scholarly journals Vine-Copula-Based Quantile Regression for Cascade Reservoirs Management

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 964
Author(s):  
Wafaa El Hannoun ◽  
Salah-Eddine El Adlouni ◽  
Abdelhak Zoglat
Keyword(s):  
Eastern Canada ◽  
Statistical Tool ◽  
Risk Modelling ◽  
Heavy Tailed ◽  
Composite Risk ◽  
Dependence Modelling ◽  
Vine Copula ◽  
Hydrologic Design ◽  
Vine Copulas ◽  
Main Attraction

This paper features an application of Regular Vine (R-vine) copulas, a recently developed statistical tool to assess composite risk. Copula-based dependence modelling is a popular tool in conditional risk assessment, but is usually applied to pairs of variables. By contrast, Vine copulas provide greater flexibility and permit the modelling of complex dependency patterns using a wide variety of bivariate copulas which may be arranged and analysed in a tree structure to explore multiple dependencies. This study emphasises the use of R-vine copulas in an analysis of the co-dependencies of five reservoirs in the cascade of the Saint-John River basin in Eastern Canada. The developed R-vine copulas lead to the joint and conditional return periods of maximum volumes, for hydrologic design and cascade reservoir management in the basin. The main attraction of this approach to risk modelling is the flexibility in the choice of distributions used to model heavy-tailed marginals and co-dependencies.

scholarly journals Streamflow estimation at partially gaged sites using multiple dependence conditions via vine copulas

2020 ◽  
Author(s):  
Kuk-Hyun Ahn
Keyword(s):  
Missing Values ◽  
The Other ◽  
Dependence Structure ◽  
Proposed Model ◽  
Pee Dee ◽  
Streamflow Estimation ◽  
Vine Copula ◽  
Bivariate Copula ◽  
Pee Dee River ◽  
Vine Copulas

Abstract. Reliable estimates of missing streamflow values are relevant for water resources planning and management. This study proposes a multiple dependence condition model via vine copulas for the purpose of estimating streamflow at partially gaged sites. The proposed model is attractive in modeling the high dimensional joint distribution by building a hierarchy of conditional bivariate copulas when provided a complex streamflow gage network. The usefulness of the proposed model is firstly highlighted using a synthetic streamflow scenario. In this analysis, the bivariate copula model and a variant of the vine copulas are also employed to show the ability of the multiple dependence structure adopted in the proposed model. Furthermore, the evaluations are extended to a case study of 54 gages located within the Yadkin-Pee Dee River Basin, the eastern U. S. Both results inform that the proposed model is better suited for infilling missing values. After that, the performance of the vine copula is compared with six other infilling approaches to confirm its applicability. Results demonstrate that the proposed model produces more reliable streamflow estimates than the other approaches. In particular, when applied to partially gaged sites with sufficient available data, the proposed model clearly outperforms the other models. Even though the model is illustrated by a specific case, it can be extended to other regions with diverse hydro-climatological variables for the objective of infilling.

scholarly journals Impact of Facebook and Newspaper Advertising on Sales: A Comparative Study of Online and Print Media

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yushan Lin ◽  
Zubair Ahmad ◽  
Wasswa Shafik ◽  
Saima K. Khosa ◽  
Zahra Almaspoor ◽  
...  
Keyword(s):  
Quantitative Research ◽  
Statistical Tests ◽  
Secondary Data ◽  
Research Approach ◽  
Statistical Tool ◽  
Family Approach ◽  
Advertising Budget ◽  
Proposed Model ◽  
Heavy Tailed ◽  
Linear Regression Modeling

Marketing means the strategies and tactics an organization undertakes for attracting consumers to promote the buying or selling of a product or service. Active marketing is about receiving messages from potential buyers to create ways to influence their purchasing decisions. Advertising is one of the most prominent marketing strategies to promote products to consumers. It is well known that advertisement has a significant impact on the sale of certain goods or services. In this paper, we consider two mediums of advertisement, such as Facebook (which is an online medium) and Newspaper (which is a printed medium). We consider a dataset representing the advertising budget (in hundreds of US dollars) of an electronic company and the sales of that company. We apply the quantitative research approach, and the data which are used in this research are secondary data. For analysis purposes, we consider a statistical tool called simple linear regression modeling. To check the significance of the advertising on sale, definite statistical tests are applied. Based on the findings of this research, it is observed that advertising has a significant impact on sales. It is also showed that spending money on advertising through Facebook has better sales than newspapers. The finding of this research shows that the use of computer-based technologies and online mediums has a brighter future for advertising. Furthermore, a new statistical model is introduced using the Z family approach. The proposed model is very interesting and possesses heavy-tailed properties. Finally, the applicability of the proposed model is illustrated by considering the financial dataset.

scholarly journals Examining the Interdependence between the Exchange Rates of China and ASEAN Countries: A Canonical Vine Copula Approach

2019 ◽  
Vol 11 (19) ◽  
pp. 5487 ◽  
Author(s):  
Liu ◽  
Wang ◽  
Sriboonchitta
Keyword(s):  
Exchange Rates ◽  
Tail Dependence ◽  
Dependence Structure ◽  
Positive Dependence ◽  
Asymmetric Dependence ◽  
Before And After ◽  
Copula Approach ◽  
Asean Countries ◽  
Vine Copula ◽  
Vine Copulas

Based on the canonical vine (C-vine) copula approach, this paper examines the interdependence between the exchange rates of the Chinese Yuan (CNY) and the currencies of major Association of Southeast Asian Nations (ASEAN) countries. The differences in the dependence structure and degree between currencies before and after the Belt and Road (B&R) Initiative were compared in order to investigate the changing role of the Renminbi (RMB) in the ASEAN foreign exchange markets. The results indicate a positive dependence between the exchange rate returns of CNY and the currencies of ASEAN countries and show the rising power of RMB in the regional currency markets after the B&R Initiative was launched. Besides this, the Malaysian Ringgit proved to be most relevant to the other ASEAN currencies, thus playing an important role in the stability of regional financial markets. Moreover, evidence of tail dependence was found in the returns of three currency pairs after the B&R Initiative, which implies the presence of asymmetric dependence between exchange rates. The results from time-varying C-vine copulas further confirmed the robustness of the results from the static C-vine copulas.

scholarly journals Truncation Mixture R-Vine Copulas

2021 ◽  
Author(s):  
Fadhah Alanazi
Keyword(s):  
Simulation Study ◽  
Real Data ◽  
Computational Time ◽  
Model Parameters ◽  
Copula Models ◽  
Truncation Level ◽  
Truncation Method ◽  
Tree Estimation ◽  
Vine Copula ◽  
Vine Copulas

Uncovering hidden mixture correlation among variables have been investigating in the literature using mixture R-vine copula models. These models are hierarchical in nature. They provides a huge flexibility for modelling multivariate data. As the dimensions increases, the number of the model parameters that need to be estimated is increased dramatically, which becomes along with huge computational times and efforts. This situation becomes even much more harder and complicated in the mixture Regular vine models. Incorporating truncation method with mixture Regular vine models will reduce the computation difficulty for the mixture based models. In this paper, tree-by-tree estimation mixture model is joined with the truncation method, in order to reduce the computational time and the number of the parameters that need to be estimated in the mixture vine copula models. A simulation study and a real data applications illustrated the performance of the method. In addition, the real data applications show the affect of the mixture components on the truncation level.

scholarly journals The Relationship between Economic Growth, Small and Medium Enterprises and the Number of Employees in Malaysia using Vine Copula Approach

2021 ◽  
Vol 50 (8) ◽  
pp. 2455-2468
Author(s):  
Mazmira Adan ◽  
Saiful Izzuan Hussain
Keyword(s):  
Economic Growth ◽  
Small And Medium Enterprises ◽  
Value Added ◽  
Data Sets ◽  
Strong Positive Correlation ◽  
Copula Approach ◽  
Vine Copula ◽  
Medium Enterprises ◽  
The Relationship ◽  
Vine Copulas

This paper investigates the dynamic linkages of dependency between economic growth, the development of small and medium enterprises (SMEs) in Malaysia represented by SMEs’ value-added, and the number of employees in Malaysia, shown by the total number of SMEs’ employees in Malaysia as an additional variable. A vine copula methods was used to assess the linkage between these three variables by utilizing the panel data sets from 1998-2017. The results confirmed that dependency modeling, using vine copulas, is useful for high dimensional distributions of binding affinity between these variables. Vine copulas provide a versatile and flexible approach to the study. The results show that there is a strong positive correlation among the variables. The dependence between the SMEs’ growth and the total number of employees is the strongest, followed by the dependence between SMEs’ growth and economic growth. This study also found that the interrelation among the variables and reliance structure is also influenced by the country’s economic system and situation.

scholarly journals Beyond linear dynamic functional connectivity: a vine copula change point model

2021 ◽  
Author(s):  
Xin Xiong ◽  
Ivor Cribben
Keyword(s):  
Functional Connectivity ◽  
Change Point ◽  
Sliding Window ◽  
Brain Regions ◽  
Change Points ◽  
Fmri Data ◽  
Data Sets ◽  
Dynamic Functional Connectivity ◽  
Vine Copula ◽  
Vine Copulas

To estimate dynamic functional connectivity for functional magnetic resonance imaging (fMRI) data, two approaches have dominated: sliding window and change point methods. While computationally feasible, the sliding window approach has several limitations. In addition, the existing change point methods assume a Gaussian distribution for and linear dependencies between the fMRI time series. In this work, we introduce a new methodology called Vine Copula Change Point (VCCP) to estimate change points in the functional connectivity network structure between brain regions. It uses vine copulas, various state-of-the-art segmentation methods to identify multiple change points, and a likelihood ratio test or the stationary bootstrap for inference. The vine copulas allow for various forms of dependence between brain regions including tail, symmetric and asymmetric dependence, which has not been explored before in the analysis of neuroimaging data. We apply VCCP to various simulation data sets and to two fMRI data sets: a reading task and an anxiety inducing experiment. In particular, for the former data set, we illustrate the complexity of textual changes during the reading of Chapter 9 in Harry Potter and the Sorcerer's Stone and find that change points across subjects are related to changes in more than one type of textual attributes. Further, the graphs created by the vine copulas indicate the importance of working beyond Gaussianity and linear dependence. Finally, the R package vccp implementing the methodology from the paper is available from CRAN.

scholarly journals Worst-Case Expected Shortfall with Univariate and Bivariate Marginals

2020 ◽  
Author(s):  
Anulekha Dhara ◽  
Bikramjit Das ◽  
Karthik Natarajan
Keyword(s):  
Risk Management ◽  
Portfolio Optimization ◽  
Polynomial Time ◽  
Expected Shortfall ◽  
Tree Structure ◽  
Discrete Distributions ◽  
Worst Case ◽  
Vine Copula ◽  
Multiple Experts ◽  
Vine Copulas

Computing and minimizing the worst-case bound on the expected shortfall risk of a portfolio given partial information on the distribution of the asset returns is an important problem in risk management. One such bound that been proposed is for the worst-case distribution that is “close” to a reference distribution where closeness in distance among distributions is measured using [Formula: see text]-divergence. In this paper, we advocate the use of such ambiguity sets with a tree structure on the univariate and bivariate marginal distributions. Such an approach has attractive modeling and computational properties. From a modeling perspective, this provides flexibility for risk management applications where there are many more choices for bivariate copulas in comparison with multivariate copulas. Bivariate copulas form the basis of the nested tree structure that is found in vine copulas. Because estimating a vine copula is fairly challenging, our approach provides robust bounds that are valid for the tree structure that is obtained by truncating the vine copula at the top level. The model also provides flexibility in tackling instances when the lower dimensional marginal information is inconsistent that might arise when multiple experts provide information. From a computational perspective, under the assumption of a tree structure on the bivariate marginals, we show that the worst-case expected shortfall is computable in polynomial time in the input size when the distributions are discrete. The corresponding distributionally robust portfolio optimization problem is also solvable in polynomial time. In contrast, under the assumption of independence, the expected shortfall is shown to be #P-hard to compute for discrete distributions. We provide numerical examples with simulated and real data to illustrate the quality of the worst-case bounds in risk management and portfolio optimization and compare it with alternate probabilistic models such as vine copulas and Markov tree distributions.

scholarly journals Explaining predictive models using Shapley values and non-parametric vine copulas

2021 ◽  
Vol 9 (1) ◽  
pp. 62-81
Author(s):  
Kjersti Aas ◽  
Thomas Nagler ◽  
Martin Jullum ◽  
Anders Løland
Keyword(s):  
Traditional Approach ◽  
Simulated Data ◽  
Real Data ◽  
Data Sets ◽  
Data Set ◽  
Wide Range ◽  
Shapley Values ◽  
Non Gaussian ◽  
Vine Copula ◽  
Vine Copulas

Abstract In this paper the goal is to explain predictions from complex machine learning models. One method that has become very popular during the last few years is Shapley values. The original development of Shapley values for prediction explanation relied on the assumption that the features being described were independent. If the features in reality are dependent this may lead to incorrect explanations. Hence, there have recently been attempts of appropriately modelling/estimating the dependence between the features. Although the previously proposed methods clearly outperform the traditional approach assuming independence, they have their weaknesses. In this paper we propose two new approaches for modelling the dependence between the features. Both approaches are based on vine copulas, which are flexible tools for modelling multivariate non-Gaussian distributions able to characterise a wide range of complex dependencies. The performance of the proposed methods is evaluated on simulated data sets and a real data set. The experiments demonstrate that the vine copula approaches give more accurate approximations to the true Shapley values than their competitors.

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