arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.07736q-fin.PRmath.PRq-fin.MFstat.ML
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多个收益率曲线市场的机器学习:高斯仿射框架中的快速校准

Machine learning for multiple yield curve markets: fast calibration in the Gaussian affine framework

Sandrine Gümbel, Thorsten Schmidt

摘要:

校准是一项极具挑战性的任务,特别是在多个收益率曲线市场中。本文是首次尝试研究机器学习技术在这方面应用的机会和挑战。我们采用高斯过程回归,一种与扩展卡尔曼滤波有许多相似之处的机器学习方法-一种已多次应用于利率市场和期限结构模型的技术。在Vasicek框架中,我们发现单曲线市场的结果非常好,而多曲线市场面临许多挑战。高斯过程回归是用Adam优化器和非线性共轭梯度法实现的,其中后者表现最好。我们还指出了未来的研究。

英文摘要:

Calibration is a highly challenging task, in particular in multiple yield curve markets. This paper is a first attempt to study the chances and challenges of the application of machine learning techniques for this. We employ Gaussian process regression, a machine learning methodology having many similarities with extended Kalman filtering - a technique which has been applied many times to interest rate markets and term structure models. We find very good results for the single curve markets and many challenges for the multi curve markets in a Vasicek framework. The Gaussian process regression is implemented with the Adam optimizer and the non-linear conjugate gradient method, where the latter performs best. We also point towards future research.

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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