Integrating Structure Time Series Forecasting and Multicriteria Decision Analysis for Adaptive Operational Risk Assessment: An Empirical Study Using Real-Time Data

Abstract

This study presents a framework for integrating Multi-Criteria Decision Analysis (MCDA) and Structure Time Series (STS) prediction for multifaceted 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 by Python, and employs the model to perform an empirical assessment of 161 countries’ operational risk, using the Armed Conflict Location & Event Data Project’s (ACLED) real-time data. A comprehensive analysis of the model’s functionality, quality, and sensitivity is provided based on the assessment result. Conclusions and limitations are also discussed, highlighting the model’s theoretical novelty and practical implications.