Vine-copula Based Models for Farmland Portfolio Management

U.S. farmland has achieved total returns of 10%-13% over the past decade with volatility of only 4%-5% (NCREIF Farmland Index). In addition, farmland returns have had low or negative correlation with traditional asset classes. These characteristics make farmland an attractive asset class for investors. Farmland, as a real asset, can also provide a hedge against inflation because farmland returns exhibit positive correlation with inflation. Over the past decade, annual U.S. farmland total return exceeds U.S. inflation rate by 3.55% (NCREIF Farmland Index and Consumer Price Index - Urban). With growing global demand for agricultural commodities and limited land to expand capacity, some investors expect that farmland will continue to generate superior returns for the foreseeable future

A multinomial quadrivariate D-vine copula mixed model for meta-analysis of diagnostic studies in the presence of non-evaluable subjects

Diagnostic test accuracy studies observe the result of a gold standard procedure that defines the presence or absence of a disease and the result of a diagnostic test. They typically report the number of true positives, false positives, true negatives and false negatives. However, diagnostic test outcomes can also be either non-evaluable positives or non-evaluable negatives. We propose a novel model for the meta-analysis of diagnostic studies in the presence of non-evaluable outcomes, which assumes independent multinomial distributions for the true and non-evaluable positives, and, the true and non-evaluable negatives, conditional on the latent sensitivity, specificity, probability of non-evaluable positives and probability of non-evaluable negatives in each study. For the random effects distribution of the latent proportions, we employ a drawable vine copula that can successively model the dependence in the joint tails. Our methodology is demonstrated with an extensive simulation study and applied to data from diagnostic accuracy studies of coronary computed tomography angiography for the detection of coronary artery disease. The comparison of our method with the existing approaches yields findings in the real data application that change the current conclusions

Behaviour of multivariate tail dependence coefficients

In applications tail dependence is an important property of a copula. Bivariate tail dependence is investigated in many papers, but multivariate tail dependence has not been studied widely. We define multivariate upper and lower tail dependence coefficients as limits of the probability that values of one marginal will be large if at least one of other marginals will be as large also. Further we derive some relations between introduced tail dependence and bivariate tail dependence coefficients. Applications have shown that the multivariate t-copula has been successfully used in practice because of its tail dependence property. Therefore, t-copula can be used as an alternative method for risk assessment under Solvency II for insurance models. We have paid attention to the properties of the introduced multivariate tail dependence coefficient for t-copula and examine it in the simulation experiment

Local Dependence for Bivariate Weibull Distributions Created by Archimedean Copula

في تحليل البقاء على قيد الحياة متعدد المتغيرات ، يعد تقدير دالة التوزيع متعدد المتغيرات و من ثم قياس علاقة و ارتباط بين أوقات البقاء ذات أهمية كبيرة. تُستخدم دالات الكوبيلا ، مثل ارخميديان كوبيلا، بشكل شائع لتقدير توزيعات المتغيرات غير المعروفة بناءً على الدوال الهامشية المعروفة. في هذا البحث تم استكشاف جدوى استخدام فكرة ارتباط الموضعي لتحديد افضل نموذج الكوبيلا و الأكثر كفاءة ، والذي يستخدم لبناء دالة ويبل ثنائي المتغير كدالة وقت البقاء ثنائي المتغير، من بين بعض انواع الآرخميديان كوبيلا. لتقييم كفاءة طريقة المقترحة ، تم تنفيذ دراسة محاكاة، وقد ثبت أن هذا طريقة مفيد للحالات العملية وقابل للتطبيق على مجموعات البيانات الحقيقية. و عند تنفيذ الإجراء المقترحة، على بيانات فعلية، على بيانات دراسة اعتلال الشبكية السكري وجد أن العيون المعالجة لديها فرصة أكبر لعدم فقدان البصر مقارنة بالعين غير المعالجة.In multivariate survival analysis, estimating the multivariate distribution functions and then measuring the association between survival times are of great interest. Copula functions, such as Archimedean Copulas, are commonly used to estimate the unknown bivariate distributions based on known marginal functions. In this paper the feasibility of using the idea of local dependence to identify the most efficient copula model, which is used to construct a bivariate Weibull distribution for bivariate Survival times, among some Archimedean copulas is explored. Furthermore, to evaluate the efficiency of the proposed procedure, a simulation study is implemented. It is shown that this approach is useful for practical situations and applicable for real datasets. Moreover, when the proposed procedure implemented on Diabetic Retinopathy Study (DRS) data, it is found that treated eyes have greater chance for non-blindness compared to untreated eyes

A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence

A bivariate copula mixed model has been recently proposed to synthesize diagnostic test accuracy studies and it has been shown that it is superior to the standard generalized linear mixed model in this context. Here, we call trivariate vine copulas to extend the bivariate meta-analysis of diagnostic test accuracy studies by accounting for disease prevalence. Our vine copula mixed model includes the trivariate generalized linear mixed model as a special case and can also operate on the original scale of sensitivity, specificity, and disease prevalence. Our general methodology is illustrated by re-analyzing the data of two published meta-analyses. Our study suggests that there can be an improvement on trivariate generalized linear mixed model in fit to data and makes the argument for moving to vine copula random effects models especially because of their richness, including reflection asymmetric tail dependence, and computational feasibility despite their three dimensionality

Land-atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity.

Compound extremes such as cooccurring soil drought (low soil moisture) and atmospheric aridity (high vapor pressure deficit) can be disastrous for natural and societal systems. Soil drought and atmospheric aridity are 2 main physiological stressors driving widespread vegetation mortality and reduced terrestrial carbon uptake. Here, we empirically demonstrate that strong negative coupling between soil moisture and vapor pressure deficit occurs globally, indicating high probability of cooccurring soil drought and atmospheric aridity. Using the Global Land Atmosphere Coupling Experiment (GLACE)-CMIP5 experiment, we further show that concurrent soil drought and atmospheric aridity are greatly exacerbated by land-atmosphere feedbacks. The feedback of soil drought on the atmosphere is largely responsible for enabling atmospheric aridity extremes. In addition, the soil moisture-precipitation feedback acts to amplify precipitation and soil moisture deficits in most regions. CMIP5 models further show that the frequency of concurrent soil drought and atmospheric aridity enhanced by land-atmosphere feedbacks is projected to increase in the 21st century. Importantly, land-atmosphere feedbacks will greatly increase the intensity of both soil drought and atmospheric aridity beyond that expected from changes in mean climate alone