Some properties of the Kendall distribution in bivariate Archimedean copula models under censoring

2008 ◽  
Vol 78 (16) ◽  
pp. 2578-2583 ◽  
Author(s):  
Antai Wang ◽  
David Oakes
Keyword(s):  
Archimedean Copula ◽  
Copula Models ◽  
Kendall Distribution

scholarly journals Tail Approximations for Sums of Dependent Regularly Varying Random Variables Under Archimedean Copula Models

2018 ◽  
Vol 21 (2) ◽  
pp. 461-490 ◽  
Author(s):  
Hélène Cossette ◽  
Etienne Marceau ◽  
Quang Huy Nguyen ◽  
Christian Y. Robert
Keyword(s):  
Random Variables ◽  
Archimedean Copula ◽  
Copula Models ◽  
Regularly Varying

scholarly journals Archimedean Copula Estimation Parameter with Kendall Distribution Function

2017 ◽  
Vol 38 (4) ◽  
pp. 619-625
Author(s):  
Ayşe METIN KARAKAS ◽  
Murat KARAKAS ◽  
Mine DOGAN
Keyword(s):  
Distribution Function ◽  
Archimedean Copula ◽  
Copula Estimation ◽  
Kendall Distribution

Comments on: Inference in multivariate Archimedean copula models

Test ◽  
2011 ◽  
Vol 20 (2) ◽  
pp. 263-270 ◽  
Author(s):  
Paul Embrechts ◽  
Marius Hofert
Keyword(s):  
Archimedean Copula ◽  
Copula Models

scholarly journals Analisis Dependensi Faktor Makroekonomi terhadap Tingkat Harga Emas Dunia dengan Pendekatan Copula

2019 ◽  
Vol 1 (2) ◽  
pp. 82
Author(s):  
Sri Wati Agustini ◽  
Mustika Hadijati ◽  
Nurul Fitriyani
Keyword(s):  
Stock Price ◽  
Price Index ◽  
Relevant Information ◽  
Archimedean Copula ◽  
Copula Models ◽  
Macroeconomic Variables ◽  
Clayton Copula ◽  
Dependency Relationship ◽  
Copula Approach ◽  
The Relationship

Gold is a precious metal that used many times as an alternative investment. Before investing, every investor requires relevant information to make profitable investment decisions. Relevant information can be obtained by looking at the dependency relationship between variables. In identifying the relationship between variables, a Copula approach could be used, since it is not tight against the assumption of normality, which is common in macroeconomic variables. Copula used were Archimedean Copula family, such as Clayton, Frank, and Gumbel.  The results of this study indicated that the Archimedean Copula of the Frank family is the best Copula models to explain the structure of dependencies between gold and each composite stock price index and exchange rate, with each parameter obtained were 2.286 and -2.2390, respectively, while Clayton Copula family was the best Copula models to explain the structure of dependencies between gold and oil, with parameter obtained was 3.4090.

SPATIAL DEPENDENCE AND AGGREGATION IN WEATHER RISK HEDGING: A LÉVY SUBORDINATED HIERARCHICAL ARCHIMEDEAN COPULAS (LSHAC) APPROACH

2018 ◽  
Vol 48 (02) ◽  
pp. 779-815 ◽  
Author(s):  
Wenjun Zhu ◽  
Ken Seng Tan ◽  
Lysa Porth ◽  
Chou-Wen Wang
Keyword(s):  
Spatial Dependence ◽  
Crop Production ◽  
Archimedean Copula ◽  
Dependence Structure ◽  
Copula Model ◽  
Copula Models ◽  
Basis Risk ◽  
Risk Hedging ◽  
Adverse Weather ◽  
Weather Risk

AbstractAdverse weather-related risk is a main source of crop production loss and a big concern for agricultural insurers and reinsurers. In response, weather risk hedging may be valuable, however, due to basis risk it has been largely unsuccessful to date. This research proposes the Lévy subordinated hierarchical Archimedean copula model in modelling the spatial dependence of weather risk to reduce basis risk. The analysis shows that the Lévy subordinated hierarchical Archimedean copula model can improve the hedging performance through more accurate modelling of the dependence structure of weather risks and is more efficient in hedging extreme downside weather risk, compared to the benchmark copula models. Further, the results reveal that more effective hedging may be achieved as the spatial aggregation level increases. This research demonstrates that hedging weather risk is an important risk management method, and the approach outlined in this paper may be useful to insurers and reinsurers in the case of agriculture, as well as for other related risks in the property and casualty sector.

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