{"title":"前入侵企业网络风险:来自互联网协议网络的证据","authors":"Bill Francis, Wenyao Hu, Thomas D. Shohfi","doi":"10.21314/jop.2021.007","DOIUrl":null,"url":null,"abstract":"Previous event studies of corporate cyber-risk have been limited to successful attacks on public firms but are biased samples constructed based on the economic magnitude of equity losses. To address this selection bias, we construct a larger and more representative sample of cyber intrusions only to find diminished negative equity (and insignificant corporate bond) market reactions compared to these prior studies. To identify cyber-risk irrespective of observing successful attacks, we match public firms to Internet protocol (IP) network data from the American Registry for Internet Numbers (ARIN) from 1991 to 2017. We find that both stockholders and creditors incorporate external IP network size into firm value. Further, debt and equity market reactions to cyberattacks are mitigated for firms with registered IP networks and that have larger network deployments. Overall, our study demonstrates an important public data source that can help institutions proxy for and more accurately price firm cybersecurity risk.","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"1 1","pages":""},"PeriodicalIF":0.4000,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Ex-intrusion corporate cyber risk: evidence from internet protocol networks\",\"authors\":\"Bill Francis, Wenyao Hu, Thomas D. Shohfi\",\"doi\":\"10.21314/jop.2021.007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Previous event studies of corporate cyber-risk have been limited to successful attacks on public firms but are biased samples constructed based on the economic magnitude of equity losses. To address this selection bias, we construct a larger and more representative sample of cyber intrusions only to find diminished negative equity (and insignificant corporate bond) market reactions compared to these prior studies. To identify cyber-risk irrespective of observing successful attacks, we match public firms to Internet protocol (IP) network data from the American Registry for Internet Numbers (ARIN) from 1991 to 2017. We find that both stockholders and creditors incorporate external IP network size into firm value. Further, debt and equity market reactions to cyberattacks are mitigated for firms with registered IP networks and that have larger network deployments. Overall, our study demonstrates an important public data source that can help institutions proxy for and more accurately price firm cybersecurity risk.\",\"PeriodicalId\":54030,\"journal\":{\"name\":\"Journal of Operational Risk\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2021-09-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Operational Risk\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.21314/jop.2021.007\",\"RegionNum\":4,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Operational Risk","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.21314/jop.2021.007","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
Ex-intrusion corporate cyber risk: evidence from internet protocol networks
Previous event studies of corporate cyber-risk have been limited to successful attacks on public firms but are biased samples constructed based on the economic magnitude of equity losses. To address this selection bias, we construct a larger and more representative sample of cyber intrusions only to find diminished negative equity (and insignificant corporate bond) market reactions compared to these prior studies. To identify cyber-risk irrespective of observing successful attacks, we match public firms to Internet protocol (IP) network data from the American Registry for Internet Numbers (ARIN) from 1991 to 2017. We find that both stockholders and creditors incorporate external IP network size into firm value. Further, debt and equity market reactions to cyberattacks are mitigated for firms with registered IP networks and that have larger network deployments. Overall, our study demonstrates an important public data source that can help institutions proxy for and more accurately price firm cybersecurity risk.
期刊介绍:
In December 2017, the Basel Committee published the final version of its standardized measurement approach (SMA) methodology, which will replace the approaches set out in Basel II (ie, the simpler standardized approaches and advanced measurement approach (AMA) that allowed use of internal models) from January 1, 2022. Independently of the Basel III rules, in order to manage and mitigate risks, they still need to be measurable by anyone. The operational risk industry needs to keep that in mind. While the purpose of the now defunct AMA was to find out the level of regulatory capital to protect a firm against operational risks, we still can – and should – use models to estimate operational risk economic capital. Without these, the task of managing and mitigating capital would be incredibly difficult. These internal models are now unshackled from regulatory requirements and can be optimized for managing the daily risks to which financial institutions are exposed. In addition, operational risk models can and should be used for stress tests and Comprehensive Capital Analysis and Review (CCAR). The Journal of Operational Risk also welcomes papers on nonfinancial risks as well as topics including, but not limited to, the following. The modeling and management of operational risk. Recent advances in techniques used to model operational risk, eg, copulas, correlation, aggregate loss distributions, Bayesian methods and extreme value theory. The pricing and hedging of operational risk and/or any risk transfer techniques. Data modeling external loss data, business control factors and scenario analysis. Models used to aggregate different types of data. Causal models that link key risk indicators and macroeconomic factors to operational losses. Regulatory issues, such as Basel II or any other local regulatory issue. Enterprise risk management. Cyber risk. Big data.