scholarly journals A Note on the Tail Behavior of Randomly Weighted Sums with Convolution-Equivalently Distributed Random Variables

2013 ◽  
Vol 2013 ◽  
pp. 1-4
Author(s):  
Yang Yang ◽  
Jun-feng Liu ◽  
Yu-lin Zhang
Keyword(s):  
Asymptotic Behavior ◽  
Discrete Time ◽  
Finite Time ◽  
Risk Model ◽  
Random Variables ◽  
Weighted Sums ◽  
Financial Risks ◽  
Tail Behavior ◽  
Insurance Risk ◽  
Randomly Weighted Sums

We investigate the tailed asymptotic behavior of the randomly weighted sums with increments with convolution-equivalent distributions. Our obtained result can be directly applied to a discrete-time insurance risk model with insurance and financial risks and derive the asymptotics for the finite-time probability of the above risk model.

scholarly journals On the Kesten-type inequality for randomly weighted sums with applications to an operational risk model

Filomat ◽  
2021 ◽  
Vol 35 (6) ◽  
pp. 1879-1888
Author(s):  
Yishan Gong ◽  
Yang Yang ◽  
Jiajun Liu
Keyword(s):  
Value At Risk ◽  
Risk Model ◽  
Cell Model ◽  
Type Inequality ◽  
Random Variables ◽  
Operational Risk ◽  
Weighted Sums ◽  
Asymptotic Formulas ◽  
Tail Probabilities ◽  
Randomly Weighted Sums

This paper considers the randomly weighted sums generated by some dependent subexponential primary random variables and some arbitrarily dependent random weights. To study the randomly weighted sums with infinitely many terms, we establish a Kesten-type upper bound for their tail probabilities in presence of subexponential primary random variables and under a certain dependence among them. Our result extends the study of Chen [5] to the dependent case. As applications, we derive some asymptotic formulas for the tail probability and the Value-at-Risk of total aggregate loss in a multivariate operational risk cell model.

ASYMPTOTIC RUIN PROBABILITIES IN FINITE HORIZON WITH SUBEXPONENTIAL LOSSES AND ASSOCIATED DISCOUNT FACTORS

2005 ◽  
Vol 20 (1) ◽  
pp. 103-113 ◽  
Author(s):  
Qihe Tang
Keyword(s):  
Stochastic Processes ◽  
Discrete Time ◽  
Finite Time ◽  
Ruin Probability ◽  
Risk Model ◽  
Ruin Probabilities ◽  
Insurance Risk ◽  
Finite Time Ruin Probability ◽  
Discount Factors ◽  
Subexponential Class

Consider a discrete-time insurance risk model with risky investments. Under the assumption that the loss distribution belongs to a certain subclass of the subexponential class, Tang and Tsitsiashvili (Stochastic Processes and Their Applications 108(2): 299–325 (2003)) established a precise estimate for the finite time ruin probability. This article extends the result both to the whole subexponential class and to a nonstandard case with associated discount factors.

A note on the max-sum equivalence of randomly weighted sums of heavy-tailed random variables

2013 ◽  
Vol 18 (4) ◽  
pp. 519-525 ◽  
Author(s):  
Yang Yang ◽  
Kaiyong Wang ◽  
Remigijus Leipus ◽  
Jonas Šiaulys
Keyword(s):  
Random Variables ◽  
Tail Probability ◽  
Weighted Sums ◽  
Asymptotic Formulas ◽  
Dependence Structure ◽  
Insurance Risk ◽  
Risk Models ◽  
Randomly Weighted Sums ◽  
Random Weights ◽  
Heavy Tailed

This paper investigates the asymptotic behavior for the tail probability of the randomly weighted sums ∑k=1nθkXk and their maximum, where the random variables Xk and the random weights θk follow a certain dependence structure proposed by Asimit and Badescu [1] and Li et al. [2]. The obtained results can be used to obtain asymptotic formulas for ruin probability in the insurance risk models with discounted factors.

APPROXIMATION OF THE TAIL PROBABILITIES FOR BIDIMENSIONAL RANDOMLY WEIGHTED SUMS WITH DEPENDENT COMPONENTS

2018 ◽  
Vol 34 (1) ◽  
pp. 112-130
Author(s):  
Xinmei Shen ◽  
Mingyue Ge ◽  
Ke-Ang Fu
Keyword(s):  
Discrete Time ◽  
Ruin Probability ◽  
Risk Model ◽  
Weighted Sums ◽  
Tail Probabilities ◽  
Random Vectors ◽  
Moment Conditions ◽  
Randomly Weighted Sums ◽  
Asymptotic Formulae ◽  
Extended Regular Variation

AbstractLet $\left\{ {{\bi X}_k = {(X_{1,k},X_{2,k})}^{\top}, k \ge 1} \right\}$ be a sequence of independent and identically distributed random vectors whose components are allowed to be generally dependent with marginal distributions being from the class of extended regular variation, and let $\left\{ {{\brTheta} _k = {(\Theta _{1,k},\Theta _{2,k})}^{\top}, k \ge 1} \right\}$ be a sequence of nonnegative random vectors that is independent of $\left\{ {{\bi X}_k, k \ge 1} \right\}$. Under several mild assumptions, some simple asymptotic formulae of the tail probabilities for the bidimensional randomly weighted sums $\left( {\sum\nolimits_{k = 1}^n {\Theta _{1,k}} X_{1,k},\sum\nolimits_{k = 1}^n {\Theta _{2,k}} X_{2,k}} \right)^{\rm \top }$ and their maxima $({{\max} _{1 \le i \le n}}\sum\nolimits_{k = 1}^i {\Theta _{1,k}} X_{1,k},{{\max} _{1 \le i \le n}}\sum\nolimits_{k = 1}^i {\Theta _{2,k}} X_{2,k})^{\rm \top }$ are established. Moreover, uniformity of the estimate can be achieved under some technical moment conditions on $\left\{ {{\brTheta} _k, k \ge 1} \right\}$. Direct applications of the results to risk analysis are proposed, with two types of ruin probability for a discrete-time bidimensional risk model being evaluated.

scholarly journals Tail Behavior of Randomly Weighted Sums

2012 ◽  
Vol 44 (03) ◽  
pp. 794-814 ◽  
Author(s):  
Rajat Subhra Hazra ◽  
Krishanu Maulik
Keyword(s):  
Mellin Transform ◽  
Sufficient Conditions ◽  
Random Variables ◽  
Independent Random Variables ◽  
Positive Sequence ◽  
Weighted Sums ◽  
Tail Behavior ◽  
Moment Conditions ◽  
Randomly Weighted Sums ◽  
Regularly Varying

Let {X t , t ≥ 1} be a sequence of identically distributed and pairwise asymptotically independent random variables with regularly varying tails, and let {Θ t , t ≥ 1} be a sequence of positive random variables independent of the sequence {X t , t ≥ 1}. We will discuss the tail probabilities and almost-sure convergence of X (∞) = ∑ t=1 ∞Θ t X t + (where X + = max{0, X}) and max1≤k<∞∑ t=1 k Θ t X t , and provide some sufficient conditions motivated by Denisov and Zwart (2007) as alternatives to the usual moment conditions. In particular, we illustrate how the conditions on the slowly varying function involved in the tail probability of X 1 help to control the tail behavior of the randomly weighted sums. Note that, the above results allow us to choose X 1, X 2,… as independent and identically distributed positive random variables. If X 1 has a regularly varying tail of index -α, where α > 0, and if {Θ t , t ≥ 1} is a positive sequence of random variables independent of {X t }, then it is known – which can also be obtained from the sufficient conditions in this article – that, under some appropriate moment conditions on {Θ t , t ≥ 1}, X (∞) = ∑ t=1 ∞Θ t X t converges with probability 1 and has a regularly varying tail of index -α. Motivated by the converse problems in Jacobsen, Mikosch, Rosiński and Samorodnitsky (2009) we ask the question: if X (∞) has a regularly varying tail then does X 1 have a regularly varying tail under some appropriate conditions? We obtain appropriate sufficient moment conditions, including the nonvanishing Mellin transform of ∑ t=1 ∞Θ t along some vertical line in the complex plane, so that the above is true. We also show that the condition on the Mellin transform cannot be dropped.

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