IDEAS home Printed from https://ideas.repec.org/a/vrs/poicbe/v12y2018i1p544-556n49.html
   My bibliography  Save this article

Measures of volatility for the Romanian Stock Exchange: a regime switching approach

Author

Listed:
  • Lolea Iulian-Cornel
  • Vilcu Lucian Constantin

    (The Bucharest University of Economic Studies, Bucharest, Romania)

Abstract

This paper aims to identify if regime-switching GARCH models perform better than singlestate GARCH models for the Romanian stock market. There will be used two approaches: in-sample and out-of-sample. All estimations are going to be made for the BET Index, which is the most relevant index from the selected market. The results will be ranked based on statistical loss functions for each of the two considered approaches. These rankings should ensure an accurate comparison for models’ performance and they succeeded to return about the same results as in the relevant literature. Hence, for the in-sample evaluation there was no model which performs best for all loss-functions, but one can notice that for the out-of-sample evaluation the regime-switching models performed better especially on short-term (1-day observation period). All of these results were used further to improve some risk management strategies based on VaR, for which the volatility could be estimated through regimeswitching GARCH models, than considering historical volatility.

Suggested Citation

  • Lolea Iulian-Cornel & Vilcu Lucian Constantin, 2018. "Measures of volatility for the Romanian Stock Exchange: a regime switching approach," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 12(1), pages 544-556, May.
  • Handle: RePEc:vrs:poicbe:v:12:y:2018:i:1:p:544-556:n:49
    DOI: 10.2478/picbe-2018-0049
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/picbe-2018-0049
    Download Restriction: no

    File URL: https://libkey.io/10.2478/picbe-2018-0049?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Pagan, Adrian R. & Schwert, G. William, 1990. "Alternative models for conditional stock volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 267-290.
    3. Koopman, Siem Jan & Jungbacker, Borus & Hol, Eugenie, 2005. "Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 445-475, June.
    4. Franc Klaassen, 2002. "Improving GARCH volatility forecasts with regime-switching GARCH," Empirical Economics, Springer, vol. 27(2), pages 363-394.
    5. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Marcucci Juri, 2005. "Forecasting Stock Market Volatility with Regime-Switching GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-55, December.
    8. Gita Persand & Chris Brooks, 2003. "Volatility forecasting for risk management," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(1), pages 1-22.
    9. Carol Alexander & Emese Lazar, 2009. "Modelling Regime‐Specific Stock Price Volatility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 761-797, December.
    10. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
    Full references (including those not matched with items on IDEAS)

    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. Yue-Jun Zhang & Ting Yao & Ling-Yun He, 2015. "Forecasting crude oil market volatility: can the Regime Switching GARCH model beat the single-regime GARCH models?," Papers 1512.01676, arXiv.org.
    2. Zhang, Yue-Jun & Yao, Ting & He, Ling-Yun & Ripple, Ronald, 2019. "Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models?," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 302-317.
    3. Abounoori, Esmaiel & Elmi, Zahra (Mila) & Nademi, Younes, 2016. "Forecasting Tehran stock exchange volatility; Markov switching GARCH approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 264-282.
    4. Ataurima Arellano, Miguel & Rodríguez, Gabriel, 2020. "Empirical modeling of high-income and emerging stock and Forex market return volatility using Markov-switching GARCH models," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    5. Halkos, George E. & Tsirivis, Apostolos S., 2019. "Effective energy commodity risk management: Econometric modeling of price volatility," Economic Analysis and Policy, Elsevier, vol. 63(C), pages 234-250.
    6. Aurea Grané & Helena Veiga, 2012. "Asymmetry, realised volatility and stock return risk estimates," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 11(2), pages 147-164, August.
    7. Grané, Aurea & Veiga, Helena, 2007. "The effect of realised volatility on stock returns risk estimates," DES - Working Papers. Statistics and Econometrics. WS ws076316, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Ardia, David & Bluteau, Keven & Boudt, Kris & Catania, Leopoldo, 2018. "Forecasting risk with Markov-switching GARCH models:A large-scale performance study," International Journal of Forecasting, Elsevier, vol. 34(4), pages 733-747.
    9. Carol Alexander & Emese Lazar, 2009. "Modelling Regime‐Specific Stock Price Volatility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 761-797, December.
    10. Lin, Yu & Xiao, Yang & Li, Fuxing, 2020. "Forecasting crude oil price volatility via a HM-EGARCH model," Energy Economics, Elsevier, vol. 87(C).
    11. Cheng, Ai-Ru & Jahan-Parvar, Mohammad R., 2014. "Risk–return trade-off in the pacific basin equity markets," Emerging Markets Review, Elsevier, vol. 18(C), pages 123-140.
    12. Naeem, Muhammad & Tiwari, Aviral Kumar & Mubashra, Sana & Shahbaz, Muhammad, 2019. "Modeling volatility of precious metals markets by using regime-switching GARCH models," Resources Policy, Elsevier, vol. 64(C).
    13. Alizadeh, Amir H. & Gabrielsen, Alexandros, 2013. "Dynamics of credit spread moments of European corporate bond indexes," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3125-3144.
    14. Ryan Lemand, 2003. "Should Stock Market Indexes Time Varying Correlations Be Taken Into Account? A Conditional Variance Multivariate Approach," Econometrics 0307004, University Library of Munich, Germany, revised 07 Dec 2020.
    15. Issler, João Victor, 1999. "Estimating and forecasting the volatility of Brazilian finance series using arch models (Preliminary Version)," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 347, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    16. Degiannakis, Stavros & Xekalaki, Evdokia, 2007. "Assessing the Performance of a Prediction Error Criterion Model Selection Algorithm in the Context of ARCH Models," MPRA Paper 96324, University Library of Munich, Germany.
    17. G.R. Pasha & Tahira Qasim & Muhammad Aslam, 2007. "Estimating and Forecasting Volatility of Financial Time Series in Pakistan with GARCH-type Models," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 12(2), pages 115-149, Jul-Dec.
    18. Shen, Zhiwei & Ritter, Matthias, 2016. "Forecasting volatility of wind power production," Applied Energy, Elsevier, vol. 176(C), pages 295-308.
    19. Herrera, Ana María & Hu, Liang & Pastor, Daniel, 2018. "Forecasting crude oil price volatility," International Journal of Forecasting, Elsevier, vol. 34(4), pages 622-635.
    20. Markus Haas, 2004. "Mixed Normal Conditional Heteroskedasticity," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 211-250.

    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:vrs:poicbe:v:12:y:2018:i:1:p:544-556:n:49. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    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.