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Lithuanian stock market analysis using a set of Garch models

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  • Deimante Teresiene

Abstract

This article analyses the main factors that influence stock price volatility. The author offers a three‐stage system for explaning a set of stock price volatility factors. The main point is to pay attention to investor's psychology as the main factor of price volatility. For practical analysis the returns of the OMXV index and stock prices of the Lithuanian stock market are taken and applied to a set of GARCH models. The main idea is to choose the best of the general autoregressive conditional heteroskedasticity models (GARCH) for OMXV index and all sectors. All models are ranged according to their ability to model stock price return. The main tendencies of the Lithuanian stock market are also analysed in this article by highlighting the leverage effect.

Suggested Citation

  • Deimante Teresiene, 2009. "Lithuanian stock market analysis using a set of Garch models," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 10(4), pages 349-360, August.
    • Handle: RePEc:taf:jbemgt:v:10:y:2009:i:4:p:349-360
    DOI: 10.3846/1611-1699.2009.10.349-360
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    References listed on IDEAS

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    1. Steven A. Sharpe, 1999. "Stock prices, expected returns, and inflation," Finance and Economics Discussion Series 1999-02, Board of Governors of the Federal Reserve System (U.S.).
    2. Grant McQueen, 2004. "Whence GARCH? A Preference-Based Explanation for Conditional Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 17(4), pages 915-949.
    3. Black, Angela & Fraser, Patricia & Groenewold, Nicolaas, 2003. "U.S. stock prices and macroeconomic fundamentals," International Review of Economics & Finance, Elsevier, vol. 12(3), pages 345-367.
    4. Mr. Norbert Funke & Mr. Akimi Matsuda, 2002. "Macroeconomic News and Stock Returns in the United States and Germany," IMF Working Papers 2002/239, International Monetary Fund.
    5. Charlotte Christiansen & Angelo Ranaldo, 2007. "Realized bond—stock correlation: Macroeconomic announcement effects," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(5), pages 439-469, May.
    6. Canto, Bea & Kräussl, Roman, 2006. "Stock market interactions and the impact of macroeconomic news: Evidence from high frequency data of European futures markets," CFS Working Paper Series 2006/25, Center for Financial Studies (CFS).
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    Cited by:

    1. Saralees Nadarajah & Emmanuel Afuecheta & Stephen Chan, 2015. "GARCH modeling of five popular commodities," Empirical Economics, Springer, vol. 48(4), pages 1691-1712, June.
    2. Yu Hsing, 2011. "Macroeconomic Variables and the Stock Market: the Case of Lithuania," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 3(1), pages 031-037, June.
    3. Iglesias, Emma M., 2015. "Value at Risk and expected shortfall of firms in the main European Union stock market indexes: A detailed analysis by economic sectors and geographical situation," Economic Modelling, Elsevier, vol. 50(C), pages 1-8.
    4. Imlak Shaikh & Puja Padhi, 2013. "Macroeconomic Announcements and the Implied Volatility Index: Evidence from India VIX," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 7(4), pages 417-442, November.

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