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A New Approach for Using Lévy Processes for Determining High-Frequency Value-at-Risk Predictions

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  • Wei Sun
  • Svetlozar Rachev
  • Frank J. Fabozzi

Abstract

"A new approach for using Lévy processes to compute value-at-risk (VaR) using high-frequency data is presented in this paper. The approach is a parametric model using an ARMA(1,1)-GARCH(1,1) model where the tail events are modelled using fractional Lévy stable noise and Lévy stable distribution. Using high-frequency data for the German DAX Index, the VaR estimates from this approach are compared to those of a standard nonparametric estimation method that captures the empirical distribution function, and with models where tail events are modelled using Gaussian distribution and fractional Gaussian noise. The results suggest that the proposed parametric approach yields superior predictive performance." Copyright (c) 2008 The Authors Journal compilation (c) 2008 Blackwell Publishing Ltd.

Suggested Citation

  • Wei Sun & Svetlozar Rachev & Frank J. Fabozzi, 2009. "A New Approach for Using Lévy Processes for Determining High-Frequency Value-at-Risk Predictions," European Financial Management, European Financial Management Association, vol. 15(2), pages 340-361.
  • Handle: RePEc:bla:eufman:v:15:y:2009:i:2:p:340-361
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    File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1468-036X.2008.00467.x
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    Cited by:

    1. Batten, Jonathan A. & Kinateder, Harald & Wagner, Niklas, 2014. "Multifractality and value-at-risk forecasting of exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 71-81.
    2. Chen Yi-Ting & Sun Edward W. & Yu Min-Teh, 2015. "Improving model performance with the integrated wavelet denoising method," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(4), pages 445-467, September.

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