Farlie Gumbel Morgenstern Copula Research Articles

Low Flow Regimes of the Tarim River Basin, China: Probabilistic Behavior, Causes and Implications

Droughts are a frequent occurrence in Xinjiang, China, and therefore fundamental to determining their hydrologic characteristics is low flow analysis. To that end, 11 probability distribution functions and 26 copulas functions were employed to analyze the changing characteristics of low flow regime (defined as seven-day low flow) of the Tarim River Basin. Results indicated that: (1) The Wakeby distribution satisfactorily described the probabilistic behavior of the low flow regime. According to Akaike Information Criterion (AIC), Bayesian Information Criterions (BIC), maximum likelihood, and other residual-based metrics, Tawn copula, Farlie–Gumbel–Morgenstern copula and Frank copula were the best choice and used in this current study. (2) After 1987, hydrological droughts of longer return periods were prone to higher occurrence frequency. (3) The low flow volume has been increasing in recent years due to the temperature-induced increase of snowmelt and increasing precipitation. However, hydrological droughts can be expected to occur due to the massive increase in water demand from the development of irrigated agriculture, increasing arable land and livestock farming. As a result, the water shortage in the lower Tarim River Basin will be increasingly severe under the influence of climate change and human activities. To alleviate the shortage would call for the development of water-saving agricultural irrigation, water-saving technology, conservation of eco-environment and sustainable development of local socio-economy.

A new extension of the FGM copula for negative association

ABSTRACTIn this paper we introduced a single parameter, absolutely continuous and radially symmetric bivariate extension of the Farlie-Gumbel-Morgenstern (FGM) family of copulas. Specifically, this extension measures the higher negative dependencies than most FGM extensions available in literature. Closed-form formulas for distribution, quantile, density, conditional distribution, regression, Spearman's rho, Kendall's tau, and Gini's gamma are obtained. In addition, a formula for random variate generations is presented in closed-form to facilitate simulation studies. We conduct both paired and multiple comparisons with Frank, Gaussian, and Plackett copulas to investigate the performance based on Vuong's test. Furthermore, the new copula is compared with Frank, Gaussian, and Plackett copulas using both Kolmogorov-Smirnov and Cramér-von Mises type test statistics. Finally, a bivariate dataset is analyzed to compare and illustrate the flexibility of the new copula for negative dependence.

Bivariate Kumaraswamy Models via Modified FGM Copulas: Properties and Applications

A copula is a useful tool for constructing bivariate and/or multivariate distributions. In this article, we consider a new modified class of FGM (Farlie–Gumbel–Morgenstern) bivariate copula for constructing several different bivariate Kumaraswamy type copulas and discuss their structural properties, including dependence structures. It is established that construction of bivariate distributions by this method allows for greater flexibility in the values of Spearman’s correlation coefficient, ρ and Kendall’s τ .

Bivariate dependence measures and bivariate competing risks models under the generalized FGM copula

The first part of this paper reviews the properties of bivariate dependence measures (Spearman’s rho, Kendall’s tau, Kochar and Gupta’s dependence measure, and Blest’s coefficient) under the generalized Farlie–Gumbel–Morgenstern (FGM) copula. We give a few remarks on the relationship among the bivariate dependence measures, derive Blest’s coefficient, and suggest simplifying the previously obtained expression of Kochar and Gupta’s dependence measure. The second part of this paper derives some useful measures for analyzing bivariate competing risks models under the generalized FGM copula. We obtain the expression of sub-distribution functions under the generalized FGM copula, which has not been discussed in the literature. With the Burr III margins, we show that our expression has a closed form and generalizes the reliability measure previously obtained by Domma and Giordano (Stat Pap 54(3):807–826, 2013).

Extension of de Bruijn's identity to dependent non-Gaussian noise channels

Abstract De Bruijn's identity relates two important concepts in information theory: Fisher information and differential entropy. Unlike the common practice in the literature, in this paper we consider general additive non-Gaussian noise channels where more realistically, the input signal and additive noise are not independently distributed. It is shown that, for general dependent signal and noise, the first derivative of the differential entropy is directly related to the conditional mean estimate of the input. Then, by using Gaussian and Farlie–Gumbel–Morgenstern copulas, special versions of the result are given in the respective case of additive normally distributed noise. The previous result on independent Gaussian noise channels is included as a special case. Illustrative examples are also provided.

On the expected discounted penalty function for a risk model with dependence under a multi-layer dividend strategy

ABSTRACTIn this article, we consider a dependent risk model in the presence of a multi-laydividend strategy. We construct the dependence structure between the claim size and interclaim time by a Farlie–Gumbel–Morgenstern copula. A piecewise integro-differential equations for the expected discounted penalty function with boundary conditions are established. A renewal equation satisfied by the expected discounted penalty function is obtained via the translation operator. Then, we provide a recursive approach to derive the analytical solution of the expected discounted penalty function. Finally, a numerical example is presented to illustrate the solution procedure.

Joint survival probability via truncated invariant copula

Given an intensity-based credit risk model, this paper studies dependence structure between default intensities. To model this structure, we use a multivariate shot noise intensity process, where jumps occur simultaneously and their sizes are correlated. Through very lengthy algebra, we obtain explicitly the joint survival probability of the integrated intensities by using the truncated invariant Farlie–Gumbel–Morgenstern copula with exponential marginal distributions. We also apply our theoretical result to pricing basket default swap spreads. This result can provide a useful guide for credit risk management.

Various measures of dependence of a new asymmetric generalized Farlie–Gumbel–Morgenstern copulas

ABSTRACTIn this paper, we discuss an asymmetric extension of Farlie–Gumbel–Morgenstern copulas studied by several authors and obtain the range of the parameter. We derive an expression for regression function and the properties of these copulas are studied in detail. Also, explicit expressions for various measures of association are obtained. These measures are numerically compared for some particular parametric values of the copulas.

A note on generalized Farlie-Gumbel-Morgenstern copulas

ABSTRACTIn this note, we discuss a generalization of a subclass of the Rodriguez-Lallena and Ubeda-Flores family of copulas, through order statistics. This generalized family includes an important class of the Farlie–Gumbel–Morgenstern (FGM) family of copulas, which are widely used in statistics. We derive the expressions for regression function and cumulative conditional expectation function. The explicit expressions for some measures of dependence like Spearman’s rho, Gini’s gamma coefficient, and quadrant dependence are discussed. Finally, Spearman’s rho is numerically tabulated and compared for some generalized families of FGM copulas.

A note on conjugate distributions for copulas

A family of conjugated distributions for a given type of copulas is defined in this paper. Those copulas can be written as a mixture of d‐dimensional parameter exponential functions. The generalized Farlie–Gumbel–Morgenstern copula is an example of this representation. This family is used to illustrate the estimation technique with real data. Also, the applicability of Bayesian predictive approach is shown in an education policy issue by defining goals for the number of students per class that leads to improve their performance at school. Copyright © 2014 John Wiley & Sons, Ltd.

Truncation invariant copulas for modeling directional dependence: Application to foreign currency exchange data

Directional dependence modeling has been applied to many research areas including economics, finance, biostatistics, and bioinformatics. The concept of directional dependence using copula regression functions has been introduced by Sungur [21]. So we propose a new copula family which incorporates the truncation invariant structure [20] into the generalized Farlie-Gumbel-Morgenstern (FGM) distributions. The directional dependence of the new truncated invariant FGM copulas will be also introduced in this research. We will show that there exists a directional dependence in our truncation invariant FGM copulas using Foreign Currency Exchange Data of the Canadian Dollar (CAD/USD), the Japanese Yen (JPY/USD), and the Korean Won (KRW/USD).

The maximum surplus before ruin for dependent risk models through Farlie–Gumbel–Morgenstern copula

We extend the classical compound Poisson risk model to consider the distribution of the maximum surplus before ruin where the claim sizes depend on inter-claim times via the Farlie–Gumbel–Morgenstern copula. We derive an integro-differential equation with certain boundary conditions for this distribution, of which the Laplace transform is provided. We obtain the renewal equation and explicit expressions for this distribution are derived when the claim amounts are exponentially distributed. Finally, we present numerical examples.

Perturbation of bivariate copulas

New types of constructions of bivariate copulas are introduced, discussed and exemplified. Based on a given copula C, we look for its perturbation into another copula CH, possibly close to C. A special stress is put on the perturbation of the basic copulas M, W, Π. Our results generalize several methods known from the literature, such as the Farlie–Gumbel–Morgenstern copula family, for example. An illustrative example when fitting copulas to real data is also added.

On the compound Poisson risk model with dependence and a threshold dividend strategy

In this paper, we consider the compound Poisson risk model with a threshold dividend strategy and a dependence structure modeled by a Farlie–Gumbel–Morgenstern copula. The integro-differential equations satisfied by the Gerber–Shiu functions and the expected discounted dividend payments paid until ruin respectively are derived. Further, by deriving and solving the renewal equations satisfied by the Gerber–Shiu functions and the expected discounted dividend payments, we give the explicit formulas for them.

Multivariate distribution defined with Farlie–Gumbel–Morgenstern copula and mixed Erlang marginals: Aggregation and capital allocation

In this paper, we investigate risk aggregation and capital allocation problems for a portfolio of possibly dependent risks whose multivariate distribution is defined with the Farlie–Gumbel–Morgenstern copula and mixed Erlang distribution marginals. In such a context, we first show that the aggregate claim amount has a mixed Erlang distribution. Based on a top-down approach, closed-form expressions for the contribution of each risk are derived using the TVaR and covariance rules. These findings are illustrated with numerical examples.

Mixtures of Farlie-Gumbel-Morgenstern Copulas

Some modifications of bivariate Farlie-Gumbel-Morgenstern (FGM) copulas are going to be explained in this article. These modifications are generated by using mixtures of bivariate FGM copula functions. The main goal of this study is to determine both the ranges of association parameter and the rate of correlation, and also observe the changes in local dependence function. An application, which is related with simulated data, is conducted and results are illustrated.

On a compound Poisson risk model with dependence and in the presence of a constant dividend barrier

In this paper, we consider a classical risk process with dependence and in the presence of a constant dividend barrier. The dependence structure between the claim amounts and the interclaim times is introduced through a Farlie–Gumbel–Morgenstern copula. We analyze the expectation of the discounted penalty function and the expectation of the present value of the distributed dividends. For each function, an integro‐differential equation with boundary conditions is derived, and the solution is provided. Finally, we find an explicit solution for each function when the claim amounts are exponentially distributed. We illustrate the impact of the dependence on these two quantities. Copyright © 2012 John Wiley & Sons, Ltd.

On a renewal risk process with dependence under a Farlie–Gumbel–Morgenstern copula

In this article, we consider an extension to the renewal or Sparre Andersen risk process by introducing a dependence structure between the claim sizes and the interclaim times through a Farlie–Gumbel–Morgenstern copula proposed by Cossette et al. (2010) for the classical compound Poisson risk model. We consider that the inter-arrival times follow the Erlang(n) distribution. By studying the roots of the generalised Lundberg equation, the Laplace transform (LT) of the expected discounted penalty function is derived and a detailed analysis of the Gerber–Shiu function is given when the initial surplus is zero. It is proved that this function satisfies a defective renewal equation and its solution is given through the compound geometric tail representation of the LT of the time to ruin. Explicit expressions for the discounted joint and marginal distribution functions of the surplus prior to the time of ruin and the deficit at the time of ruin are derived. Finally, for exponential claim sizes explicit expressions and numerical examples for the ruin probability and the LT of the time to ruin are given.

Analysis of ruin measures for the classical compound Poisson risk model with dependence

In this paper, we consider an extension to the classical compound Poisson risk model. Historically, it has been assumed that the claim amounts and claim inter-arrival times are independent. In this contribution, a dependence structure between the claim amount and the interclaim time is introduced through a Farlie–Gumbel–Morgenstern copula. In this framework, we derive the integro-differential equation and the Laplace transform (LT) of the Gerber–Shiu discounted penalty function. An explicit expression for the LT of the discounted value of a general function of the deficit at ruin is obtained for claim amounts having an exponential distribution.

Gerber–Shiu analysis in a perturbed risk model with dependence between claim sizes and interclaim times

In this paper, we consider a compound Poisson risk model perturbed by a Brownian motion. We construct the bivariate cumulative distribution function of the claim size and interclaim time by Farlie–Gumbel–Morgenstern copula. The integro-differential equations and the Laplace transforms for the Gerber–Shiu functions are obtained. We also show that the Gerber–Shiu functions satisfy some defective renewal equations. For exponential claims, some explicit expressions are obtained, and numerical examples for the ruin probabilities are also given.