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The Use of Risk and Return for Testing the Stability of Stock Markets

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Listed:
  • Viorica Chirila

    (University Alexandru Ioan Cuza of Iasi)

  • Ciprian Chirila

    (University Alexandru Ioan Cuza of Iasi)

Abstract

The European Central Bank stipulates that a financial system is stable if the financial risks are evaluated and rewarded correctly and if the economic and financial shocks are absorbed. When analyzing the return and volatility of the stock exchanges we may ascertain that a stock exchange is stable if there is a connection between return and volatility and if the shocks determined by the new positive and negative information do not cause significant changes of the volatility. We took into consideration the values of the indices of stock markets from Holland (AEX), Belgium (BEL), Romania (BET), Hungary (BUX), Germany (DAX), France (CAC), Czech Republic (PX), Slovakia (SAX), Austria (ATX), Estonia (OMXT), Latvia (OMXR) and Lithuania (OMXV). In order to test the relationship between return-volatility and volatility asymmetry we estimated a GJR-GARCH-M model. The results confirm the lack of existence of a correlation between return and volatility for the entire period under analysis and the existence of the volatility asymmetry.

Suggested Citation

  • Viorica Chirila & Ciprian Chirila, 2014. "The Use of Risk and Return for Testing the Stability of Stock Markets," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 10(2), pages 182-192, April.
  • Handle: RePEc:dug:actaec:y:2014:i:2:p:182-192
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    References listed on IDEAS

    as
    1. Allen, William A. & Wood, Geoffrey, 2006. "Defining and achieving financial stability," Journal of Financial Stability, Elsevier, vol. 2(2), pages 152-172, June.
    2. 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.
    3. Sei‐Wan Kim & Bong‐Soo Lee, 2008. "Stock Returns, Asymmetric Volatility, Risk Aversion, And Business Cycle: Some New Evidence," Economic Inquiry, Western Economic Association International, vol. 46(2), pages 131-148, April.
    4. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    5. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    6. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    7. Radu Lupu & Iulia Lupu, 2007. "Testing for Heteroskedasticity on the Bucharest Stock Exchange," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 10(23), pages 19-28, June.
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