英文标题：
《An importance sampling approach for copula models in insurance》
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作者：
Philipp Arbenz, Mathieu Cambou and Marius Hofert
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最新提交年份：
2015
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英文摘要：
An importance sampling approach for sampling copula models is introduced. We propose two algorithms that improve Monte Carlo estimators when the functional of interest depends mainly on the behaviour of the underlying random vector when at least one of the components is large. Such problems often arise from dependence models in finance and insurance. The importance sampling framework we propose is general and can be easily implemented for all classes of copula models from which sampling is feasible. We show how the proposal distribution of the two algorithms can be optimized to reduce the sampling error. In a case study inspired by a typical multivariate insurance application, we obtain variance reduction factors between 10 and 30 in comparison to standard Monte Carlo estimators.
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中文摘要：
介绍了copula模型抽样的一种重要抽样方法。当感兴趣的函数主要取决于基本随机向量的行为时，当至少一个分量较大时，我们提出了两种改进蒙特卡罗估计的算法。这类问题通常源于金融和保险业中的依赖模型。我们提出的重要性抽样框架是通用的，可以很容易地应用于所有类别的copula模型，从中抽样是可行的。我们展示了如何优化这两种算法的建议分布，以减少采样误差。在一个典型的多变量保险应用启发下的案例研究中，与标准蒙特卡罗估计相比，我们得到了10到30之间的方差缩减因子。
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分类信息：

一级分类：Statistics 统计学
二级分类：Computation 计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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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：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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