A copula-based simulation model for supply portfolio risk

IF 0.4 4区经济学 Q4 BUSINESS, FINANCE

Journal of Operational Risk Pub Date : 2011-09-01 DOI:10.21314/JOP.2011.093

Halis Sak, Ç. Haksöz

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.

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基于copula的供给组合风险仿真模型

本文提出了一种基于copula的供应组合风险仿真模型。我们为在伦敦金属交易所(LME)交易的商品金属的供应链合约组合展示了我们的方法。通过对LME 6种大宗商品金属现货价格数据的分析，我们采用了具有t边际和广义双曲边际的t-copula依赖结构来表示对数收益。我们还提供了有效的模拟算法，使用正态和t- copula依赖结构的重要抽样来量化风险度量、风险供应和条件风险供应。对6种商品金属组合的数值算例表明，本文提出的方法成功地减小了模拟的方差。本文还对耦合函数的选择进行了数值敏感性分析。据我们所知，这是第一篇针对供应链合同组合提出高效仿真算法的论文，该组合具有基于copula的依赖结构和广义双曲边际。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Operational Risk BUSINESS, FINANCE-

CiteScore

1.00

自引率

40.00%

发文量

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.