Introduction to Bayesian Estimation and Copula Models of Dependence

Shemyakin, Arkady; Князев, А Г

doi:10.1002/9781118959046

Cited by 36 publications

(28 citation statements)

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“…then, by computing the above integrals, the outage probability is given by (7). The details of the proof can be found in Appendix 7.1.…”

Section: Outage Probabilitymentioning

confidence: 99%

“…One of the most efficient and flexible methods to express the correlation of random variables in statistics is exploiting Copula functions. Copula functions easily provide joint distributions from the various marginal distributions of random variables and have been widely used in statistics, economics, survival analysis, image processing, machine learning, and a wide range of engineering applications [6,7]. In the context of fading point to point multiantenna channels, several works have been done studying various aspects [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Copula function‐based analysis of outage probability and coverage region for wireless multiple access communications with correlated fading channels

Ghadi

Hodtani

2020

IET Communications

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“…then, by computing the above integrals, the outage probability is given by (7). The details of the proof can be found in Appendix 7.1.…”

Section: Outage Probabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Copula function‐based analysis of outage probability and coverage region for wireless multiple access communications with correlated fading channels

Ghadi

Hodtani

2020

IET Communications

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“…Clayton's, Frank's and Gumbel-Hougaard's copula families provide a valid alternative to the use of elliptical copulas for modeling joint distribution tails (Genest, Rivest, 1993). However, in dimensions higher than = 2 d they require additional definition of the hierarchical structure (vines and nested copulas being two main options, see (Shemyakin, Kniazev, 2017)). We will consider Clayton's nested copula family as in (Hofert, Mächler, 2011;Kangina et al, 2016;Okhrin, Ristig, 2014) along with Student's t-copula as possible choices.…”

Section: финансы Financementioning

confidence: 99%

Copula models of economic capital for life insurance companies

Benson¹,

Burroughs²,

Ladyzhets³

et al. 2020

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The objective of the paper is to introduce a copula methodology of economic capital modeling, which is practically applicable for life insurance companies. Copula methods make it possible to address multiple dependent risk factors including both investment and underwriting risks in the framework of a portfolio approach. We identify a relevant set of asset and liability variables, and suggest a copula model for the joint distribution of these variables. Estimates of economic capital are constructed via VaR and TVaR calculations based on the tails of this joint distribution. This approach requires ARIMA and copula model selection followed by Monte Carlo simulation of the time series of the joint asset/liability portfolio. Models are implemented in open source software (R and MS Excel) and tested using historical and simulated asset/liability data. The results are applied to the construction of a software tool which can be utilized for customization and direct user application. The novelty of the approach consists in estimating interdependent underwriting and investment risks in one multivariate model taking into account short-term (daily or monthly) fluctuations of the market. In particular, we address the challenges that life insurance companies face in the low interest environment, using the market data for the 15-year period 2003-2018.

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“…Performance analysis of wireless channels with correlated fading coefficients requires the multivariate distributions representing the unknown joint statistics of different fading coefficients. Therefore, considering the correlation between channel coefficients needs an effective mathematical tool, called Copula which easily provides joint distributions from the various marginal distributions of random variables, and has been widely used in statistics, economics, survival analysis, image processing, machine learning, and engineering applications [4, 5 ].…”

Section: Introductionmentioning

confidence: 99%