Bernstein estimator for unbounded copula densities

IF 1.3 Q2 STATISTICS & PROBABILITY
T. Bouezmarni, El Ghouch, A. Taamouti
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引用次数: 26

Abstract

Abstract Copulas are widely used for modeling the dependence structure of multivariate data. Many methods for estimating the copula density functions are investigated. In this paper, we study the asymptotic properties of the Bernstein estimator for unbounded copula density functions. We show that the estimator converges to infinity at the corner and we establish its relative convergence when the copula density is unbounded. Also, we provide the uniform strong consistency of the estimator on every compact in the interior region. We investigate the finite sample performance of the estimator via an extensive simulation study and we compare the Bernstein copula density estimator with other nonparametric methods. Finally, we consider an empirical application where the asymmetric dependence between international equity markets (US, Canada, UK, and France) is examined.
无界联结密度的Bernstein估计量
copula被广泛用于多变量数据的依赖结构建模。研究了许多估计联结密度函数的方法。本文研究了无界联结密度函数的Bernstein估计量的渐近性质。我们证明了估计量在角处收敛到无穷,并证明了它在联结密度无界时的相对收敛性。此外,我们还给出了估计量在内区域上的一致强相合性。我们通过广泛的模拟研究研究了估计器的有限样本性能,并将Bernstein copula密度估计器与其他非参数方法进行了比较。最后,我们考虑了一个实证应用,其中检验了国际股票市场(美国、加拿大、英国和法国)之间的不对称依赖。
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来源期刊
Statistics & Risk Modeling
Statistics & Risk Modeling STATISTICS & PROBABILITY-
CiteScore
1.80
自引率
6.70%
发文量
6
期刊介绍: Statistics & Risk Modeling (STRM) aims at covering modern methods of statistics and probabilistic modeling, and their applications to risk management in finance, insurance and related areas. The journal also welcomes articles related to nonparametric statistical methods and stochastic processes. Papers on innovative applications of statistical modeling and inference in risk management are also encouraged. Topics Statistical analysis for models in finance and insurance Credit-, market- and operational risk models Models for systemic risk Risk management Nonparametric statistical inference Statistical analysis of stochastic processes Stochastics in finance and insurance Decision making under uncertainty.
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