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•Synoptic-scale climate drivers are used to model seasonal wheat yield.•A seasonal lag relationship is used to forecast wheat yield using D-vine copulas.•Five-fold cross-validation is ...used to estimate out-of-sample accuracy of forecast models.•D-vine copulas are superior to LQR method, hierarchical Archimedean and meta-elliptical copulas.•Forecasts with climate drivers enable production strategies to optimise yield and profit.
Twelve large-scale climate drivers are employed to investigate their spatio-temporal influence on the variability of seasonal wheat yield in five major wheat-producing states across Australia using data for the period 1983–2013. Generally, the fluctuations in the Indian Ocean appear to have a dominant effect on the Australian wheat crop in all states except Western Australia, while the impact of oceanic conditions in the Pacific region is much stronger in Queensland. The results show a statistically significant negative correlation between the Indian Ocean Dipole (IOD) and the anomalous wheat yield in the early growing stage of the crop in the eastern and southeastern wheat belt regions. This correlation suggests that the wheat yield can be skillfully forecast 3–6 months ahead, supporting early decision-making in regard to precision agriculture. In this study, we use vine copula models to capture climate-yield dependence structures, including the occurrence of extreme events (i.e., the tail dependences). The co-occurrence of extreme events is likely to enhance the impacts of climate mode and this can be quantified probabilistically through conditional copula-based models. Generally, the developed D-vine quantile regression model provide greater accuracy for the forecasting of wheat yield at given different confidence levels compared to the traditional linear quantile regression (LQR) method. A five-fold cross-validation approach is also used to estimate the out-of-sample accuracy of both copula-statistical forecasting models. These findings provide a comprehensive analysis of the spatio-temporal impacts of different climate mode indices on Australian wheat crops. Improved quantification of the impacts of large-scale climate drivers on the wheat yield can allow a development of suitable planning processes and crop production strategies designed to optimize the yield and agricultural profit.
We propose a new family of directional dependence measures for multivariate distributions. The family of dependence measures is indexed by α≥1. When α=1, they measure the strength of dependence along ...different paths to the joint upper or lower orthant. For α large, they become tail-weighted dependence measures that put more weight in the joint upper or lower tails of the distribution. As α→∞, we show the convergence of the directional dependence measures to the multivariate tail dependence function and characterize the convergence pattern with an asymptotic expansion. This expansion leads to a method to estimate the multivariate tail dependence function using weighted least square regression. We develop rank-based sample estimators for the tail-weighted dependence measures and establish their asymptotic distributions. The practical utility of the tail-weighted dependence measures in multivariate tail inference is further demonstrated through their application to a financial dataset.
In this study, using vine copulas and tree sequences, dependence analysis of groundwater quality variables (Total hardness (TH), Sodium adsorption ratio (SAR), Sodium percentage (Na %) and magnesium ...(Mg)) was performed. For this purpose, the tree sequence of vine copulas including regular vine (R-vine), independent version of R-vine, also Gaussian version of R-vine, Gaussian independent version of R-vine, canonical vine (C-vine), independent version of C-vine, drawable vine (D-vine) and independent D-vine were evaluated independently in pairwise variables analysis. The study results of dependence structures and tree sequences of Vine copulas showed that among the studied copulas, the performance of the independent C-vine was 3.8 % better than R-vine and 0.25 % (insignificant and negligible) better than D-vine. The tree sequences provided by independent C-vine preserve correlation of pairwise variables until the last tree. In the last tree of independent C-vine, edge correlation of Mg, Na % | TH, and SAR reaches zero. Due to the proper performance of D-vine in dependence analysis of the studied variables, this copula is introduced as the selected copula.
Copulas have proven to be very successful tools for the flexible modeling of cross-sectional dependence. In this paper we express the dependence structure of continuous-valued time series data using ...a sequence of bivariate copulas. This corresponds to a type of decomposition recently called a "vine" in the graphical models literature, where each copula is entitled a "pair-copula." We propose a Bayesian approach for the estimation of this dependence structure for longitudinal data. Bayesian selection ideas are used to identify any independence pair-copulas, with the end result being a parsimonious representation of a time-inhomogeneous Markov process of varying order. Estimates are Bayesian model averages over the distribution of the lag structure of the Markov process. Using a simulation study we show that the selection approach is reliable and can improve the estimates of both conditional and unconditional pairwise dependencies substantially. We also show that a vine with selection outperforms a Gaussian copula with a flexible correlation matrix. The advantage of the pair-copula formulation is further demonstrated using a longitudinal model of intraday electricity load. Using Gaussian, Gumbel, and Clayton pair-copulas we identify parsimonious decompositions of intraday serial dependence, which improve the accuracy of intraday load forecasts. We also propose a new diagnostic for measuring the goodness of fit of high-dimensional multivariate copulas. Overall, the pair-copula model is very general and the Bayesian method generalizes many previous approaches for the analysis of longitudinal data. Supplemental materials for the article are also available online.
In this paper, a new generalization of the Pareto type II model is introduced and studied. The new density canbe “right skewed” with heavy tail shape and its corresponding failure rate can be ...“J-shape”, “decreasing” and “upside down (or increasing-constant-decreasing)”. The new model may be used as an “under-dispersed” and “over-dispersed” model. Bayesian and non-Bayesian estimation methods are considered. We assessed the performance of all methods via simulation study. Bayesian and non-Bayesian estimation methods are compared in modeling real data via two applications. In modeling real data, the maximum likelihood method is the best estimation method. So, we used it in comparing competitive models. Before using the the maximum likelihood method, we performed simulation experiments to assess the finite sample behavior of it using the biases and mean squared errors.
Since the pioneering work of Embrechts and co-authors in 1999, copula models have enjoyed steadily increasing popularity in finance. Whereas copulas are well studied in the bivariate case, the ...higher-dimensional case still offers several open issues and it is far from clear how to construct copulas which sufficiently capture the characteristics of financial returns. For this reason, elliptical copulas (i.e. Gaussian and Student-t copula) still dominate both empirical and practical applications. On the other hand, several attractive construction schemes have appeared in the recent literature promising flexible but still manageable dependence models. The aim of this work is to empirically investigate whether these models are really capable of outperforming its benchmark, i.e. the Student-t copula and, in addition, to compare the fit of these different copula classes among themselves.
We consider the class of bivariate copulas that are invariant under truncation with respect to the first variable. The elements of this class are hence characterized in terms of a suitable ...differential equation, which allows to express also their positive dependence properties. Various results about bounds for this class under special constraints are hence considered, with particular emphasis on the case when either the associated Spearman's ρ or Kendall's τ is known.
Abstract The article describes the behavior of aesthetic adjectives ( bonito ‘beautiful’) in the so-called innovative constructions with estar ‘be estar ’, documented in some American varieties of ...Spanish. These innovative structures ( El poema está bonito ‘The poem is beautiful’) do not compare stages of the subject with respect to an aesthetic property (as would be their meaning in general Spanish), but rather express a perspectivized assertion, linked to the subjective judgment of the speaker about a particular quality. The article explains this pattern of variation on the basis of the work by Gumiel-Molina, Moreno-Quibén and Pérez-Jiménez (2020) and Moreno-Quibén (2022) , according to which the classes of adjectives that appear in perspectivized estar- sentences have undergone a process of argument augmentation. Aesthetic adjectives in innovative estar- construction have an experiencer in their argument structure in the varieties of Spanish where this construction is possible. This experiencer serves as the basis for establishing the comparison required by estar and ultimately gives rise to the subjective/perspectivized meaning of the copular structure.
This paper examines the dependence structure between crude oil benchmark prices using copulas. By considering several copula models with different conditional dependence structures and time-varying ...dependence parameters, we find evidence of significant symmetric upper and lower tail dependence between crude oil prices. These findings suggest that crude oil prices are linked with the same intensity during bull and bear markets, thus supporting the hypothesis that the oil market is 'one great pool'--in contrast with the hypothesis that states that the oil market is regionalized. Our findings on crude oil price co-movements also have implications for risk management, hedging strategies and asset pricing.
•Nonparametric copula density was introduced to model extreme river water temperature and low flow for the Swiss River.•Beta kernel estimator, Bernstein estimator and Transformation kernel estimator ...were employed and compared to approximate copula density.•Estimated bivariate exceedance probabilities and joint and conditional return periods benefit aquatic and freshwater ecosystems.
This study proposed a nonparametric copula hazard framework in the joint risk of river water temperature (RWT) and low flow (LF) events for aquatic ecosystems, specifically ectotherm fish. This nonparametric copula density can adapt to any mutual dependence structure, providing greater flexibility. This can reduce the risk of misspecification if the underlying assumption is violated compared to conventional parametric or semiparametric copula settings. The analysis uses nonparametric copulas densities like Beta kernel copula estimator (BKCE), Bernstein copula estimator (BCE), and Transformation kernel estimator (TKE), conjoined with Gaussian Kernel density estimations (GKDEs) and parametric marginals for joint annual maximum RWT (AMRWT) and LF. The study compares different models for analyzing five Swiss river basins: parametric copulas with best-fitted GKDE and parametric margins, versus nonparametric copula. Six bandwidth selectors are estimated to fit GKDE. BKCE with GKDE margins outperformed most stations, while TKE and BCE density with GKDE margins work best for only one station. However, at one station, BKCE with best-fitted parametric margins is outperformed. All stations except Station 2473 are characterized by temperature and low flows that may be conducive to stress of a number of aquatic species, causing high AMRWT (exceeding 19 °C) and minimum LF or specific discharge, SD) quantiles at low AND-joint return periods (RP). AND (i.e. low flow AND high temperature) hazard events are less likely to occur together than OR hazard events, while univariate RPs happen more often than OR-joint RPs. In addition, the joint RP of AMRWT, given LF at different percentiles, significantly affected AMRWT for various LF conditions. Higher AMRWT with low flow conditions results in lower joint RP (except station 2473), and this stress level would be reduced when conditioned with high LF events at the same AMRWT. Station 2473 has high LF even at low percentiles, and with low AMRWT makes it very less stressful than other station under different LF conditions. Summer river flow maintenance can improve aquatic environments with high RWT. Analyzing joint statistics is crucial to understanding mutual risk in freshwater ecosystems.
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