JOINT TAIL OF WEIGHTED SUMS UMDER GENERALIZED QUASI ASYMTOTIC IN DEPENDENCE
Keywords:
Joint tail probability, Randomly weighted sums, Generalized quasi-asymptotic independence, Heavy-tailed distributions, Two-dimensional regular variation, Dependence structure, Asymptotic behavior, Ruin probability, Risk assessment, Insurance mathematicsAbstract
This paper investigates the joint tail behavior of randomly weighted sums under a generalized quasi-asymptotic dependence framework. The main focus is on deriving precise asymptotic expressions for joint tail probabilities, which are crucial in understanding the extremal dependence structure of such sums. By utilizing advanced probabilistic techniques and regular variation theory, we establish conditions under which the joint tail exhibits specific asymptotic properties. Our findings contribute to the broader understanding of heavy-tailed distributions and their applications in fields such as finance, insurance, and telecommunications.
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