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Volatility Estimators With High-Frequency Data From Bucharest Stock Exchange

Author

Listed:
  • Virgil DAMIAN

    (The Bucharest University of Economic Studies)

  • Cosmin – Octavian CEPOI

    (The Bucharest University of Economic Studies)

Abstract

In this paper, we have focused on accurately volatility estimation due to its crucial importance in investment and risk management activities. Based on tick by tick data, provided by Thomson Reuters, we have realized a comparative study among different high-frequency volatility estimators for some of the most important three companies listed on Bucharest Stock Exchange. Our findings emphasize that the presence of jumps or microstructure noises affect the efficiency of realized volatility estimator. So, based on data architecture, we have used adequate estimators jump and noise robust. We concluded that for less liquid markets, the presence of more visible jumps leads to higher intra-day volatilities comparing with more liquid markets.

Suggested Citation

  • Virgil DAMIAN & Cosmin – Octavian CEPOI, 2016. "Volatility Estimators With High-Frequency Data From Bucharest Stock Exchange," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(3), pages 247-264.
    • Handle: RePEc:cys:ecocyb:v:50:y:2016:i:3:p:247-264
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    References listed on IDEAS

    as
    1. Jacod, Jean & Li, Yingying & Mykland, Per A. & Podolskij, Mark & Vetter, Mathias, 2009. "Microstructure noise in the continuous case: The pre-averaging approach," Stochastic Processes and their Applications, Elsevier, vol. 119(7), pages 2249-2276, July.
    2. Andersen, Torben G. & Dobrev, Dobrislav & Schaumburg, Ernst, 2012. "Jump-robust volatility estimation using nearest neighbor truncation," Journal of Econometrics, Elsevier, vol. 169(1), pages 75-93.
    3. Ole E. Barndorff-Nielsen, 2004. "Power and Bipower Variation with Stochastic Volatility and Jumps," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 1-37.
    4. Zhang, Lan & Mykland, Per A. & Ait-Sahalia, Yacine, 2005. "A Tale of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1394-1411, December.
    5. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2008. "Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise," Econometrica, Econometric Society, vol. 76(6), pages 1481-1536, November.
    6. Nikolaus Hautsch, 2012. "Econometrics of Financial High-Frequency Data," Springer Books, Springer, number 978-3-642-21925-2, April.
    7. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    8. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 127-161, April.
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    Citations

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    Cited by:

    1. Adina Ionela Străchinaru & Bogdan Andrei Dumitrescu, 2019. "Assessing the Sustainability of Inflation Targeting: Evidence from EU Countries with Non-EURO Currencies," Sustainability, MDPI, vol. 11(20), pages 1-13, October.
    2. Meral KAGITCI & Leonardo BADEA & Vasile Cosmin NICULA, 2021. "The Catch-up Effect of Economic Growth. Evidence from the European Countries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 76-86, December.
    3. Ionut – Daniel Pop, 2019. "Systemic Sustainability of European Banking Activity: A Multi-Perspective Approach," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 9(3), pages 49-58, July.
    4. Mircea BAHNA & Cosmin-Octavian CEPOI & Bogdan Andrei DUMITRESCU & Virgil DAMIAN, 2018. "Estimating the Price Impact of Market Orders on the Bucharest Stock Exchange," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 120-133, December.
    5. Pop Ionuț-Daniel & Chicu Nicoleta & Răduțu Andrei, 2018. "Non-performing loans decision making in the Romanian banking system," Management & Marketing, Sciendo, vol. 13(1), pages 761-776, March.

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    More about this item

    Keywords

    stochastic volatility; realized variance; realized kernel; two time scales estimator; jump; stochastic integral representation.;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • 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)

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