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Is global diversification rational? Evidence from emerging equity markets through mixed copula approach

In this article, we aim to model the level and structure of the dependence between the worlds leading stock markets and those of the emerging market groups – Europe, Latin America and Far East. To this end we use a mixture model of Gaussian, Gumbel and Gumbel survival copulas. Our results indicate that none of the pairs of stock markets exhibit a right-tail dependence structure. All valid models exhibit a mixture of Gaussian and left-tail dependence structure. Our findings imply that Gaussian dependence structure is dominant in most of the models. The emerging equity markets in the European region exhibit the most significant dependence structure with the world leaders. Furthermore, most of the emerging equity markets have a significant dependence structure with the US stock market. We further compare our findings with the results of the conventional correlation coefficients and conclude the importance of using copula models in analysing the portfolio diversification opportunities. Our findings overall indicate two important remarks: First, the copula models reveal better indicators for global investors to establish a diversified portfolio; Second, international equity markets exhibit significant dependence, which leaves a smaller opportunity to benefit from international portfolio diversification.

On the Mean Remaining Strength of the k-Out-of-n:F System with Exchangeable Components

In this article, we consider the mean remaining strength of a k-out-of-n:F system in the stress–strength setup for the exchangeable components. We provide some results for parallel and series systems under this setup, where X1, X2, …, Xn are the strengths of the components designed under the common stress. An illustrative example is given for the k-out-of- n:F system using the multivariate FGM distribution.

On the mean remaining strength at the system level for some bivariate survival models based on exponential distribution

In this paper, we consider the mean remaining strength at the system level of two-component parallel and series systems in the case of possibly dependent components which are subject to a common stress. We assume that the strengths of the components have FGM, Freunds, Marshall-Olkin and Block-Basus bivariate exponential models. A numerical study based on the generated data set is performed to obtain the maximum likelihood estimates of the mean remaining strength for two-component parallel system in the stress-strength setup.

Testing independence for Archimedean copula based on Bernstein estimate of Kendall distribution function

ABSTRACT In this study, we estimate the Kendall distribution function for Archimedean copula family using Bernstein polynomial approximation and we investigate its performance by Monte Carlo simulation. Then, we introduce a nonparametric test of independence which is based on Cramér–von-Mises distance of the new estimate of Kendall distribution function. Also, we examine the power and the size of the test and we compare it with the classical nonparametric test that is based on the empirical Kendall distribution function.

Estimation and goodness-of-fit procedures for Farlie–Gumbel–Morgenstern bivariate copula of order statistics

In this study, we provide the Farlie–Gumbel–Morgenstern bivariate copula of rth and sth order statistics. The main emphasis in this study is on the inference procedure which is based on the maximum pseudo-likelihood estimate for the copula parameter. As for the methodology, goodness-of-fit test statistic for copulas which is based on a Cramér–von Mises functional of the empirical copula process is applied for selecting an appropriate model by bootstrapping. An application of the methodology to simulated data set is also presented.