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arXiv 2409.00535q-fin.MF

基于G-BSDE的稳健定价核长期分解方法

A G-BSDE approach to the long-term decomposition of robust pricing kernels

Jaehyun Kim, Hyungbin Park

AI总结:

本文在G-期望框架下提出基于三类二次G-BSDE的定价核长期分解方法,将定价核唯一分解为指数贴现、暂时性、对称G-鞅和波动率不确定性递减四个分量,并用二阶PDE表示。

摘要:

本研究在G-期望框架下提出一种用于定价核长期分解的BSDE方法。我们建立了三类二次G-BSDE解的存在性、唯一性和正则性:有限时域G-BSDE、无限时域G-BSDE和遍历G-BSDE。此外,我们探讨了与这三类二次G-BSDE相关的Feynman--Kac公式。利用这些结果,定价核被唯一分解为四个组成部分:指数贴现分量、暂时性分量、对称G-鞅,以及一个刻画G-布朗运动波动率不确定性的递减分量。进一步地,这些分量通过一个二阶偏微分方程的解来表示。本研究将先前在单一固定概率框架下得到的结论推广到G-期望情境中。

英文摘要:

This study proposes a BSDE approach to the long-term decomposition of pricing kernels under the G-expectation framework. We establish the existence, uniqueness, and regularity of solutions to three types of quadratic G-BSDEs: finite-horizon G-BSDEs, infinite-horizon G-BSDEs, and ergodic G-BSDEs. Moreover, we explore the Feynman--Kac formula associated with these three types of quadratic G-BSDEs. Using these results, a pricing kernel is uniquely decomposed into four components: an exponential discounting component, a transitory component, a symmetric G-martingale, and a decreasing component that captures the volatility uncertainty of the G-Brownian motion. Furthermore, these components are represented through the solution to a second-order PDE. This study extends previous findings obtained under a single fixed probability framework to the G-expectation context.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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