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Measuring Tail Thickness under GARCH and an Application to Extreme Exchange Rate Changes

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Author Info

  • Niklas Wagner
  • Terry A. Marsh

Abstract

Accurate modeling of extreme price changes is vital to financial risk management. We examine the small sample properties of adaptive tail index estimators under the class of student-t marginal distribution functions including GARCH and propose a model-based bias-corrected estimation approach. Our simulation results indicate that bias strongly relates to the underlying model and may be positively as well as negatively signed. The empirical study of daily exchange rate changes reveals substantial differences in measured tail-thickness due to small sample bias. As a consequence, high quantile estimation may lead to a substantial underestimation of tail risk.

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File URL: http://128.118.178.162/eps/em/papers/0401/0401008.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 0401008.

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Length: 40 pages
Date of creation: 30 Jan 2004
Date of revision:
Handle: RePEc:wpa:wuwpem:0401008

Note: Type of Document - pdf; prepared on win00; to print on laserjet; pages: 40
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Web page: http://128.118.178.162

Related research

Keywords: fat tails; tail index; stationary marginal distribution; GARCH; Hill estimator; foreign exchange;

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Cited by:
  1. Mohammad Karimi & Marcel-Cristian Voia, 2011. "Identifying Extreme Values of Exchange Market Pressure," Carleton Economic Papers 11-10, Carleton University, Department of Economics.
  2. Emma M. Iglesias & Oliver Linton, 2009. "Estimation of tail thickness parameters from GJR-GARCH models," Economics Working Papers we094726, Universidad Carlos III, Departamento de EconomĂ­a.
  3. Marimoutou, Velayoudoum & Raggad, Bechir & Trabelsi, Abdelwahed, 2009. "Extreme Value Theory and Value at Risk: Application to oil market," Energy Economics, Elsevier, vol. 31(4), pages 519-530, July.
  4. Wagner, Niklas, 2005. "Autoregressive conditional tail behavior and results on Government bond yield spreads," International Review of Financial Analysis, Elsevier, vol. 14(2), pages 247-261.
  5. Tim Bollerslev & Viktor Todorov, 2010. "Jump Tails, Extreme Dependencies, and the Distribution of Stock Returns," CREATES Research Papers 2010-64, School of Economics and Management, University of Aarhus.
  6. VĂȘlayoudom Marimoutou & Bechir Raggad & Abdelwahed Trabelsi, 2006. "Extreme Value Theory and Value at Risk : Application to Oil Market," Working Papers halshs-00410746, HAL.
  7. Alex Yi-Hou Huang & Tsung-Wei Tseng, 2009. "Forecast of value at risk for equity indices: an analysis from developed and emerging markets," Journal of Risk Finance, Emerald Group Publishing, vol. 10(4), pages 393-409, August.

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