H igh price volatility and the risk are the main features of commodity markets. One way to reduce this risk is to apply the hedging policy by future contracts. In this regard, in this paper, we will calculate the optimal hedging ratios for OPEC oil. In this study, besides the multivariate GARCH Models, for the first time we use conditional Copula Models for modelling dependence structure between OPEC oil and WTI future contract with different maturities and estimating hedging ratio for OPEC oil by using WTI future contracts. The results of this study show that dependence structure between OPEC oil and WTI future contract in three maturities is asymmetric. In addition, results indicate that during the studied period (2003-2017), Copula-based Models have more efficiency in applying the hedging policy than multivariate GARCH Models. With an increase in the maturity of contracts, the average optimal hedge ratio increases. On the other hand, the highest performance of hedging strategies achieved by using WTI futures contract with six months maturity...

There is a relationship between choosing an activity and its duration, especially for non-mandatory activities. A number of studies have analyzed the decisions about an activity type and its duration independently, while some others have used joint Models. This paper contributes to the body of knowledge using nested-logit and Copula-based Models for assessing the existence of interdependency between, or a hierarchy of, the choice of non-mandatory activity and its relative duration. In the case of the nestedlogit model, it is assumed that error terms of these decisions are interrelated, although one is in uenced by another. In contrast, the Copula-based model can facilitate making a spatial error correlation between observational units without imposing the assumption of restrictive distribution on the dependency structures between the error components. The data available from Qazvin, a city in Iran, were used for estimating both nested-logit and Copula-based Models and the best variables explaining both choices for each model were selected. The Final Models were compared in terms of log-likelihood at convergence and adjusted likelihood ratio index. The results indicated that there were some common in uential observed and unobserved factors between these decisions. Also, the Copula-based joint model with  2 0 equal to 0. 134 outperformed nested-logit Models and provided better explanatory power.

Introduction: Analyzing long term survivors such as diabetic patients can't be done using the usual survival Models. One approach to analyze it is using defective distribution that doesn't force a pre-assumption of cure fraction to the model. To study more than one random variable interacting together, multivariate distributions may be used. However, most of multivariate distributions have complicated forms, which make the computations difficult. Besides, it may be hard to find a multivariate distribution that fits the data properly, especially in health care field. To get over this problem, one can use Copula approach. In literature, to the best of our knowledge, only one paper handled Copula defective Models and didn't consider the effect of covariates. In this paper, we take into consideration not only existed covariates but also unobserved ones by including frailty term. Methods: Two new Models are introduced. The first model, used Gumbel Copula to take the dependence into consideration together with the observed covariates. The second one take into consideration not only the dependence but also the unobserved covariates by integrating frailty term in to the model. Results: A diabetic retinopathy data is analyzed. The two Models indicated the existence of long-term survivals through negative parameters without the need of pre-assuming the existence of it. Including frailty term to the model helped in capturing more dependence between the variables. We compared the results using goodness of fit methods, and the results suggested that the model with frailty term is the best to be used. Conclusion: The two introduced Models correctly detected the existence of cure fraction with less estimated parameters than that in mixture cure fraction Models. Also, it has the advantage of not pre-assuming the existence of cure fraction to the model. comparing both Models, the model with frailty term fitted the data better.

A phased-mission system (PMS) involves several different tasks or phases that must be accomplished in sequence. The system configuration, task success criteria, and component failure characteristics may vary from phase to phase. Consequently, the reliability evaluation of PMSs is more challenging than that of single-phase in the field of system reliability analysis. The paper deals with the reliability evaluation of non-repairable Phased-Mission Systems with three phases and five phases involving dependent components in each phase.  The cumulative exposure model has been used to model a PMS, and the dependency between components of a system in a phase is modeled using the Gumbel-Hougaard Copula. Reliability importance analyses of the 3-PMS and  5-PMS  have also been carried out. The method developed has been illustrated using numerical examples. The proposed methodology can also be generalized to PMSs with more than five phases.