This paper investigates the dynamics of the co-movement of GCC stock market returns with global oil market uncertainty, using an ARMA-DCC-EGARCH and time varying Student-t copula models. Empirical ...results demonstrate that oil uncertainty has significant and time varying impacts on the GCC stock returns. The GCC stock returns are found to be negatively affected by oil market uncertainty for almost the entire period under examination. More interestingly, we find that the impact of oil price uncertainty differs across GCC member states and allows for grouping. The results also show that the stock markets of Oman and Bahrain are relatively less sensitive to the oil uncertainty factor, thus offering investors and portfolio managers different investment options and portfolio diversification opportunities across GCC members.
•This paper investigates the dynamics of the co-movement of GCC stock market returns with global oil market uncertainty, using an ARMA-DCC-EGARCH and time varying Student-t copula models.•Empirical results demonstrate that oil uncertainty has significant and time varying impacts on the GCC stock returns.•The GCC stock returns are found to be negatively affected by oil market uncertainty for almost the entire period under examination.•More interestingly, we find that the impact of oil price uncertainty differs across GCC member states and allow for grouping.•Differing dependence on oil price uncertainty offers portfolio diversification opportunities across GCC members.
This paper examines the time-varying dependence structure of commodity futures portfolios based on multivariate dynamic copula models. The importance of accounting for time-variation is emphasized in ...the context of the Basel traffic light system. We enhance the flexibility of this structure by modeling regimes with multivariate mixture copulas and by applying the dynamic conditional correlation model (DCC) to multivariate elliptical copulas. The most suitable dynamic dependence model in terms of in-sample and out-of sample valuation is the dynamic Student-t-Clayton mixture copula, followed by the dynamic Student-t copula, and the dynamic Gaussian-Clayton mixture. In comparison to the multivariate normal model, the dynamic Clayton copula also scales down significantly the number of VaR(99%) violations during the 2007/08 financial crisis period. The predictive performance of our multivariate dynamic copula models confirms its superiority over bivariate regime-switching copula models for various states of the economy.
We propose a family of goodness-of-fit tests for copulas. The tests use generalizations of the information matrix (IM) equality of White and so relate to the copula test proposed by Huang and ...Prokhorov. The idea is that eigenspectrum-based statements of the IM equality reduce the degrees of freedom of the test's asymptotic distribution and lead to better size-power properties, even in high dimensions. The gains are especially pronounced for vine copulas, where additional benefits come from simplifications of score functions and the Hessian. We derive the asymptotic distribution of the generalized tests, accounting for the nonparametric estimation of the marginals and apply a parametric bootstrap procedure, valid when asymptotic critical values are inaccurate. In Monte Carlo simulations, we study the behavior of the new tests, compare them with several Cramer-von Mises type tests and confirm the desired properties of the new tests in high dimensions.
Modeling Multivariate Count Data Using Copulas Nikoloulopoulos, Aristidis K.; Karlis, Dimitris
Communications in statistics. Simulation and computation,
01/2010, Letnik:
39, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Multivariate count data occur in several different disciplines. However, existing models do not offer great flexibility for dependence modeling. Models based on copulas nowadays are widely used for ...continuous data dependence modeling. Modeling count data via copulas is still in its infancy; see the recent article of Genest and Nešlehová (
2007
). A series of different copula models providing various residual dependence structures are considered for vectors of count response variables whose marginal distributions depend on covariates through negative binomial regressions. A real data application related to the number of purchases of different products is provided.
Different sufficient conditions for stochastic comparisons between random vectors have been described in the literature. In particular, conditions for the comparison of random vectors having the same ...copula, i.e., the same dependence structure, may be found in
Müller and Scarsini (2001). Here we provide conditions for the comparison, in the usual stochastic order sense and in other weaker stochastic orders, of two time transformed exponential bivariate lifetimes having different copulas. Some examples of applications are provided too.
AbstractThis paper presents a novel copula-based approach to model the joint cumulative distribution function (JCDF) of wind speed and direction for wind-resistant design of engineering structures. ...Copula functions enable the JCDF to be obtained with the corresponding marginal distributions of wind speed and wind direction. The daily maximum wind speed recorded during 1971–2017 in Dali, China, was collected and used as the data source. The Weibull distribution was applied to represent the marginal distribution of wind speed; meanwhile, the marginal distribution of wind direction was modeled by the von Mises distribution. The Farlie-Gumbel-Morgenstern (FGM) and four commonly used Archimedean copulas were employed to construct the continuous bivariate JCDF of wind speed and direction. The simulation results were compared with those obtained using the traditional methods, i.e., the approaches based on multiplication rules and angular-linear (AL) model. The statistics of the coefficient of determination R2 and root-mean-squared error (RMSE) obtained in the regression analysis were used to judge the goodness of fit of each approach. The analytical results show that the approach based on copulas can not only yield good JCDF estimations of wind speed and direction, but also provide an effective and practical way to predict the extreme wind speed at a certain return period. Moreover, the estimated extreme wind speed varies significantly in the 16 directions and the predicted extreme wind speed in the studied region will be unreliable when neglecting the joint effect of wind speed and direction.
Investigating serial dependence is an important step in statistical process control (SPC). One recent approach is to fit a copula-based Markov chain model to perform SPC, which provides an attractive ...alternative to the traditional AR1 model. However, methodologies for model diagnostic have not been considered. In this paper, we develop two different approaches for model diagnostic procedures for copula-based Markov chain models. The first approach employs a formal test based on the Kolmogorov-Smirnov or the Cramér-von Mises statistics with aid of a parametric bootstrap. The second approach employs the second-order Markov chain model to examine the Markov property in the model. This second approach itself is a new SPC method. We made all the computing methodologies available in the R Copula.Markov package, and check their performance by simulations. We analyze three datasets for illustration.
•We examine the hedging and safe haven ability of diamonds and five precious metals.•We compare the performance of diamond versus metals in terms of hedging and safe haven against USD ...depreciation.•Empirical results show that gold, silver, palladium and platinum outperform diamonds in term of hedging against USD movement.•Our finding for safe haven study reveals that both precious metals and diamonds serve as a weak safe haven.
Historically, commodity market, particularly the metal market has been used as a hedge for stock market and currencies during time of distress. In this paper, we shed light on a new alternative asset and examine the hedging and safe haven ability of diamonds and five precious metals that is, gold, silver, palladium, platinum and rhodium by using copula process. Then, we compare the performance of diamond versus metals in terms of hedging and safe haven against USD depreciation. Our empirical results show that gold, silver, palladium and platinum outperform diamonds and rhodium in term of hedging against USD movement. Our finding for safe haven study reveals that both precious metals and diamonds serve as a weak safe haven.
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