The new family of Fisher copulas to model upper tail dependence and radial asymmetry: Properties and application to high-dimensional rainfall data

2018 ◽  
Vol 29 (3) ◽  
pp. e2494 ◽  
Author(s):  
Anne-Catherine Favre ◽  
Jean-François Quessy ◽  
Marie-Hélène Toupin
Keyword(s):  
Tail Dependence ◽  
Rainfall Data ◽  
High Dimensional ◽  
New Family ◽  
Upper Tail Dependence ◽  
Radial Asymmetry

scholarly journals New families of bivariate copulas via unit weibull distortion

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Fadal A.A. Aldhufairi ◽  
Jungsywan H. Sepanski
Keyword(s):  
Tail Dependence ◽  
Copula Function ◽  
Kendall’S Tau ◽  
Kendall's Tau ◽  
Density Distributions ◽  
New Family ◽  
Upper Tail Dependence ◽  
Bivariate Copula

Abstract This paper introduces a new family of bivariate copulas constructed using a unit Weibull distortion. Existing copulas play the role of the base or initial copulas that are transformed or distorted into a new family of copulas with additional parameters, allowing more flexibility and better fit to data. We present a general form for the new bivariate copula function and its conditional and density distributions. The tail behaviors are investigated and indicate the unit Weibull distortion may result in new copulas with upper tail dependence when the base copula has no upper tail dependence. The concordance ordering and Kendall’s tau are derived for the cases when the base copulas are Archimedean, such as the Clayton and Frank copulas. The Loss-ALEA data are analyzed to evaluate the performance of the proposed new families of copulas.

Diversifying systemic risk in agriculture

2016 ◽  
Vol 76 (4) ◽  
pp. 512-531 ◽  
Author(s):  
Xiaoguang Feng ◽  
Dermot Hayes
Keyword(s):  
Crop Yield ◽  
Systemic Risk ◽  
Crop Insurance ◽  
Tail Dependence ◽  
Model Potential ◽  
High Dimensional ◽  
Computationally Efficient ◽  
Content Type ◽  
Insurance Portfolio ◽  
Systemic Nature

Purpose Portfolio risk in crop insurance due to the systemic nature of crop yield losses has inhibited the development of private crop insurance markets. Government subsidy or reinsurance has therefore been used to support crop insurance programs. The purpose of this paper is to investigate the possibility of converting systemic crop yield risk into “poolable” risk. Specifically, this study examines whether it is possible to remove the co-movement as well as tail dependence of crop yield variables by enlarging the risk pool across different crops and countries. Design/methodology/approach Hierarchical Kendall copula (HKC) models are used to model potential non-linear correlations of the high-dimensional crop yield variables. A Bayesian estimation approach is applied to account for estimation risk in the copula parameters. A synthetic insurance portfolio is used to evaluate the systemic risk and diversification effect. Findings The results indicate that the systemic nature – both positive correlation and lower tail dependence – of crop yield risks can be eliminated by combining crop insurance policies across crops and countries. Originality/value The study applies the HKC in the context of agricultural risks. Compared to other advanced copulas, the HKC achieves both flexibility and parsimony. The flexibility of the HKC makes it appropriate to precisely represent various correlation structures of crop yield risks while the parsimony makes it computationally efficient in modeling high-dimensional correlation structure.

Inorganic–organic hybrid high-dimensional polyoxotantalates and their structural transformations triggered by water

2019 ◽  
Vol 55 (78) ◽  
pp. 11735-11738 ◽  
Author(s):  
Zhong Li ◽  
Jing Zhang ◽  
Li-Dan Lin ◽  
Jin-Hua Liu ◽  
Xin-Xiong Li ◽  
...  
Keyword(s):  
Structural Transformations ◽  
High Dimensional ◽  
Organic Hybrid ◽  
New Family

In this work, novel dimeric polyoxotantalate (POTa) clusters {Cu(en)(Ta6O19)}2/{Cu(enMe)(Ta6O19)}2 were introduced as SBUs to construct a new family of extended POTa materials, including the first two 3D POTa frameworks and two 2D POTa layers.

Conditional Similarity Reduction Method and Complex Wave Excitations for a High-Dimensional Nonlinear System

2015 ◽  
Vol 70 (9) ◽  
pp. 739-744
Author(s):  
Fu-Zhong Lin ◽  
Song-Hua Ma
Keyword(s):  
Nonlinear System ◽  
Water Wave ◽  
Reduction Method ◽  
Solitary Wave Solution ◽  
High Dimensional ◽  
Similarity Reduction ◽  
New Family ◽  
Wave Solutions ◽  
Complex Wave ◽  
Novel Complex

AbstractWith the help of the conditional similarity reduction method, a new family of complex wave solutions with q=lx + my + kt + Γ(x, y, t) for the (2+1)-dimensional modified dispersive water-wave (MDWW) system are obtained. Based on the derived solitary wave solution, some novel complex wave localised excitations are investigated.

scholarly journals Dependence Modelling in Insurance via Copulas with Skewed Generalised Hyperbolic Marginals

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Vitali Alexeev ◽  
Katja Ignatieva ◽  
Thusitha Liyanage
Keyword(s):  
Value At Risk ◽  
Tail Dependence ◽  
Dependence Structure ◽  
Portfolio Risk ◽  
Total Risk ◽  
Copula Models ◽  
Out Of Sample ◽  
Upper Tail Dependence ◽  
Dependence Modelling ◽  
Best Fit

Abstract This paper investigates dependence among insurance claims arising from different lines of business (LoBs). Using bivariate and multivariate portfolios of losses from different LoBs, we analyse the ability of various copulas in conjunction with skewed generalised hyperbolic (GH) marginals to capture the dependence structure between individual insurance risks forming an aggregate risk of the loss portfolio. The general form skewed GH distribution is shown to provide the best fit to univariate loss data. When modelling dependency between LoBs using one-parameter and mixture copula models, we favour models that are capable of generating upper tail dependence, that is, when several LoBs have a strong tendency to exhibit extreme losses simultaneously. We compare the selected models in their ability to quantify risks of multivariate portfolios. By performing an extensive investigation of the in- and out-of-sample Value-at-Risk (VaR) forecasts by analysing VaR exceptions (i.e. observations of realised portfolio value that are greater than the estimated VaR), we demonstrate that the selected models allow to reliably quantify portfolio risk. Our results provide valuable insights with regards to the nature of dependence and fulfils one of the primary objectives of the general insurance providers aiming at assessing total risk of an aggregate portfolio of losses when LoBs are correlated.

scholarly journals New copulas based on general partitions-of-unity (part III) — the continuous case

2019 ◽  
Vol 7 (1) ◽  
pp. 181-201 ◽  
Author(s):  
Dietmar Pfeifer ◽  
Andreas Mändle ◽  
Olena Ragulina ◽  
Côme Girschig
Keyword(s):  
Risk Management ◽  
Natural Extension ◽  
Simple Algorithm ◽  
Tail Dependence ◽  
Partition Of Unity ◽  
Solvency Ii ◽  
Continuous Case ◽  
High Dimensional ◽  
Data Sets ◽  
Empirical Copula

AbstractIn this paper we discuss a natural extension of infinite discrete partition-of-unity copulas which were recently introduced in the literature to continuous partition of copulas with possible applications in risk management and other fields. We present a general simple algorithm to generate such copulas on the basis of the empirical copula from high-dimensional data sets. In particular, our constructions also allow for an implementation of positive tail dependence which sometimes is a desirable property of copula modelling, in particular for internal models under Solvency II.

Impact of Chinese Yuan Devaluation on the Dependence Structure: The Archimedean Copula Approach

2019 ◽  
Vol 22 (01) ◽  
pp. 1950005
Author(s):  
Wing-Choong Lai ◽  
Kim-Leng Goh
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Extreme Event ◽  
Linear Regression Analysis ◽  
Tail Dependence ◽  
Dependence Structure ◽  
Before And After ◽  
Copula Approach ◽  
Upper Tail Dependence ◽  
Chinese Yuan

This paper investigates the linkages of Chinese yuan to other currencies before and after the yuan devaluation on 11 August 2015. Linear regression analysis shows that only a few of the 14 currencies considered are significantly affected by the devaluation. However, the devaluation of Chinese yuan has been associated with larger fluctuations in these currencies and the occurrence of extreme positive and negative returns. The regression method may under estimate the tail dependence between currencies, as financial data are usually non-normally distributed, especially when extreme event occurs. We apply the Archimedean copulas to capture the presence of lower and upper tail dependence between the exchange rate returns of Chinese yuan and the selected currencies, and found dependencies not revealed by the linear regression analysis. The extreme returns after the Chinese yuan devaluation have resulted in higher dependence with the selected currencies. While the dependence structure was dominated by risks due to unusual currency gains before the devaluation, the market responses to large losses and gains have become more symmetric after the devaluation.

scholarly journals Spatial Tail Dependence and Survival Stability in a Class of Archimedean Copulas

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Diakarya Barro ◽  
Moumouni Diallo ◽  
Remi Guillaume Bagré
Keyword(s):  
Dimensional Analysis ◽  
Tail Dependence ◽  
High Dimensional ◽  
Archimedean Copulas ◽  
Lower Tail

This paper investigates properties of extensions of tail dependence of Archimax copulas to high dimensional analysis in a spatialized framework. Specifically, we propose a characterization of bivariate margins of spatial Archimax processes while spatial multivariate upper and lower tail dependence coefficients are modeled, respectively, for Archimedean copulas and Archimax ones. A property of stability is given using convex transformations of survival copulas in a spatialized Archimedean family.

scholarly journals Development of a Maximum Entropy-Archimedean Copula-Based Bayesian Network Method for Streamflow Frequency Analysis—A Case Study of the Kaidu River Basin, China

Water ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 42 ◽  
Author(s):  
Xiangming Kong ◽  
Xueting Zeng ◽  
Cong Chen ◽  
Yurui Fan ◽  
Guohe Huang ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Maximum Entropy ◽  
River Basin ◽  
Frequency Analysis ◽  
Tail Dependence ◽  
Archimedean Copula ◽  
Hydrological Extremes ◽  
Monthly Streamflow ◽  
Upper Tail Dependence ◽  
Kaidu River

Frequency analysis of streamflow is critical for water-resources system planning, water conservancy projects and the mitigation of hydrological extremes events. In this study, a maximum entropy-Archimedean copula-based Bayesian network (MECBN) method has been proposed for frequency analysis of monthly streamflow in the Kaidu River Basin, which integrates the maximum entropy-Archimedean copula (MEAC) and Bayesian network methods into a general framework. MECBN is effective for representing the uncertainties that exist in model representation, preserving the distributional characteristics of streamflow records and addressing the correlation structure between streamflow pairs. Application to the Kaidu River Basin shows a good performance of MECBN in describing the historical data of this basin in China. The results indicate that the interactions between two adjacent monthly streamflow pairs are non-linear. There is upper tail dependence between monthly streamflow pairs. The dependence coefficients including Spearman’s rho, Kendall’s tau, and the upper tail dependence coefficient are in inverse proportion of monthly streamflow values in the Kaidu River Basin, due to the fact that other factors (i.e., rainfall, snow melting, evapotranspiration rate and requirement of water use) provide more contributions to the streamflow in the flooding season. These findings can be used for providing vital information in the prevention and control of hydrological extremes and to further water resources planning in Kaidu River Basin.

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