Inf-convolution of G-expectations
Xuepeng Bai, Rainer Buckdahn
TL;DR
This paper explores how G-expectations can be used to model optimal risk transfer problems and investigates the relationship between the inf-convolution of G-expectations and their generating drivers.
Contribution
It establishes a connection between the inf-convolution of G-expectations and the inf-convolution of their drivers G, advancing the understanding of risk measures in uncertain environments.
Findings
Characterizes the inf-convolution of G-expectations
Links the inf-convolution of G-expectations to the inf-convolution of drivers G
Provides insights into risk transfer under G-expectation frameworks
Abstract
In this paper we will discuss the optimal risk transfer problems when risk measures are generated by G-expectations, and we present the relationship between inf-convolution of G-expectations and the inf-convolution of drivers G.
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Taxonomy
TopicsRisk and Portfolio Optimization · Stochastic processes and financial applications · Probability and Risk Models