Estimating low sampling frequency risk measure by high-frequency data

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IRTG 1792 Discussion Paper ; No. 2019-003

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Specific Distributions; Specific Statistics ; Forecasting Models; Simulation Methods ; Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
• Subject: high-frequency ; multifractal ; stable distribution ; rescaling ; risk management ; Value at Risk ; quantile distribution

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Wesselhöfft, Niels ; Härdle, Wolfgang Karl
• Event: Veröffentlichung
• (who): Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
• (where): Berlin
• (when): 2019

Further information

• Handle: http://hdl.handle.net/10419/230779
• Last update: 10.03.2025, 11:42 AM CET