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arXiv 2008.01241q-fin.MF
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粗糙波动率下带倒向随机偏微分方程的期权定价

Pricing Options Under Rough Volatility with Backward SPDEs

Christian Bayer, Jinniao Qiu, Yao Yao

摘要:

本文研究粗糙波动率模型下的期权定价问题。由于该框架是非马尔可夫的,欧式期权的价值函数不是确定性的,而是随机的,并满足一个倒向随机偏微分方程(BSPDE)。证明了具有无界随机前导系数的一般非线性倒向随机微分方程弱解的存在唯一性,并导出了其与某些正向-倒向随机微分方程的关系.这些BSPDE然后被用来近似美式期权的价格。一个基于深度学习的方法也研究了这种BSPDE和相关的非马尔可夫定价问题的数值逼近。最后,对欧式期权和美式期权的粗糙Bergomi型期权进行了数值计算.

英文摘要:

In this paper, we study the option pricing problems for rough volatility models. As the framework is non-Markovian, the value function for a European option is not deterministic; rather, it is random and satisfies a backward stochastic partial differential equation (BSPDE). The existence and uniqueness of weak solution is proved for general nonlinear BSPDEs with unbounded random leading coefficients whose connections with certain forward-backward stochastic differential equations are derived as well. These BSPDEs are then used to approximate American option prices. A deep leaning-based method is also investigated for the numerical approximations to such BSPDEs and associated non-Markovian pricing problems. Finally, the examples of rough Bergomi type are numerically computed for both European and American options.

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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