Everything You Always Wanted to Know about Copula Modeling but Were Afraid to Ask

Genest, Christian; Favre, Anne‐Catherine

doi:10.1061/(asce)1084-0699(2007)12:4(347)

Cited by 1,289 publications

(963 citation statements)

References 65 publications

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“…On the other hand, it is also known (cf., e.g., Schweizer and Wolf (1981)) that the copula functions capture the properties of dependence between random variables. A known statistical test of independence (Genest and Favre, 2007), for l¼2, is based on the empirical version of Kendall's t measure, that can be defined by…”

Section: Methodsologymentioning

confidence: 99%

“…In this paper, a study of the dependence between extreme rainfall values from 12 rain gauge stations distributed over Madeira Island was carried out based on the Kendall's t association measure (Genest and Favre, 2007). An adjustment was also made to a family of extreme value copulas and return period estimates for a given extreme event were obtained.…”

Section: Introductionmentioning

confidence: 99%

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A dependence modelling study of extreme rainfall in Madeira Island

Gouveia-Reis

Lopes

Mendonça

2016

Physics and Chemistry of the Earth, Parts A/B/C

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a b s t r a c tThe dependence between variables plays a central role in multivariate extremes. In this paper, spatial dependence of Madeira Island's rainfall data is addressed within an extreme value copula approach through an analysis of maximum annual data. The impact of altitude, slope orientation, distance between rain gauge stations and distance from the stations to the sea are investigated for two different periods of time. The results obtained highlight the influence of the island's complex topography on the spatial distribution of extreme rainfall in Madeira Island.

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Section: Methodsologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A dependence modelling study of extreme rainfall in Madeira Island

Gouveia-Reis

Lopes

Mendonça

2016

Physics and Chemistry of the Earth, Parts A/B/C

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“…The overall dependence of the demand parameter D and the intensity measures IM is tested by using the definition of independence, i.e., The joint distribution of F (im, d) may be calculated by either (1) using copulas, which have been extensively used for this purpose in applications [10], or (2) empirically using direct observations. Details on the representation of distribution of random vectors by copulas and their uniqueness are stated by Sklar theorem [11].…”

Section: Overall Dependencementioning

confidence: 99%

Uncertainty in Seismic Intensity Measures Used for Fragility Analysis

Radu¹,

Grigoriu²

2017

Proceedings of the 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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Abstract. Seismic fragility is the main tool used in the performance-based earthquake engineering, and represents the probability of a dynamic system to enter a damage state for a given intensity measure (IM). The most commonly-used seismic IMs are the peak ground acceleration (P GA), which is the absolute maximum of the ground acceleration, and the spectral acceleration (SA), which characterizes well the response of simple linear systems, but shows large uncertainties in the characterization of the seismic response of complex non-linear systems. The main assumption that underlies the basis of the selection of P GA or SA as IMs is that they correlate satisfactorily with the demand parameters (D) of realistic structures, i.e., nonlinear, complex dynamic systems. Statistics and tools of random vibrations are employed to quantify the dependence relations between seismic IMs and structural demand parameters, for non-linear systems subjected to seismic ground-motion records.

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“…Financial applications of copula methods are covered in papers by Bouyé et al (2000), Chorós et al (2009) or the publication of Cherubini et al (2004). Interesting application of copula methods in hydrology is mentioned by Genest and Favre (2007).…”

Section:  Perfect Positive Dependence Does Not Imply a Correlation Omentioning

confidence: 99%

Comparison of different copula assumptions and their application in portfolio construction

Štulajter¹

2009

ER-CEREI

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The paper deals with modeling of mutual dependencies among financial assets. Its aim is to investigate the impact of different copula assumptions on optimal portfolios, when CVaR optimization is used. Strategic asset allocation perspective is supposed. It is demonstrated that copula functions enable us to separate the modeling of dependency features of financial assets from the modeling of marginal distribution characteristics, in the context of practical portfolio construction tasks. The difference between portfolios constructed using normal copula and student t copula is shown when mutual or pension fund exposed to long-only constrain is assumed. The fund is considered to invest solely into equity and fixed-income instruments. As expected, the exclusive use of linear correlation coefficients leads to underestimation of total portfolio risk. The superiority of student t copula portfolios intensifies as the confidence level of CVaR rises and/or as the CVaR target increases.

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Everything You Always Wanted to Know about Copula Modeling but Were Afraid to Ask

Cited by 1,289 publications

References 65 publications

A dependence modelling study of extreme rainfall in Madeira Island

A dependence modelling study of extreme rainfall in Madeira Island

Uncertainty in Seismic Intensity Measures Used for Fragility Analysis

Comparison of different copula assumptions and their application in portfolio construction

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