{"title":"A copula-based simulation model for supply portfolio risk","authors":"Halis Sak, Ç. Haksöz","doi":"10.21314/JOP.2011.093","DOIUrl":null,"url":null,"abstract":"A copula-based simulation model for supply portfolio risk in the presence of dependent breaches of contracts is introduced in this paper. We demonstrate our method for a supply-chain contract portfolio of commodity metals traded at the London Metal Exchange (LME). The analysis of spot price data on six LME com- modity metals leads us to use a t-copula dependence structure with t-marginals and generalized hyperbolic marginals for the log returns. We also provide effi- cient simulation algorithms using importance sampling for the normal and t- copula dependence structure to quantify risk measures, supply-at-risk and condi- tional supply-at-risk. Numerical examples on a portfolio of six commodity metals demonstrate that our proposed method succeeds in decreasing the variance of the simulations. A numerical sensitivity analysis for the choice of the copula function is also provided. To the best of our knowledge, this is the first paper proposing efficient simulation algorithms on a supply-chain contract portfolio that has a copula-based dependence structure with generalized hyperbolic marginals.","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"31 1","pages":"15-38"},"PeriodicalIF":0.4000,"publicationDate":"2011-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Operational Risk","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.21314/JOP.2011.093","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: 12
Abstract
A copula-based simulation model for supply portfolio risk in the presence of dependent breaches of contracts is introduced in this paper. We demonstrate our method for a supply-chain contract portfolio of commodity metals traded at the London Metal Exchange (LME). The analysis of spot price data on six LME com- modity metals leads us to use a t-copula dependence structure with t-marginals and generalized hyperbolic marginals for the log returns. We also provide effi- cient simulation algorithms using importance sampling for the normal and t- copula dependence structure to quantify risk measures, supply-at-risk and condi- tional supply-at-risk. Numerical examples on a portfolio of six commodity metals demonstrate that our proposed method succeeds in decreasing the variance of the simulations. A numerical sensitivity analysis for the choice of the copula function is also provided. To the best of our knowledge, this is the first paper proposing efficient simulation algorithms on a supply-chain contract portfolio that has a copula-based dependence structure with generalized hyperbolic marginals.
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
