List of Articles pair-copula

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  1 - Modeling rainfall event characteristics using D-vine copulas
  مریم شفائی احمد فاخری فرد یعقوب دین پژوه رسول میرعباسی
  Investigation of precipitation characteristics is necessitate in understanding and predicting phenomena of precipitation such as runoff and flood. Therefore in this study, dependence among the main characteristics of a rainfall event (i.e., rainfall depth R, maximum rai More

  Investigation of precipitation characteristics is necessitate in understanding and predicting phenomena of precipitation such as runoff and flood. Therefore in this study, dependence among the main characteristics of a rainfall event (i.e., rainfall depth R, maximum rainfall depth M, wet period L, and dry period D) were modeled using D-vine structure. Firstly, different multivariate probability distributions were built, making all the permutations of the conditioning variables and then Archimedean and Elliptic copulas were used for fitting each pair-copula. The best copula family was selected for fitting on each pair-copula according to different criteria. In the next stage, M-R-D-L structure, i.e., with D conditioned by L, R by D and L, and M by R, D, and L, was known as the most suitable structure considering to AIC and BIC criteria. Finally, rainfall event characteristics were simulated using the selected structure. In order to evaluation of simulation accuracy of proposed model, the main statistics of simulated variables were compared with those of observed variables. The results showed that the majority of simulated statistics have good accordance with observed statistics.  Manuscript profile
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  2 - Comparison of performance of C-Vine and D-Vine tree copulas in multivariate analysis of precipitation characteristics
  Maryam Shafaei Rasoul Mirabbasi
  10.30495/wsrcj.2021.18546
  In this study, the basic features of a tree vine copula such as the ability to decompose multivariate distributions into two-dimensional distributions, its flexibility in high-dimensional problems, and the use of conditional dependencies between variables have been cons More

  In this study, the basic features of a tree vine copula such as the ability to decompose multivariate distributions into two-dimensional distributions, its flexibility in high-dimensional problems, and the use of conditional dependencies between variables have been considered. The purpose is to use C-Vine and D-Vine structures to determine the four-dimensional probabilistic distribution function of important characteristics of precipitation events of Cremona rain station located in Italy including maximum precipitation intensity total precipitation depth, wet period duration and dry period. So that, a combination of the most suitable Archimedean and elliptical copulas families was identified to fit the pair-copulas of each of the C-Vine and D-Vine structures. The optimal combined distribution functions of C-Vine and D-Vine structures were also calculated using chain density functions and compared with the four-dimensional experimental copula of important precipitation characteristics. Finally, the accuracy of C-Vine and D-Vine tree structures in determining the combined distribution functions of important precipitation characteristics was compared. The results showed that the RDLM C-Vine structure has a minimum value of evaluation criteria RMSE = 0.029 and MAE = 0.022, as well as a maximum of P-value = 0.35 and R2 = 0.998 among all C-Vine and D-Vine structures. As a result, it has the highest accuracy for frequency analyzing the of precipitation characteristics of Cremona station in Italy. Manuscript profile
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  3 - Portfolio VaR Modelling using EVT-Pair-Copulas Approach
  Ali Souri Saeed Falahpor Ali Foroush Bastani Ehsan Ahmadi
  The purpose of this research is to model Value-at-Risk (VaR) of portfolio with EVT-Pair-Copulas approach. In the financial literature, a significant amount of empirical studies have been done on the characteristics of financial assets returns and researchers have found More

  The purpose of this research is to model Value-at-Risk (VaR) of portfolio with EVT-Pair-Copulas approach. In the financial literature, a significant amount of empirical studies have been done on the characteristics of financial assets returns and researchers have found a set of stylized facts about this subject. In this regard leptokurtic, left-skewed, weak autocorrelation, volatility clustering, and heteroscedasticity can be mentioned. Any estimation of risk without considering these characteristics or using unrealistic assumptions about financial assets returns increases the probability of failure in the risk management process. For this purpose, at first, the marginal distributions of returns are obtained using extreme value theory (EVT). Concerning characteristics of financial assets returns and also the primary filter to apply EVT, we use heteroscedasticity models for the marginal distributions of assets. Then the structure of the dependence between different stocks is estimated by using C-Vine, D-Vine, and R-Vine pair copula models. Afterward, the VaR of portfolio is estimated using the Monte Carlo simulation method. The final results show that the model with GARCH marginal distribution and R-Vine pair copula has been able to achieve the best performance among rival models at 95% confidence level. Manuscript profile