{"title":"Credible value-at-risk","authors":"Peter Mitic","doi":"10.21314/jop.2023.005","DOIUrl":null,"url":null,"abstract":"Some value-at-risk (VaR) calculations yield extremely large results, which are often rejected on the grounds that they are inconsistent with the operational loss profile of the organization concerned. Therefore, an informal limit has effectively been placed on VaR. Hitherto, the concept of a “maximum” VaR has rarely been considered. In this paper, we propose an objective and simple process to determine whether or not a calculated VaR is “too large”, and thereby give a precise definition of “too large” in this context. A simple decision process, using a constant multiplier of the annualized sum of losses, is proposed to reject distributions that produce extremely high VaR values. This decision process works in conjunction with a bootstrap to also reject distributions that produce very low VaR values. Together, they determine whether or not a calculated VaR value is “credible”. A practical guide to using the combined procedures is given, along with a discussion of potential problems and viable solutions to those problems.","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"9 1","pages":"0"},"PeriodicalIF":0.4000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Operational Risk","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21314/jop.2023.005","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 1
Abstract
Some value-at-risk (VaR) calculations yield extremely large results, which are often rejected on the grounds that they are inconsistent with the operational loss profile of the organization concerned. Therefore, an informal limit has effectively been placed on VaR. Hitherto, the concept of a “maximum” VaR has rarely been considered. In this paper, we propose an objective and simple process to determine whether or not a calculated VaR is “too large”, and thereby give a precise definition of “too large” in this context. A simple decision process, using a constant multiplier of the annualized sum of losses, is proposed to reject distributions that produce extremely high VaR values. This decision process works in conjunction with a bootstrap to also reject distributions that produce very low VaR values. Together, they determine whether or not a calculated VaR value is “credible”. A practical guide to using the combined procedures is given, along with a discussion of potential problems and viable solutions to those problems.
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.