Exploring the Highway Icing Risk: Considering the Dynamic Dependence of Icing-Inducing Factors

In terms of the dynamic dependence between icing-inducing factors, this study is to explore the risk distribution of highways when icing events occur in the study area. A joint distribution considering the dynamic correlation of inducing factors was first constructed employing the Copula theory, which then yielded the possibility of icing events. Meanwhile, hazard zones and intensities of icing were proposed under different exceeding probabilities. After finishing the vulnerability analysis of highways, the risk matrix was used to conduct the icing risk for the highway, which was then applied to the construction of the risk zoning map. The results showed that there was an upper-tail dependence between extreme precipitation and temperature in the study area in winter, which could be well captured by the Gumbel Copula function. Indeed, the constructed joint distribution can express the possibility of icing under different intensities of precipitation and temperature. Besides, the highway with the tallest vulnerability in the study area was the Hegang-Yichun line. The case application showed that during March 2020, the traffic lines with a high icing risk were distributed around Fujin, Jiamusi, Hegang, and Qitaihe cities, and the Hegang section of the Hegang-Yichun line was at the highest icing risk. The low-risk lines were concentrated in the western part of the study area. This study is of great significance for the prevention and control of ice-snow disasters on the highway in cold regions.

New families of bivariate copulas via unit weibull distortion

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.

Dependence Structure between Conventional and Islamic Indexes: A Copula Approach

This article examines the conditional dependence structure between Islamic stock indexes and conventional counterparts. Our empirical analysis relies on Islamic and conventional indexes of dependence distribution using copula methods over the period 1999–2014. The results from the copula models denote that the dependence is not formally symmetric in that the lower tail dependence is significantly larger than the upper tail dependence.

Dependence Modelling in Insurance via Copulas with Skewed Generalised Hyperbolic Marginals

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.

Who Will Replace Parker? A Copula Function Analysis of Bordeaux En Primeur Wine Raters

The influence of the wine rater Robert Parker Jr. on Bordeaux wine extended over a 40-year period, with a particular impact on en primeur wine prices. Consequently, his announcement in 2015 that he would no longer rate en primeur wines creates some uncertainty for many chateaux that have purposely designed their production with his palate and preferences in mind. Although the wine rater Neal Martin was named by Parker to be his successor in terms of en primeur wine ratings, there are several other wine critics who have consistently rated en primeur wines over several years. Consequently, we employ copula function analysis to explore which wine critics’ ratings exhibit the closest linear and nonlinear relationship, for right bank en primeur wines, with those of Parker. The study employs data over the period of 2005 through 2012, during which time several wine critics, including Neal Martin for the period of 2010–2012, rated en primeur wines alongside Parker. Our results indicate that of the wine critics that continue to rate en primeur wines, the ratings of James Suckling exhibit the highest rank correlation and also bivariate upper tail dependence, identified through copula function analysis, with those of Parker. (JEL Classifications: C19, G13, L66)

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

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.

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

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.

A note on upper-patched generators for Archimedean copulas

The class of multivariate Archimedean copulas is defined by using a real-valued function called the generator of the copula. This generator satisfies some properties, including d-monotonicity. We propose here a new basic transformation of this generator, preserving these properties, thus ensuring the validity of the transformed generator and inducing a proper valid copula. This transformation acts only on a specific portion of the generator, it allows both the non-reduction of the likelihood on a given dataset, and the choice of the upper tail dependence coefficient of the transformed copula. Numerical illustrations show the utility of this construction, which can improve the fit of a given copula both on its central part and its tail.