Operational Loss Data Collection: A Literature Review

Esterhuysen, Ja'nel; Styger, Paul; Vuuren, Gary Wayne van

doi:10.1007/s40745-018-0139-2

Cited by 8 publications

(5 citation statements)

References 80 publications

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“…First, there are differences in constructing the operational loss databases: the Fitch First qualitative database used by Li and Moosa (2015) and SAS Global used in this research. Fitch First, re-named to Algo First in 2005, has a loss reporting threshold of US $1 million and focuses primarily on the US financial services industry (Wei et al 2018). In contrast, SAS Global has a loss reporting threshold of US $100,000, making the SAS Global a more comprehensive and accurate database (Wei et al 2018).…”

Section: Discussionmentioning

confidence: 99%

“…Fitch First, re-named to Algo First in 2005, has a loss reporting threshold of US $1 million and focuses primarily on the US financial services industry (Wei et al 2018). In contrast, SAS Global has a loss reporting threshold of US $100,000, making the SAS Global a more comprehensive and accurate database (Wei et al 2018). Second, a larger sample is used in this research.…”

Section: Discussionmentioning

confidence: 99%

“…Operational loss data are retrieved from the SAS OpRisk Global database, the world's most comprehensive and accurate repository of external loss events (SAS 2015; Wei et al 2018). The database includes all publicly reported operational losses in excess of US$100,000 across all industries worldwide.…”

Section: Methodsmentioning

confidence: 99%

“…The database includes all publicly reported operational losses in excess of US$100,000 across all industries worldwide. SAS OpRisk Global can be considered as the broadest possible statistical sample of large operational risk events (SAS 2015; Wei et al 2018). Data are retrieved for the period of 2008-2019 covering 132 countries and 8,144 loss events.…”

Section: Methodsmentioning

confidence: 99%

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Does governance matter? Country-level determinants of operational risk

Berlinger

Keresztúri

Lublóy

et al. 2021

SocEc

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The severity and frequency of operational loss events show high variability across the globe. In this paper, we first examine the extent to which the quality of country-level governance measured by the Worldwide Governance Indicators explains cross-country variation of operational losses. We use the comprehensive database of SAS OpRisk Global for the period of 2008–2019 covering 132 countries and 8,144 loss events with a total loss amount of almost 490 billion USD. Our findings indicate that the governance indicators lost their explanatory power over the past decades, which contradicts the academic consensus and calls for new explanatory variables. To find these variables, we hypothesize that the changes are driven by some important megatrends such as economic development and technological advancement, globalization, and sustainability. Accordingly, we propose an extended model where the number of mobile subscribers, the export to GDP ratio, and the poverty headcount ratio were significant for the frequency. For severity, only GDP is a significant and robust explanatory variable. Investors, regulators, and analysts should, therefore, concentrate on these factors if they wish to model, manage, or mitigate operational risks.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Does governance matter? Country-level determinants of operational risk

Berlinger

Keresztúri

Lublóy

et al. 2021

SocEc

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“… 6 Wei et al ( 2018 ) review 66 databases for operational risk and show that the SAS OpRisk database is the largest and most comprehensive database across all industries worldwide that is publicly available. Even larger, but not publicly available is the Operational Riskdata eXchange (ORX) database, which is a consortium-based database.…”

mentioning

confidence: 99%

Heterogeneity in cyber loss severity and its impact on cyber risk measurement

Eling

Jung

2022

Risk Manag

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We use the world’s largest publicly available dataset of operational risk to model cyber losses and show that the Tweedie model best fits the cyber loss severity in the financial industry. Three key determinants of loss severity are firm size, contagion risk and legal liability. We also measure the size of risk based on the estimation results and show a large degree of heterogeneity across financial firms. The results are particularly relevant with respect to the recent discussion on simplifying operational risk capital requirements and reiterate the importance of considering individual firm characteristics when modelling operational losses.

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Environmental policy and stakeholder engagement: Incident‐based, cross‐country analysis of firm‐level greenwashing practices

Keresztúri,

Berlinger,

Lublóy

2024

Corp Soc Responsibility Env

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In this study, we investigate how internal and external monitoring systems can help combat greenwashing. We propose a novel, incident‐based measure to investigate the greenwashing behavior of 1218 large and mid‐cap companies across different industries between 2008 and 2020. These companies are constituents of the MSCI World Index, covering 23 developed market economies. We consider a company to be engaged in greenwashing if it improves its environmental ESG subscore while simultaneously being responsible for severe environmental damage. According to our greenwashing indicator, about 7% of the companies were involved in greenwashing at least once during the 13‐year sample period. The proportion of greenwashing companies is highest in the energy, utilities, and materials industries. We find evidence that both internal and external monitoring mechanisms can be effective in deterring companies from adopting greenwashing strategies. Firms with more independent board members, attracting more attention from the investors, and headquartered in countries where the population is more environmentally aware are significantly less likely to engage in greenwashing. The awareness of the population is a key factor especially in the energy, utility, and material sectors.

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Operational Loss Data Collection: A Literature Review

Cited by 8 publications

References 80 publications

Does governance matter? Country-level determinants of operational risk

Does governance matter? Country-level determinants of operational risk

Heterogeneity in cyber loss severity and its impact on cyber risk measurement

Environmental policy and stakeholder engagement: Incident‐based, cross‐country analysis of firm‐level greenwashing practices

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