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Price Volatility and Financial Instability

  • H. L. Leon
  • DeLisle Worrell
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    Statistical measures of the volatility of exchange rates, interest rates, and stock prices are estimated for a number of countries. Periods of high volatility are identified and compared with periods of financial difficulty. The results indicate that GARCH models of volatility could be potentially useful in assessing financial soundness. Daily data are more revealing, but monthly series allow comparisons among many countries. Country specific models may be needed for more reliable inference.

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    File URL: http://www.imf.org/external/pubs/cat/longres.aspx?sk=4096
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    Paper provided by International Monetary Fund in its series IMF Working Papers with number 01/60.

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    Length: 43
    Date of creation: 01 Apr 2001
    Date of revision:
    Handle: RePEc:imf:imfwpa:01/60
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    1. Karpoff, Jonathan M., 1987. "The Relation between Price Changes and Trading Volume: A Survey," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(01), pages 109-126, March.
    2. Burkhard Drees & Ceyla Pazarbasioglu, 1998. "The Nordic Banking Crisis; Pitfalls in Financial Liberalization," IMF Occasional Papers 161, International Monetary Fund.
    3. Pagan, A.R. & Schwert, G.W., 1989. "Alternative Models For Conditional Stock Volatility," Papers 89-02, Rochester, Business - General.
    4. Caprio, Gerard Jr. & Klingebiel, Daniela, 1996. "Bank insolvencies : cross-country experience," Policy Research Working Paper Series 1620, The World Bank.
    5. Ghysels, E. & Harvey, A. & Renault, E., 1995. "Stochastic Volatility," Papers 95.400, Toulouse - GREMAQ.
    6. Diebold & Lopez, . "Modeling Volatility Dynamics," Home Pages _062, University of Pennsylvania.
    7. Bookstaber, Richard M & McDonald, James B, 1987. "A General Distribution for Describing Security Price Returns," The Journal of Business, University of Chicago Press, vol. 60(3), pages 401-24, July.
    8. Paul Louis Ceriel Hilbers & Alfredo Mario Leone & Mahinder Singh Gill & Owen Evens, 2000. "Macroprudential Indicators of Financial System Soundness," IMF Occasional Papers 192, International Monetary Fund.
    9. Geoffrey Booth, G. & Hatem, John & Virtanen, Ilkka & Yli-Olli, Paavo, 1992. "Stochastic modeling of security returns: Evidence from the Helsinki Stock Exchange," European Journal of Operational Research, Elsevier, vol. 56(1), pages 98-106, January.
    10. Baillie, R.T. & Degennaro, R.P., 1988. "Stock Returns And Volatility," Papers 8803, Michigan State - Econometrics and Economic Theory.
    11. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    12. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    13. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
    14. E Philip Davis, 1999. "Financial data needs for macroprudential surveillance - What are the key indicators of risks to domestic financial stability?," Lectures, Centre for Central Banking Studies, Bank of England, number 2, November.
    15. Lamoureux, Christopher G & Lastrapes, William D, 1990. " Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects," Journal of Finance, American Finance Association, vol. 45(1), pages 221-29, March.
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