Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230779
Authors: 
Wesselhöfft, Niels
Härdle, Wolfgang Karl
Year of Publication: 
2019
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2019-003
Abstract: 
Weekly, quarterly and yearly risk measures are crucial for risk reporting according to Basel III and Solvency II. For the respective data frequencies, the authors show in a simulation and backtest study that available data series are not sufficient in order to estimate Value at Risk and Expected Shortfall sufficiently, given confidence levels of 99.9% and 99.99%. Accordingly, this paper presents a semi-parametric estimation method, rescaling data from high- to low-frequency which allows to obtain significantly more data points for the estimation of the respective risk measures. The presented methodology in the α-stable framework, which is able to mimic multifractal behavior in asset returns, provides tail events which never occurred in the original low-frequency dataset.
Subjects: 
high-frequency
multifractal
stable distribution
rescaling
risk management
Value at Risk
quantile distribution
JEL: 
C14
C22
C46
C53
G32
Document Type: 
Working Paper

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