南開大學金融學院朱文君老師作了一場題為“Spatial Dependence & Aggregation in Weather Risk Hedging: A Lévy subordinated hierarchical Archimedean copula (LSHAC) approach(空間相關(guān)性和聚集性的天氣風險對沖：征收次級分層阿基米德Copula函數(shù)(LSAC)的方法)”的講座。金融學院開設(shè)了國際金融系、數(shù)量金融系、應(yīng)用金融系、金融經(jīng)濟系、風險管理與保險學系、精算系、金融研究所、計量經(jīng)濟學研究所、不動產(chǎn)金融研究所、風險管理與保險研究所。此外，金融學院還設(shè)立了“金融計量實驗室”和若干智庫型研究中心。在職研究生講座的主要內(nèi)容是：

　　惡劣天氣相關(guān)的風險是作物生產(chǎn)損失的主要來源，而除了農(nóng)民，這種風險是一個主要問題和不確定性的保險公司和再保險公司誰充當天氣風險承銷商。到目前為止，天氣套期保值已有限的成功，很大程度上是由于對基差風險的挑戰(zhàn)。因此，本文發(fā)展，并通過調(diào)查的在一個國家的系統(tǒng)性風險，天氣的空間依賴性和聚集水平不同的天氣風險對沖策略的農(nóng)業(yè)保險公司和再保險公司進行比較。本文提出了假設(shè)一般的雙曲線(GH)家族的利潤率捕捉數(shù)據(jù)的重尾特性靈活的時間序列模型，再加征次級分層阿基米德Copula函數(shù)(LSAC)模型來克服高的挑戰(zhàn)在維天氣風險的依賴性建模。采用小波分析來研究從時間和頻率兩者尺度數(shù)據(jù)內(nèi)的詳細特征。分析表明，在本文提出的LSHAC模式減少了392089萬$極端天氣的下行風險，相比獨立模型的假設(shè)提供了一個額外的$ 321.61萬減少風險。此外，結(jié)果表明，更有效的對沖，可以實現(xiàn)作為空間聚合水平增加。

　　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.