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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Guicang Peng & Jon Tømmerås Selvik & Eirik Bjorheim Abrahamsen & Knut Erik Bang & Tore Markeset, 2024. "Integrating structure time series forecasting and multicriteria decision analysis for adaptive operational risk assessment: an empirical study using real-time data," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(7), pages 3162-3181, July.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:7:d:10.1007_s13198-024-02322-x
    DOI: 10.1007/s13198-024-02322-x
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    References listed on IDEAS

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    1. Dalla Valle, L. & Giudici, P., 2008. "A Bayesian approach to estimate the marginal loss distributions in operational risk management," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3107-3127, February.
    2. P. V. Shevchenko & M. V. Wuthrich, 2009. "The Structural Modelling of Operational Risk via Bayesian inference: Combining Loss Data with Expert Opinions," Papers 0904.1067, arXiv.org.
    3. JosÉ Figueira & Salvatore Greco & Matthias Ehrogott, 2005. "Multiple Criteria Decision Analysis: State of the Art Surveys," International Series in Operations Research and Management Science, Springer, number 978-0-387-23081-8, January.
    4. Jyrki Wallenius & James S. Dyer & Peter C. Fishburn & Ralph E. Steuer & Stanley Zionts & Kalyanmoy Deb, 2008. "Multiple Criteria Decision Making, Multiattribute Utility Theory: Recent Accomplishments and What Lies Ahead," Management Science, INFORMS, vol. 54(7), pages 1336-1349, July.
    5. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
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    1. Roman Vavrek, 2024. "Spatial Interpretation of Multi-Criteria Analysis: A Case Study with a Decreasing Number of Criteria and Subjective Approach to Determining Their Importance," Mathematics, MDPI, vol. 12(22), pages 1-21, November.

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