Operational Risk Measurement: An Optimal Timescale Selection Method Based on Information Entropy

Yanpeng Chang, Yinghui Wang (), Jianping Li and Xiaoqian Zhu
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Yanpeng Chang: Institutes of Science and Development, Chinese Academy of Sciences, Haidian District, Beijing 100190, P. R. China2School of Public Policy and Management, University of Chinese Academy of Sciences, Shijingshan District, Beijing 100049, P. R. China
Yinghui Wang: School of Economics and Management, University of Chinese Academy of Sciences, Haidian District, Beijing 100190, P. R. China
Jianping Li: School of Economics and Management, University of Chinese Academy of Sciences, Haidian District, Beijing 100190, P. R. China4MOE Social Science Laboratory of Digital Economic, Forecasts and Policy Simulation at UCAS, Haidian District, Beijing 100190, P. R. China
Xiaoqian Zhu: School of Economics and Management, University of Chinese Academy of Sciences, Haidian District, Beijing 100190, P. R. China4MOE Social Science Laboratory of Digital Economic, Forecasts and Policy Simulation at UCAS, Haidian District, Beijing 100190, P. R. China

International Journal of Information Technology & Decision Making (IJITDM), 2025, vol. 24, issue 03, 713-741

Abstract: Operational risk capital is typically allocated on an annual timescale in extant research, while ignoring that the dependence between operational risk events may differ on different timescales, such as semi-annual and quarterly. This study examines whether operational risk dependence structures differ on multiple timescales and proposes an optimal timescale selection method based on information entropy theory to obtain more reasonable operational risk measurement results. Different dependence modeling methods, including linear correlation and copula functions, are employed to analyze the dependence structures on multiple timescales. The information changes between different timescales are calculated to select the optimal timescale. The empirical analysis is based on 2,024 operational risk events from 1994 to 2017 in the Chinese Operational Loss Database (COLD), which is one of the largest external operational risk datasets for the Chinese banking industry. The results reveal significant differences in operational risk dependence at different timescales which affect the operational risk measurement results. Crucially, the quarterly timescale should be considered for more reasonable measurements. The findings provide important insights for considering a more reasonable allocation of operational risk capital under multiple timescale dependence.

Keywords: Operational risk; multi-timescale; dependence modeling; information entropy; loss distribution approach (LDA) (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0219622023410055

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