Dingcheng Wang

A note on the complete convergence for sequences of pairwise NQD random variables

In this paper, complete convergence and strong law of large numbers for sequences of pairwise negatively quadrant dependent (NQD) random variables with non-identically distributed are investigated. The results obtained generalize and extend the relevant result of Wu (Acta. Math. Sinica. 45(3), 617-624, 2002) for sequences of pairwise NQD random variables with identically distributed.2000 MSC: 60F15.

A note on the strong limit theorem for weighted sums of sequences of negatively dependent random variables

In this paper, the strong limit theorem for weighted sums of sequences of negatively dependent random variables is further studied. As an application, the complete convergence theorem for sequences of negatively dependent random variables is obtained. Our results partly generalize and improve the corresponding results of Cai (Metrika 68:323-331, 2008) and Wang et al. (Rev. R. Acad. Cienc. Exactas Fís. Nat., Ser. a Mat., 2011, doi:10.1007/s13398-011-0048-0) to negatively dependent random variables under mild moment conditions.MSC:60F15.

Complete convergence and complete moment convergence for arrays of rowwise negatively superadditive dependent random variables

ABSTRACT In this paper, some complete convergence and complete moment convergence results for arrays of rowwise negatively superadditive dependent (NSD, in short) random variables are studied. The obtained theorems not only extend the result of Gan and Chen (2007) to the case of NSD random variables, but also improve them.

Tail asymptotic of discounted aggregate claims with compound dependence under risky investment

ABSTRACT This paper considers the tail asymptotic of discounted aggregate claims with compound dependence under risky investment. The price of risky investment is modeled by a geometric Lévy process, while claims are modeled by a one-sided linear process whose innovations further obeying a so-called upper tail asymptotic independence. When the innovations are heavy tailed, we derive some uniform asymptotic formulas. The results show that the linear dependence has significant impact on the tail asymptotic of discounted aggregate claims but the upper tail asymptotic independence is negligible.

The ruin probabilities of a discrete time risk model with one-sided linear claim sizes and dependent risks

ABSTRACT This article investigates the ruin probabilities of a discrete time risk model with dependent claim sizes and dependent relation between insurance risks and financial risks. The risk-free and risky investments of an insurer lead to stochastic discount factors {θn}n ⩾ 1. The claim sizes are assumed to follow a one-sided linear process with independent and identically distributed (i.i.d.) innovations {ϵn}n ⩾ 1. The i.i.d. random pairs {(ϵn, θn)}n ⩾ 1 follow a common bivariate Sarmanov-dependent distribution. When the common distribution of the innovations is heavy tailed, we establish some asymptotic estimates for the ruin probabilities of this discrete time risk model.

The finite-time ruin probability of a discrete-time risk model with GARCH discounted factors and dependent risks

ABSTRACT The finite-time ruin probability of a discrete-time risk model with dependent stochastic discount factors and dependent insurance and financial risks is investigated in this paper. Assume that the stochastic discount factors follow a GARCH process and the one-period insurance and financial risks form a sequence of independent and identically distributed random pairs, which are the copies of a random pair with a bivariate Sarmanov dependent distribution. When the common distribution of claim-sizes is heavy-tailed, we establish an asymptotic estimate for the finite-time ruin probability. Applying the result to a special case, we also get conservative asymptotic bounds. A numerical simulation is given at the end of the paper.

Complete convergence for weighted sums of negatively dependent random variables under the sub-linear expectations

ABSTRACT In this article, we study complete convergence theorems for weighted sums of negatively dependent random variables under the sub-linear expectations. Our results extend the corresponding results of Sung (2012) relative to the classical probability.