Estimating low sampling frequency risk measure by high-frequency data

Niels Wesselhöfft and Wolfgang Härdle

No 2019-003, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

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.

Keywords: high-frequency; multifractal; stable distribution; rescaling; risk management; Value at Risk; quantile distribution (search for similar items in EconPapers)
JEL-codes: C14 C22 C46 C53 G32 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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