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Spillover effect: A study for major capital markets and Romania capital market

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  • Cristina Belciuganu

Abstract

In this paper we focus our attention on the tail risk and how different capital markets are influencing each other. Previous studies have detected return and volatility across countries during crises periods. Using the well-know Value at Risk (VaR) measure for heavy tailed financial returns, our objective is to detect if the information for a negative shock in a foreign market helps the forecast of the behavior of another market. We calculate 1 day, 95% and 99% Value at Risk for major US stock indices- S&P 500, NASDAQ 100, DJ INDUSTRIALS, major European stock indices – CAC 40, FTSE100, DAX30 and for Romanian stock index-BET. The VaR for each index is calculated the following techniques: Historical Simulation, Variance Approach and Extreme Value Theory. Spillover effects being the influence of one market on others, is examined using the Granger causality, for daily changes of the VAR series.

Suggested Citation

  • Cristina Belciuganu, 2009. "Spillover effect: A study for major capital markets and Romania capital market," Advances in Economic and Financial Research - DOFIN Working Paper Series 29, Bucharest University of Economics, Center for Advanced Research in Finance and Banking - CARFIB.
    • Handle: RePEc:cab:wpaefr:29
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    References listed on IDEAS

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    1. Robert Engle, 2004. "Risk and Volatility: Econometric Models and Financial Practice," American Economic Review, American Economic Association, vol. 94(3), pages 405-420, June.
    2. Robert Engle, 2001. "GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 157-168, Fall.
    3. Ser-Huang Poon, 2004. "Extreme Value Dependence in Financial Markets: Diagnostics, Models, and Financial Implications," The Review of Financial Studies, Society for Financial Studies, vol. 17(2), pages 581-610.
    4. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    5. Lin, Wen-Ling & Engle, Robert F & Ito, Takatoshi, 1994. "Do Bulls and Bears Move across Borders? International Transmission of Stock Returns and Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 7(3), pages 507-538.
    6. Ho, Thomas S. Y. & Lee, Sang Bin, 2004. "The Oxford Guide to Financial Modeling: Applications for Capital Markets, Corporate Finance, Risk Management and Financial Institutions," OUP Catalogue, Oxford University Press, number 9780195169621.
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