Hierarchical Archimedean copula models for the analysis of binary familial data

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.

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

Astin Bulletin ◽

10.1017/asb.2018.6 ◽

2018 ◽

Vol 48 (02) ◽

pp. 779-815 ◽

Cited By ~ 1

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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Comments on: Inference in multivariate Archimedean copula models

Test ◽

10.1007/s11749-011-0255-1 ◽

2011 ◽

Vol 20 (2) ◽

pp. 281-283 ◽

Cited By ~ 2

Author(s):

Johan Segers

Keyword(s):

Archimedean Copula ◽

Copula Models

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Conditional tail independence in Archimedean copula models

Journal of Applied Probability ◽

10.1017/jpr.2019.48 ◽

2019 ◽

Vol 56 (3) ◽

pp. 858-869

Author(s):

Michael Falk ◽

Simone A. Padoan ◽

Florian Wisheckel

Keyword(s):

Distribution Function ◽

Random Vector ◽

Mild Condition ◽

Conditional Distribution ◽

Archimedean Copula ◽

Tail Behavior ◽

Copula Models ◽

Generator Function

AbstractConsider a random vector $\textbf{U}$ whose distribution function coincides in its upper tail with that of an Archimedean copula. We report the fact that the conditional distribution of $\textbf{U}$ , conditional on one of its components, has under a mild condition on the generator function independent upper tails, no matter what the unconditional tail behavior is. This finding is extended to Archimax copulas.

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Comments on: Inference in multivariate Archimedean copula models

Test ◽

10.1007/s11749-011-0256-0 ◽

2011 ◽

Vol 20 (2) ◽

pp. 284-286

Author(s):

Philippe Lambert

Keyword(s):

Archimedean Copula ◽

Copula Models

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