Computing Optimal Recovery Policies for Financial Markets
Computing Optimal Recovery Policies for Financial Markets
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DOI：
10.1287/opre.1120.1112
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发表时间：
2010-12
期刊：
影响因子：
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通讯作者：
F. Benth;G. Dahl;C. Mannino
中科院分区：
文献类型：
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作者：
F. Benth;G. Dahl;C. Mannino
当前的金融危机激发了对金融系统相关违约的研究。在本文中，我们重点关注这样一个基于马尔可夫随机场的模型。这是一个概率模型，其中违约概率的不确定性包含了专家对违约风险的意见（基于各种信用评级）。我们考虑采用双层优化模型来寻找最佳恢复政策：在给定固定预算的情况下应该支持哪些公司。这与寻找默认代理集的最大似然估计量的问题密切相关，我们展示了如何使用组合方法有效地计算这个解决方案。我们还证明了此类最优解决方案的属性。还给出了估计模型参数的实用程序。计算示例和实验表明我们的方法可以为多达约 100 家公司找到最佳恢复策略。总体方法是根据有关斯堪的纳维亚主要银行和公共贷款的现实问题进行评估的。据我们所知，这是将组合优化技术应用于这一重要且不断扩展的违约风险分析领域的首次尝试。
The current financial crisis motivates the study of correlated defaults in financial systems. In this paper we focus on such a model which is based on Markov random fields. This is a probabilistic model where uncertainty in default probabilities incorporates expert's opinions on the default risk (based on various credit ratings). We consider a bilevel optimization model for finding an optimal recovery policy: which companies should be supported given a fixed budget. This is closely linked to the problem of finding a maximum likelihood estimator of the defaulting set of agents, and we show how to compute this solution efficiently using combinatorial methods. We also prove properties of such optimal solutions. A practical procedure for estimation of model parameters is also given. Computational examples are presented and experiments indicate that our methods can find optimal recovery policies for up to about 100 companies. The overall approach is evaluated on a real-world problem concerning the major banks in Scandinavia and public loans. To our knowledge this is a first attempt to apply combinatorial optimization techniques to this important, and expanding, area of default risk analysis.