摘要翻译:
我们提出了信用评级变化的马尔可夫链模型。我们不对被评级公司的资产价值使用任何分布假设,而是直接对评级转换过程进行建模。模型的参数估计采用最大似然方法,使用历史评级转移和启发式全局优化技术。在债券投资组合风险管理的背景下,我们将该模型与GLMM模型进行了比较。与GLMM模型相比,所提出的模型产生了更强的依赖性和更高的风险。因此,风险最优投资组合比基准模型得出的决策更保守。
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英文标题:
《A Coupled Markov Chain Approach to Credit Risk Modeling》
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作者:
David Wozabal and Ronald Hochreiter
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最新提交年份:
2014
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分类信息:
一级分类:Quantitative Finance 数量金融学
二级分类:Risk Management 风险管理
分类描述:Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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一级分类:Quantitative Finance 数量金融学
二级分类:Portfolio Management 项目组合管理
分类描述:Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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英文摘要:
We propose a Markov chain model for credit rating changes. We do not use any distributional assumptions on the asset values of the rated companies but directly model the rating transitions process. The parameters of the model are estimated by a maximum likelihood approach using historical rating transitions and heuristic global optimization techniques. We benchmark the model against a GLMM model in the context of bond portfolio risk management. The proposed model yields stronger dependencies and higher risks than the GLMM model. As a result, the risk optimal portfolios are more conservative than the decisions resulting from the benchmark model.
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PDF链接:
https://arxiv.org/pdf/0911.3802


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