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Article: Asymptotics for joint tail probability of bidimensional randomly weighted sums with applications to insurance

TitleAsymptotics for joint tail probability of bidimensional randomly weighted sums with applications to insurance
Authors
Keywords62E20
62P05
91B05
asymptotic joint tail behavior
dependence
heavy-tailed distribution
insurance risk model
randomly weighted sum
Issue Date23-Aug-2023
PublisherSpringer
Citation
Science China Mathematics, 2023, v. 67, p. 163-186 How to Cite?
Abstract

This paper studies the joint tail behavior of two randomly weighted sums ∑mi=1 ΘiXi and ∑nj=1θjYj for some m, n ∈ ℕ ∪{∞}, in which the primary random variables {Xi;i ∈ ℕ} and {Yi;i ∈ ℕ}, respectively, are real-valued, dependent and heavy-tailed, while the random weights {Θi, θii ∈ ℕ} are nonnegative and arbitrarily dependent, but the three sequences {Xi;i ∈ ℕ}, {Yi;i ∈ ℕ} and {Θi, θi;i ∈ ℕ} are mutually independent. Under two types of weak dependence assumptions on the heavy-tailed primary random variables and some mild moment conditions on the random weights, we establish some (uniformly) asymptotic formulas for the joint tail probability of the two randomly weighted sums, expressing the insensitivity with respect to the underlying weak dependence structures. As applications, we consider both discrete-time and continuous-time insurance risk models, and obtain some asymptotic results for ruin probabilities.


Persistent Identifierhttp://hdl.handle.net/10722/337948
ISSN
2023 Impact Factor: 1.4
2023 SCImago Journal Rankings: 1.060
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYang, Y-
dc.contributor.authorChen, S-
dc.contributor.authorYuen, KC-
dc.date.accessioned2024-03-11T10:25:08Z-
dc.date.available2024-03-11T10:25:08Z-
dc.date.issued2023-08-23-
dc.identifier.citationScience China Mathematics, 2023, v. 67, p. 163-186-
dc.identifier.issn1674-7283-
dc.identifier.urihttp://hdl.handle.net/10722/337948-
dc.description.abstract<p>This paper studies the joint tail behavior of two randomly weighted sums ∑<sup><em>m</em></sup><sub><em>i</em>=1</sub> Θ<sub><em>i</em></sub><em>X</em><sub><em>i</em></sub> and ∑<sup><em>n</em></sup><sub><em>j</em>=1</sub><em>θ</em><sub><em>j</em></sub><em>Y</em><sub><em>j</em></sub> for some <em>m, n</em> ∈ ℕ ∪{∞}, in which the primary random variables {<em>X</em><sub><em>i</em></sub>;<em>i</em> ∈ ℕ} and {<em>Y</em><sub><em>i</em></sub>;<em>i</em> ∈ ℕ}, respectively, are real-valued, dependent and heavy-tailed, while the random weights {Θ<sub><em>i</em></sub>, θ<sub><em>i</em></sub>; <em>i</em> ∈ ℕ} are nonnegative and arbitrarily dependent, but the three sequences {<em>X</em><sub><em>i</em></sub>;<em>i</em> ∈ ℕ}, {<em>Y</em><sub><em>i</em></sub>;<em>i</em> ∈ ℕ} and {Θ<sub><em>i</em></sub>, θ<sub><em>i</em></sub>;<em>i</em> ∈ ℕ} are mutually independent. Under two types of weak dependence assumptions on the heavy-tailed primary random variables and some mild moment conditions on the random weights, we establish some (uniformly) asymptotic formulas for the joint tail probability of the two randomly weighted sums, expressing the insensitivity with respect to the underlying weak dependence structures. As applications, we consider both discrete-time and continuous-time insurance risk models, and obtain some asymptotic results for ruin probabilities.<br></p>-
dc.languageeng-
dc.publisherSpringer-
dc.relation.ispartofScience China Mathematics-
dc.subject62E20-
dc.subject62P05-
dc.subject91B05-
dc.subjectasymptotic joint tail behavior-
dc.subjectdependence-
dc.subjectheavy-tailed distribution-
dc.subjectinsurance risk model-
dc.subjectrandomly weighted sum-
dc.titleAsymptotics for joint tail probability of bidimensional randomly weighted sums with applications to insurance-
dc.typeArticle-
dc.identifier.doi10.1007/s11425-022-2030-5-
dc.identifier.scopuseid_2-s2.0-85168948052-
dc.identifier.volume67-
dc.identifier.spage163-
dc.identifier.epage186-
dc.identifier.eissn1869-1862-
dc.identifier.isiWOS:001064558600003-
dc.identifier.issnl1869-1862-

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