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Intra-Day Seasonality in Foreign Market Transactions

  • Kevin Dowd

    (The University of Nottingham, UK)

  • John Cotter

    (University College Dublin, Ireland)

Spectral risk measures are attractive risk measures as they allow the user to obtain risk measures that reflect their subjective risk-aversion. This paper examines spectral risk measures based on an exponential utility function, and finds that these risk measures have nice intuitive properties. It also discusses how they can be estimated using numerical quadrature methods, and how confidence intervals for them can be estimated using a parametric bootstrap. Illustrative results suggest that estimated exponential spectral risk measures obtained using such methods are quite precise in the presence of normally distributed losses.

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Paper provided by Geary Institute, University College Dublin in its series Working Papers with number 200746.

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Length: 17 pages
Date of creation: 24 Jun 2011
Date of revision:
Handle: RePEc:ucd:wpaper:2007/46
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  1. John Cotter & Kevin Dowd, 2011. "Extreme Spectral Risk Measures: An Application to Futures Clearinghouse Margin Requirements," Papers 1103.5653,
  2. Jon Danielsson & Richard Payne, 1999. "Real Trading Patterns and Prices in Spot Foreign Exchange Markets," FMG Discussion Papers dp320, Financial Markets Group.
  3. Martin D.D. Evans & Richard K. Lyons, 1999. "Order Flow and Exchange Rate Dynamics," NBER Working Papers 7317, National Bureau of Economic Research, Inc.
  4. 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.
  5. Michael A. Goldstein & Kenneth A. Kavajecz, . "Liquidity Provision during Circuit Breakers and Extreme Market Movements," Rodney L. White Center for Financial Research Working Papers 01-00, Wharton School Rodney L. White Center for Financial Research.
  6. Harris, Lawrence & Hasbrouck, Joel, 1996. "Market vs. Limit Orders: The SuperDOT Evidence on Order Submission Strategy," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 31(02), pages 213-231, June.
  7. Huisman, Ronald, et al, 2001. "Tail-Index Estimates in Small Samples," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 208-16, April.
  8. Mittnik, Stefan & Paolella, Marc S. & Rachev, Svetlozar T., 2000. "Diagnosing and treating the fat tails in financial returns data," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 389-416, November.
  9. John Cotter, 2011. "Varying the VaR for Unconditional and Conditional Environments," Papers 1103.5649,
  10. Handa, Puneet & Schwartz, Robert A, 1996. " Limit Order Trading," Journal of Finance, American Finance Association, vol. 51(5), pages 1835-61, December.
  11. de Haan, Laurens & Resnick, Sidney I. & Rootzén, Holger & de Vries, Casper G., 1989. "Extremal behaviour of solutions to a stochastic difference equation with applications to arch processes," Stochastic Processes and their Applications, Elsevier, vol. 32(2), pages 213-224, August.
  12. Fishburn, Peter C, 1977. "Mean-Risk Analysis with Risk Associated with Below-Target Returns," American Economic Review, American Economic Association, vol. 67(2), pages 116-26, March.
  13. Koedijk, Kees G & Kool, Clemens J M, 1992. "Tail Estimates of East European Exchange Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(1), pages 83-96, January.
  14. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
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