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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
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    References listed on IDEAS

    as
    1. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    2. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. 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,.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    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.

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    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

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