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

2019 ◽  
Vol 14 (2) ◽  
pp. 133-144 ◽  
Author(s):  
Don Cyr ◽  
Lester Kwong ◽  
Ling Sun
Keyword(s):  
Function Analysis ◽  
Rank Correlation ◽  
Tail Dependence ◽  
Copula Function ◽  
Nonlinear Relationship ◽  
Upper Tail Dependence ◽  
Linear And Nonlinear ◽  
Jel Classifications

AbstractThe 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)

An Examination of Tail Dependence in Bordeaux Futures Prices and Parker Ratings

2017 ◽  
Vol 12 (3) ◽  
pp. 252-266 ◽  
Author(s):  
Don Cyr ◽  
Lester Kwong ◽  
Ling Sun
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Tail Dependence ◽  
Copula Function ◽  
Bivariate Distribution ◽  
Nonlinear Relationship ◽  
Futures Prices ◽  
The Relationship ◽  
Jel Classifications

AbstractThis paper explores the nonlinearities of the bivariate distribution of Bordeaux en primeur, or wine futures, prices and Parker “barrel ratings” for the period of 2004 through 2010. In particular, copula-function methodology is introduced and employed to examine the nature of the bivariate distribution. Our results show a significant nonlinear relationship between Parker ratings and wine prices, characterized by significant positive tail dependence and higher correlation between high ratings and high prices. Marginal distributions for Parker ratings and wine prices are then identified and Monte Carlo simulation is employed to operationalize the relationship for risk-management purposes. (JEL Classifications: C19, G13, L66)

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.

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

2021 ◽  
Author(s):  
Qiang Liu ◽  
Aiping Tang ◽  
Zhongyue Wang ◽  
Buyue Zhao
Keyword(s):  
Joint Distribution ◽  
Tail Dependence ◽  
Copula Function ◽  
Copula Theory ◽  
Dynamic Correlation ◽  
Upper Tail Dependence ◽  
Case Application ◽  
Inducing Factors ◽  
And Control ◽  
Precipitation And Temperature

Abstract 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.

scholarly journals On a Multiplicative Multivariate Gamma Distribution with Applications in Insurance

Risks ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 79 ◽  
Author(s):  
Vadim Semenikhine ◽  
Edward Furman ◽  
Jianxi Su
Keyword(s):  
Risk Model ◽  
Tail Dependence ◽  
Random Variable ◽  
Copula Function ◽  
Background Risk ◽  
Aggregate Risk ◽  
Multivariate Gamma Distribution ◽  
Multivariate Probability Distribution ◽  
Dependent Risks ◽  
Main Message

One way to formulate a multivariate probability distribution with dependent univariate margins distributed gamma is by using the closure under convolutions property. This direction yields an additive background risk model, and it has been very well-studied. An alternative way to accomplish the same task is via an application of the Bernstein–Widder theorem with respect to a shifted inverse Beta probability density function. This way, which leads to an arguably equally popular multiplicative background risk model (MBRM), has been by far less investigated. In this paper, we reintroduce the multiplicative multivariate gamma (MMG) distribution in the most general form, and we explore its various properties thoroughly. Specifically, we study the links to the MBRM, employ the machinery of divided differences to derive the distribution of the aggregate risk random variable explicitly, look into the corresponding copula function and the measures of nonlinear correlation associated with it, and, last but not least, determine the measures of maximal tail dependence. Our main message is that the MMG distribution is (1) very intuitive and easy to communicate, (2) remarkably tractable, and (3) possesses rich dependence and tail dependence characteristics. Hence, the MMG distribution should be given serious considerations when modelling dependent risks.

scholarly journals Dose Correlation of Danggui and Chuanxiong Drug Pairs in the Chinese Medicine Prescription Based on the Copula Function

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xuan Zhao ◽  
Wei Lin ◽  
Jiawei Li ◽  
Yunhui Chen ◽  
Anamica Patel ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Correlation Coefficient ◽  
Rank Correlation ◽  
Correlation Coefficients ◽  
Copula Function ◽  
Prescription Database ◽  
Irregular Menstruation ◽  
Spearman’S Rank Correlation ◽  
Spearman’S Rank Correlation Coefficient

Dosage is essential for studying the compatibility and effectiveness of traditional Chinese medicine. Danggui and Chuanxiong are widely used in traditional Chinese medicine for ailments and treatment of various disorders. 628 traditional Chinese medicine prescriptions containing Danggui and Chuanxiong were extracted from the self-built prescription database and screened for the three groups of prescriptions, i.e., irregular menstruation, sores, and stroke. We processed and tested the dosage of Danggui and Chuanxiong and selected the optimal copula function, Gumbel copula function, from the Archimedes function family and elliptical copula function family to establish the data model. To establish the presence of a correlation between the dose of Danggui and Chuanxiong, a graph of the joint distribution function of rank correlation coefficients, Kendall’s rank correlation coefficient and Spearman’s rank correlation coefficient, was used. Our results suggest that the model using the Gumbel copula function better reflects the correlation between the dose of Danggui and Chuanxiong. For irregular menstruation, sores, and strokes, Kendall’s rank correlation coefficients were 0.6724, 0.5930, and 0.7757, respectively, and Spearman’s correlation coefficients were 0.8536, 0.7812, and 0.9285, respectively. In all three prescription groups, the dose of Danggui and Chuanxiong was positively correlated, implying that, as the dosage of one drug increases, the dosage of the other increases as well. From the perspective of data mining and mathematical statistics, the use of the copula function model to evaluate the correlation between the prescribed dosage of the two drugs was innovative and provided a new model for the scientific interpretation of the compatibility of traditional drugs. This might also serve to guide the clinical use of traditional Chinese medicine.

Does Globalization Influence Inbound Tourism?

2020 ◽  
pp. 004728752091951 ◽  
Author(s):  
Yi-Bin Chiu ◽  
Wenwen Zhang ◽  
Kaixin Ding
Keyword(s):  
Tourism Development ◽  
International Tourism ◽  
Nonlinear Relationship ◽  
Inbound Tourism ◽  
Tourism Service ◽  
Different Levels ◽  
Jel Classifications

This study explores the nonlinear impact of globalization on inbound tourism over the period 1995–2014 for 53 countries. The results reveal a nonlinear relationship between globalization and inbound tourism, suggesting that different levels of globalization for countries have varied impacts on inbound tourism development. More globalized countries are able to draw more inbound tourists, but this does not enhance their international tourism receipts (percentage of GDP) and net tourism service exports under a higher level of globalization, indicating that globalization does not necessarily benefit inbound tourism development. JEL classifications C23, C26, F60, L83, Z32

Empirical Research on the Non-Linear Inherent Relationship between China’s Social Security Expenditure and Private Consumption

2011 ◽  
Vol 403-408 ◽  
pp. 335-342
Author(s):  
Jian Guo Zhao ◽  
Jia Li
Keyword(s):  
Social Security ◽  
Empirical Research ◽  
Theoretical Basis ◽  
Private Consumption ◽  
Negative Relationship ◽  
Crowding Out ◽  
Nonlinear Relationship ◽  
Third Order ◽  
Three Stages ◽  
Linear And Nonlinear

For the existing lack of empirical research of relationship between social security expenditure and private consumption, this paper verified the existence of nonlinear relationship between both in China from 1952 to 2009 by applying nonlinear STR model. The conclusions demonstrate that between both is obviously negative relationship, although it is similar to most of the existing conclusions, but different in manifestation, i.e. the negative relationship presents remarkable stage characteristics and is frequently converted between linear and nonlinear. This negative relationship can be specifically divided into three stages: 1. negative nonlinear relationship from the year 1958 to 1963; 2. insignificant relationship from the year 1964 to 1978; 3. the reconverted negative nonlinear relationship from the year 1979 to 2009.The maximum elasticity of social security expenditure in the second and third order lags on consumption level amount to -0.0898 and -0.1024 respectively, and the crowding out effect is higher. These conclusions provide a realistic theoretical basis for China to develop and implement policies of social security expenditure.

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 Copula multivariate GARCH model with constrained Hamiltonian Monte Carlo

2019 ◽  
Vol 7 (1) ◽  
pp. 133-149
Author(s):  
Martin Burda ◽  
Louis Bélisle
Keyword(s):  
Monte Carlo ◽  
Garch Model ◽  
Tail Dependence ◽  
Copula Function ◽  
Multivariate Garch ◽  
Hamiltonian Monte Carlo ◽  
Dynamic Copula ◽  
Multivariate Garch Model ◽  
Financial Asset Returns ◽  
Parameter Constraints

AbstractThe Copula Multivariate GARCH (CMGARCH) model is based on a dynamic copula function with time-varying parameters. It is particularly suited for modelling dynamic dependence of non-elliptically distributed financial returns series. The model allows for capturing more flexible dependence patterns than a multivariate GARCH model and also generalizes static copula dependence models. Nonetheless, the model is subject to a number of parameter constraints that ensure positivity of variances and covariance stationarity of the modeled stochastic processes. As such, the resulting distribution of parameters of interest is highly irregular, characterized by skewness, asymmetry, and truncation, hindering the applicability and accuracy of asymptotic inference. In this paper, we propose Bayesian analysis of the CMGARCH model based on Constrained Hamiltonian Monte Carlo (CHMC), which has been shown in other contexts to yield efficient inference on complicated constrained dependence structures. In the CMGARCH context, we contrast CHMC with traditional random-walk sampling used in the previous literature and highlight the benefits of CHMC for applied researchers. We estimate the posterior mean, median and Bayesian confidence intervals for the coefficients of tail dependence. The analysis is performed in an application to a recent portfolio of S&P500 financial asset returns.

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