南开大学金融学院朱文君老师作了一场题为“Spatial Dependence & Aggregation in Weather Risk Hedging: A Lévy subordinated hierarchical Archimedean copula (LSHAC) approach(空间相关性和聚集性的天气风险对冲：征收次级分层阿基米德Copula函数(LSAC)的方法)”的讲座。金融学院开设了国际金融系、数量金融系、应用金融系、金融经济系、风险管理与保险学系、精算系、金融研究所、计量经济学研究所、不动产金融研究所、风险管理与保险研究所。此外，金融学院还设立了“金融计量实验室”和若干智库型研究中心。在职研究生讲座的主要内容是：

　　恶劣天气相关的风险是作物生产损失的主要来源，而除了农民，这种风险是一个主要问题和不确定性的保险公司和再保险公司谁充当天气风险承销商。到目前为止，天气套期保值已有限的成功，很大程度上是由于对基差风险的挑战。因此，本文发展，并通过调查的在一个国家的系统性风险，天气的空间依赖性和聚集水平不同的天气风险对冲策略的农业保险公司和再保险公司进行比较。本文提出了假设一般的双曲线(GH)家族的利润率捕捉数据的重尾特性灵活的时间序列模型，再加征次级分层阿基米德Copula函数(LSAC)模型来克服高的挑战在维天气风险的依赖性建模。采用小波分析来研究从时间和频率两者尺度数据内的详细特征。分析表明，在本文提出的LSHAC模式减少了392089万$极端天气的下行风险，相比独立模型的假设提供了一个额外的$ 321.61万减少风险。此外，结果表明，更有效的对冲，可以实现作为空间聚合水平增加。

　　原文：Adverse weather related risk is a main source of crop production loss, and in addition to farmers, this exposure is a major concern and uncertainty for insurers and reinsurers who act as weather risk underwriters. To date, weather hedging has had limited success, largely due to challenges regarding basis risk. Therefore, this paper develops and compares different weather risk hedging strategies for agricultural insurers and reinsurers, through investigating the spatial dependence and aggregation level of systemic weather risks across a country. This paper proposes a flexible time series model that assumes a general hyperbolic (GH) family for the margins to capture the heavy-tail property of the data, together with the Lévy subordinated hierarchical Archimedean copula (LSHAC) model to overcome the challenge of high-dimensionality in modeling the dependence of weather risk. Wavelet analysis is employed to study the detailed characteristics within the data from both time and frequency scales. The analysis shows that the LSHAC model proposed in this paper reduces extreme weather downside risk by $3920.89 million, providing an additional $321.61 million risk reduction compared to the independent model assumption. Further, the results reveal that more effective hedging may be achieved as the spatial aggregation level increases.