest, Romania)

  • Iulia Lupu

    (Victor Slavescu Center for Financial and Monetary Research, Romanian Academy)

Abstract

The ARCH type of models is a notorious family of models proven to be suitable for predicting financial returns. Their notoriety flourished after Bollerslev (1986) developed the econometric Generalized ARCH model (GARCH). This paper provides a presentation of the main characteristics of the modeling of financial returns with the objective to calibrate an EGARCH (Exponential GARCH) model for the logarithmic returns of the Romanian composite index BET-C on the stocks listed at the Bucharest Stock Exchange. We continue a previous study Lupu (2005) to model the statistical properties of these returns in comparison with the main non-normality properties found in previous research for the US stock index. We found that these properties are generally held on the Romanian market and this provides us reasons to trust the opportunity of an EGARCH model. The article provides the testing of the predictive power of this model for the Romanian index by calibrating the model and then evaluate its performance on an out of sample test.

Suggested Citation

  • Radu Lupu & Iulia Lupu, 2007. "Testing for Heteroskedasticity on the Bucharest Stock Exchange," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 10(23), pages 19-28, June.
    • Handle: RePEc:rej:journl:v:10:y:2007:i:23:p:19-28
    as

    Download full text from publisher

    File URL: http://www.rejournal.eu/sites/rejournal.versatech.ro/files/articole/2014-05-10/2326/je202320lupu20lupu.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Baillie, Richard T. & Bollerslev, Tim, 1992. "Prediction in dynamic models with time-dependent conditional variances," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 91-113.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Bera, Anil K & Higgins, Matthew L, 1993. "ARCH Models: Properties, Estimation and Testing," Journal of Economic Surveys, Wiley Blackwell, vol. 7(4), pages 305-366, December.
    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.
    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. Viorica Chirila & Ciprian Chirila, 2014. "The Use of Risk and Return for Testing the Stability of Stock Markets," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 10(2), pages 182-192, April.
    2. POPOVICI, Oana Cristina, 2015. "A Volatility Analysis Of The Euro Currency And The Bond Market," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 19(1), pages 67-79.
    3. OPREANA Claudiu & BRATIAN Vasile, 2012. "Modeling Of Volatility In The Romanian Capital Market," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 7(3), pages 113-128, December.
    4. Adrian Cantemir Călin, 2015. "Eloquence is The Key – the Impact of Monetary Policy Speeches on Exchange Rate Volatility," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 18(56), pages 3-18, June,.
    5. Gheorghe HURDUZEU & Radu Cristian MUSETESCU & Georgeta Madalina MEGHISAN, 2015. "Financial Market Reaction To Changes In The Volatilities Of Cds Returns," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 152-165, September.
    6. DUȚĂ, Violeta, 2018. "Using The Symmetric Models Garch (1.1) And Garch-M (1.1) To Investigate Volatility And Persistence For The European And Us Financial Markets," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 22(1), pages 64-86.
    7. El Jebari, Ouael & Hakmaoui, Abdelati, 2018. "GARCH Family Models vs EWMA: Which is the Best Model to Forecast Volatility of the Moroccan Stock Exchange Market? || Modelos de la familia GARCH vs EWMA: ¿cuál es el mejor modelo para pronosticar la ," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 237-249, Diciembre.
    8. CHIRILA, Viorica & CHIRILA, Ciprian, 2014. "Testing Stock Markets’ Integration From Central And Eastern European Countries Within Euro Zone," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 18(3), pages 76-88.

    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. Font, Begoña, 1998. "Modelización de series temporales financieras. Una recopilación," DES - Documentos de Trabajo. Estadística y Econometría. DS 3664, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654.
    3. Torben G. Andersen & Tim Bollerslev & Peter Christoffersen & Francis X. Diebold, 2007. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," NBER Chapters, in: The Risks of Financial Institutions, pages 513-544, National Bureau of Economic Research, Inc.
    4. 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.
    5. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    6. Angelidis, Timotheos & Benos, Alexandros & Degiannakis, Stavros, 2004. "The Use of GARCH Models in VaR Estimation," MPRA Paper 96332, University Library of Munich, Germany.
    7. Choudhry, Taufiq, 1996. "Stock market volatility and the crash of 1987: evidence from six emerging markets," Journal of International Money and Finance, Elsevier, vol. 15(6), pages 969-981, December.
    8. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871.
    9. Peter A. Zadrozny, 2005. "Necessary and Sufficient Restrictions for Existence of a Unique Fourth Moment of a Univariate GARCH(p,q) Process," CESifo Working Paper Series 1505, CESifo.
    10. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2013. "Financial Risk Measurement for Financial Risk Management," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1127-1220, Elsevier.
    11. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    12. Cornelis A. Los, 2005. "Measurement of Financial Risk Persistence," Finance 0502013, University Library of Munich, Germany.
    13. Michael McKenzie & Heather Mitchell & Robert Brooks & Robert Faff, 2001. "Power ARCH modelling of commodity futures data on the London Metal Exchange," The European Journal of Finance, Taylor & Francis Journals, vol. 7(1), pages 22-38.
    14. Peter Christoffersen & Silvia Gonçalves, 2004. "Estimation Risk in Financial Risk Management," CIRANO Working Papers 2004s-15, CIRANO.
    15. Dominique Guegan & Bertrand K. Hassani, 2019. "Risk Measurement," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02119256, HAL.
    16. Ruiz Ortega, Esther, 1993. "Stochastic volatility versus autoregressive conditional heteroscedasticity," DES - Working Papers. Statistics and Econometrics. WS 5708, Universidad Carlos III de Madrid. Departamento de Estadística.
    17. Tim Bollerslev & Robert J. Hodrick, 1992. "Financial Market Efficiency Tests," NBER Working Papers 4108, National Bureau of Economic Research, Inc.
    18. Zhu, Ke & Li, Wai Keung, 2013. "A new Pearson-type QMLE for conditionally heteroskedastic models," MPRA Paper 52344, University Library of Munich, Germany.
    19. 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.
    20. 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.

    More about this item

    Keywords

    Exponential GARCH; financial econometrics; Romanian stock exchange;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    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:rej:journl:v:10:y:2007:i:23:p:19-28. 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: Radu Lupu (email available below). General contact details of provider: https://edirc.repec.org/data/frasero.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.