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Forecasting of Volatility in Stock Exchange Markets by MS-GARCH Approach: An Application of Borsa Istanbul

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  • Abdulkadir Kaya
  • İkram Yusuf Yarbaşı

Abstract

The volatility observed in securities markets has an important influence on the decision making processes of stock market stakeholders. In this study, the volatilities in BIST100 index which represents Borsa Istanbul was analyzed. For this purpose, BIST100 index closing data for the period of 03.01.1988-20.04.2018 was used in the study. The BIST100 index was analyzed by Markov regime switching GARCH (MS-GARCH) with three regimes, standard, high and low volatility regimes. As a result of the triple regime MS-GARCH intensive analysis, the existence of endogenous regimens was determined, in which the regime coefficients considered for the index were statistically significant. When the possibilities of regime transitions are analyzed, it is determined that the probability of continuing the standard volatility regime is 0.62, the probability of transition to low volatility regime is 0.23 and the probability of transition to high volatility regime is 0.145. Moreover, it was determined that the possibilities of regime passage in 5 and 20 days are very close to each other.

Suggested Citation

  • Abdulkadir Kaya & İkram Yusuf Yarbaşı, 2021. "Forecasting of Volatility in Stock Exchange Markets by MS-GARCH Approach: An Application of Borsa Istanbul," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 6(1), pages 16-35.
    • Handle: RePEc:ahs:journl:v:6:y:2021:i:1:p:16-35
    DOI: 10.30784/epfad.740815
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    References listed on IDEAS

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    1. 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.
    2. Turner, Christopher M. & Startz, Richard & Nelson, Charles R., 1989. "A Markov model of heteroskedasticity, risk, and learning in the stock market," Journal of Financial Economics, Elsevier, vol. 25(1), pages 3-22, November.
    3. Simon van Norden & Huntley Schaller & ), 1995. "Regime Switching in Stock Market Returns," Econometrics 9502002, University Library of Munich, Germany.
    4. Stephen Satchell, 2011. "Regime-switching in financial markets," Journal of Asset Management, Palgrave Macmillan, vol. 12(5), pages 309-309, November.
    5. Jun Tu, 2010. "Is Regime Switching in Stock Returns Important in Portfolio Decisions?," Management Science, INFORMS, vol. 56(7), pages 1198-1215, July.
    6. Moore, Tomoe & Wang, Ping, 2007. "Volatility in stock returns for new EU member states: Markov regime switching model," International Review of Financial Analysis, Elsevier, vol. 16(3), pages 282-292.
    7. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    8. Satchell, Stephen & Knight, John, 2007. "Forecasting Volatility in the Financial Markets," Elsevier Monographs, Elsevier, edition 3, number 9780750669429.
    9. David Ardia, 2008. "Financial Risk Management with Bayesian Estimation of GARCH Models," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-540-78657-3, October.
    10. Ardia, David & Bluteau, Keven & Rüede, Maxime, 2019. "Regime changes in Bitcoin GARCH volatility dynamics," Finance Research Letters, Elsevier, vol. 29(C), pages 266-271.
    11. 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.
    12. Franc Klaassen, 2002. "Improving GARCH volatility forecasts with regime-switching GARCH," Empirical Economics, Springer, vol. 27(2), pages 363-394.
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    More about this item

    Keywords

    Volatility; Financial Risk; Markov Switching; BIST;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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