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Using Garch-in-Mean Model to Investigate Volatility and Persistence at Different Frequencies for Bucharest Stock Exchange during 1997-2012

Author

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  • Iulian PANAIT

    (Bucharest Academy of Economic Studies)

  • Ecaterina Oana SLĂVESCU

    (Bucharest Academy of Economic Studies)

Abstract

In our paper we use data mining to compare the volatility structure of high (daily) and low (weekly, monthly) frequencies for seven Romanian companies traded on Bucharest Stock Exchange and three market indices, during 1997-2012. For each of the 10 time series and three frequencies we fit a GARCH-in-mean model and we find that persistency is more present in the daily returns as compared with the weekly and monthly series. On the other hand, the GARCH-in-mean failed to confirm (on our data) the theoretical hypothesis that an increase in volatility leads to a rise in future returns, mainly because the variance coefficient from the mean equation of the model was not statistically significant for most of the time series analyzed and on most of the frequencies. The diagnosis that we ran in order the verify the goodness of fit for the model showed that GARCH-in-mean was well fitted on the weekly and monthly time series but behaved less well on the daily time series.

Suggested Citation

  • Iulian PANAIT & Ecaterina Oana SLĂVESCU, 2012. "Using Garch-in-Mean Model to Investigate Volatility and Persistence at Different Frequencies for Bucharest Stock Exchange during 1997-2012," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(5(570)), pages 55-76, May.
    • Handle: RePEc:agr:journl:v:5(570):y:2012:i:5(570):p:55-76
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    Citations

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    Cited by:

    1. Md. Zahangir Alam & Md. Noman Siddikee & Md. Masukujjaman, 2013. "Forecasting Volatility of Stock Indices with ARCH Model," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 4(2), pages 126-143, April.
    2. Ngozi G. Emenogu & Monday Osagie Adenomon & Nwaze Obini Nweze, 2020. "On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-25, December.
    3. Pierdomenico Duttilo & Stefano Antonio Gattone & Tonio Di Battista, 2021. "Volatility Modeling: An Overview of Equity Markets in the Euro Area during COVID-19 Pandemic," Mathematics, MDPI, vol. 9(11), pages 1-18, May.
    4. E.B. Nkemnole & J.T. Wulu, 2017. "Modeling of stock indices with HMM-SV models," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(2(611), S), pages 45-60, Summer.
    5. 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.
    6. Lidija Dedi & Burhan F. Yavas, 2016. "Return and volatility spillovers in equity markets: An investigation using various GARCH methodologies," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1266788-126, December.
    7. Yavas, Burhan F. & Dedi, Lidija, 2016. "An investigation of return and volatility linkages among equity markets: A study of selected European and emerging countries," Research in International Business and Finance, Elsevier, vol. 37(C), pages 583-596.

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