论文标题
多收益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
论文作者
论文摘要
校准是一项高度挑战的任务，尤其是在多个产量曲线市场中。本文是研究机器学习技术在此应用的机会和挑战的首次尝试。我们采用高斯流程回归，这是一种机器学习方法，具有许多相似之处与扩展的卡尔曼过滤 - 这项技术已多次应用于利率市场和期限结构模型。 我们在Vasicek框架中发现了单曲线市场的非常好的结果以及多曲线市场的许多挑战。高斯过程回归是通过ADAM Optimizer和非线性共轭梯度方法实现的，后者表现最好。我们还指向未来的研究。
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.
