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Summarizing Risk Using Risk Measures and Risk Indices

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  • Cameron A. MacKenzie

Abstract

Our society is fascinated with risk in many different areas and disciplines. One of the main ways to describe and communicate the level of risk is through risk indices, which summarize risk using numbers or categories such as words, letters, or colors. These indices are used to communicate risks to the public, understand how risk is changing over time, compare among different risks, and support decision making. Given the different methods to construct risk indices, including flawed methods such as risk matrices, this article develops specific steps that analysts can follow to create a risk index. This article emphasizes the importance of describing risk with a probability distribution, developing a numerical risk measure that summarizes the probability distribution, and finally translating the risk measure to an index. Measuring the risk is the most difficult part and requires the analyst to summarize a probability distribution into one or possibly a few numbers. The risk measure can then be transformed to a numerical or categorical index. I apply the method outlined in this article to construct a risk index that compares the risk of fatalities in aviation and highway transportation.

Suggested Citation

  • Cameron A. MacKenzie, 2014. "Summarizing Risk Using Risk Measures and Risk Indices," Risk Analysis, John Wiley & Sons, vol. 34(12), pages 2143-2162, December.
  • Handle: RePEc:wly:riskan:v:34:y:2014:i:12:p:2143-2162
    DOI: 10.1111/risa.12220
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    3. Jingyu Liu & Walter W. Piegorsch & A. Grant Schissler & Susan L. Cutter, 2018. "Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 803-823, June.
    4. Alexandre Mornet & Thomas Opitz & Michel Luzi & Stéphane Loisel, 2016. "Wind Storm Risk Management," Working Papers hal-01299692, HAL.
    5. Jianping Li & Chunbing Bao & Dengsheng Wu, 2018. "How to Design Rating Schemes of Risk Matrices: A Sequential Updating Approach," Risk Analysis, John Wiley & Sons, vol. 38(1), pages 99-117, January.
    6. Ren, Rui & Althof, Michael & Härdle, Wolfgang Karl, 2020. "Tail Risk Network Effects in the Cryptocurrency Market during the COVID-19 Crisis," IRTG 1792 Discussion Papers 2020-028, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    7. Silvia Facchinetti & Paolo Giudici & Silvia Angela Osmetti, 2020. "Cyber risk measurement with ordinal data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(1), pages 173-185, March.
    8. Andreas Tsanakas & Pietro Millossovich, 2016. "Sensitivity Analysis Using Risk Measures," Risk Analysis, John Wiley & Sons, vol. 36(1), pages 30-48, January.
    9. Daniel Zängerle & Dirk Schiereck, 2023. "Modelling and predicting enterprise-level cyber risks in the context of sparse data availability," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 48(2), pages 434-462, April.
    10. Busby, J.S., 2019. "The co-evolution of competition and parasitism in the resource-based view: A risk model of product counterfeiting," European Journal of Operational Research, Elsevier, vol. 276(1), pages 300-313.
    11. Zängerle, Daniel & Schiereck, Dirk, 2022. "Modelling and predicting enterprise‑level cyber risks in the context of sparse data availability," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 136276, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    12. Gabriela Costa Dias & Ualison Rébula de Oliveira & Gilson Brito Alves Lima & Vicente Aprigliano Fernandes, 2021. "Risk Management in the Import/Export Process of an Automobile Company: A Contribution for Supply Chain Sustainability," Sustainability, MDPI, vol. 13(11), pages 1-20, May.
    13. Stefan Rass & Sandra König & Stefan Schauer, 2017. "Defending Against Advanced Persistent Threats Using Game-Theory," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-43, January.

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