Integrating structure time series forecasting and multicriteria decision analysis for adaptive operational risk assessment: an empirical study using real-time data

Guicang Peng (), Jon Tømmerås Selvik, Eirik Bjorheim Abrahamsen, Knut Erik Bang and Tore Markeset
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Guicang Peng: University of Stavanger
Jon Tømmerås Selvik: University of Stavanger
Eirik Bjorheim Abrahamsen: University of Stavanger
Knut Erik Bang: University of Stavanger
Tore Markeset: University of Stavanger

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 7, No 24, 3162-3181

Abstract: Abstract This study propose a framework for integrating Multi-Criteria Decision Analysis (MCDA) and Structure Time Series (STS) prediction for multivariate operational risk assessment often with highly dynamic risk determinants. In particular, by utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) as an MCDA method, the framework is able to prioritize risk determinants according to their inherent uncertainties’ impacts on their respective operational objectives, and by employing Seasonal Autoregressive Integrated Moving Average (SARIMA) as an STS technique, the framework emphasizes real-time knowledge utilization for iteratively reducing uncertainty. By integrating SARIMA and TOPSIS, the framework aims to construct a multivariate operational risk assessment profile that is prioritized and continuously updated by the latest data and knowledge. Based on the proposed framework, the study constructs a mathematical model coded in Python to perform an empirical assessment of 161 countries’ operational risk using real-time data from the Armed Conflict Location & Event Data Project’s. A comprehensive analysis of the model’s functionality, quality, and sensitivity based on the assessment result is provided. Conclusions and limitations are also discussed, highlighting the model’s theoretical novelty and practical implications.

Keywords: Operational risk assessment; MultiCriteria decision analysis; Structure time series prediction (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-024-02322-x

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