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Forecasting Volatility: Evidence from the Bucharest Stock Exchange

Author

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  • Erginbay UGURLU

    () (Hitit Universitesi, FEAS, Department of Economics)

Abstract

Financial series tend to be characterized by volatility and this characteristic affects both financial series of developed markets and emerging markets. Because of the emerging markets have provided major investment opportunities in last decades their volatility has been widely investigated in the literature. The most popular volatility models are the Autoregressive Conditional Heteroscedastic (ARCH) or Generalized Autoregressive Conditional Heteroscedastic (GARCH) models. This paper aims to investigate the volatility of Bucharest Stock Exchange, BET index as an emerging capital market and compare forecasting power for volatility of this index during 2000-2014. To do this, this paper use GARCH, TARCH, EGARCH and PARCH models against Generalized Error distribution. We estimate these models then we compare the forecasting power of these GARCH type models in sample period. The results show that the EGARCH is the best model by means of forecasting performance.

Suggested Citation

  • Erginbay UGURLU, 2014. "Forecasting Volatility: Evidence from the Bucharest Stock Exchange," International Conference on Economic Sciences and Business Administration, Spiru Haret University, vol. 1(1), pages 302-310, December.
  • Handle: RePEc:icb:wpaper:v:1:y:2014:i:1:302-310
    as

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    File URL: http://icesba.eu/RePEc/icb/wpaper/ICESBA2014_37UGURLU_P302-310.pdf
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    References listed on IDEAS

    as
    1. Shields, Kalvinder K, 1997. "Stock Return Volatility on Emerging Eastern European Markets," The Manchester School of Economic & Social Studies, University of Manchester, vol. 65(0), pages 118-138, Supplemen.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    4. Rebecca Emerson & Stephen Hall & Anna Zalewska-Mitura, 1997. "Evolving Market Efficiency with an Application to Some Bulgarian Shares," Economic Change and Restructuring, Springer, vol. 30(2), pages 75-90, May.
    5. Scheicher, Martin, 2001. "The Comovements of Stock Markets in Hungary, Poland and the Czech Republic," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(1), pages 27-39, January.
    6. Eleftherios I. Thalassinos & Erginbay Ugurlu & Yusuf Muratoglu, 2015. "Comparison of Forecasting Volatility in the Czech Republic Stock Market," Applied Economics and Finance, Redfame publishing, vol. 2(1), pages 11-18, February.
    7. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    9. Syriopoulos, Theodore, 2007. "Dynamic linkages between emerging European and developed stock markets: Has the EMU any impact?," International Review of Financial Analysis, Elsevier, vol. 16(1), pages 41-60.
    10. Maria Kasch-Haroutounian & Simon Price, 2001. "Volatility in the transition markets of Central Europe," Applied Financial Economics, Taylor & Francis Journals, vol. 11(1), pages 93-105.
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    More about this item

    Keywords

    stock returns; volatility; GARCH models; emerging markets.;

    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
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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