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Extreme daily changes in U.S. Dollar London inter-bank offer rates

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  • Krehbiel, Tim
  • Adkins, Lee C.

Abstract

The likelihood of extreme daily changes in London Interbank Offer rates are estimated using the peaks-over-threshold method developed from extreme value theory. Value at risk and expected shortfall for high quantiles are produced for the left and right tails of the distributions for each maturity. The Generalized Pareto distribution of the peaks-over-threshold method is found to be unsuitable for modeling exceedances above a high threshold for samples of simple daily changes in the LIBOR. When the series are transformed to logarithmic daily changes, extreme value analysis proceeds smoothly and yields useful information about the relative frequency or magnitudes of extreme events. The main consequence of this is that the risk statistics associated with a given change in the LIBOR depend on the initial rate level; at higher (lower) interest rates, changes of a given size are more (less) likely to occur.

Suggested Citation

  • Krehbiel, Tim & Adkins, Lee C., 2008. "Extreme daily changes in U.S. Dollar London inter-bank offer rates," International Review of Economics & Finance, Elsevier, vol. 17(3), pages 397-411.
  • Handle: RePEc:eee:reveco:v:17:y:2008:i:3:p:397-411
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    1. Gencay, Ramazan & Selcuk, Faruk, 2004. "Extreme value theory and Value-at-Risk: Relative performance in emerging markets," International Journal of Forecasting, Elsevier, vol. 20(2), pages 287-303.
    2. Bali, Turan G. & Neftci, Salih N., 2003. "Disturbing extremal behavior of spot rate dynamics," Journal of Empirical Finance, Elsevier, vol. 10(4), pages 455-477, September.
    3. Jansen, Dennis W & de Vries, Casper G, 1991. "On the Frequency of Large Stock Returns: Putting Booms and Busts into Perspective," The Review of Economics and Statistics, MIT Press, vol. 73(1), pages 18-24, February.
    4. Turan G. Bali, 2003. "An Extreme Value Approach to Estimating Volatility and Value at Risk," The Journal of Business, University of Chicago Press, vol. 76(1), pages 83-108, January.
    5. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    6. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    7. Longin, Francois M., 2000. "From value at risk to stress testing: The extreme value approach," Journal of Banking & Finance, Elsevier, vol. 24(7), pages 1097-1130, July.
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    Cited by:

    1. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    2. Olson, Eric & Miller, Scott & Wohar, Mark E., 2012. "“Black Swans” before the “Black Swan” evidence from international LIBOR–OIS spreads," Journal of International Money and Finance, Elsevier, vol. 31(6), pages 1339-1357.
    3. Saralees Nadarajah & Bo Zhang & Stephen Chan, 2014. "Estimation methods for expected shortfall," Quantitative Finance, Taylor & Francis Journals, vol. 14(2), pages 271-291, February.

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