The yield curve is a fundamental input parameter of valuation theories in capital markets. Information about yields can be observed in a discrete form, either directly through traded yield instruments (eg., interest rate swaps) or indirectly through the prices of bonds (eg., government bonds). Capital markets usually create benchmark yield curves for specific and very liquid market instruments, or for issuers where many different quotes of individual yield information for specific maturities are observable. The standard methods to construct a continuous yield curve from discrete observable yield data quotes are the fit of a mathematical model function, interpolation or regression algorithms. This paper expands these standard methods to include artificial intelligence algorithms, which have the advantage of avoiding any assumptions with regard to the mathematical model functions of the yield curve, and which can conceptually adapt easily to any market changes. Nowadays, the most widely used risk-free yield curve in capital markets is the overnight index swap (OIS) curve, which is derived from observable OISs and is used in this paper as the benchmark curve to derive and compare different yield curve fits.

中文翻译：

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用人工智能拟合收益率曲线：标准拟合方法与人工智能算法的比较

收益率曲线是资本市场估值理论的基本输入参数。有关收益率的信息可以以离散形式观察到，可以直接通过交易的收益率工具（例如利率掉期）或间接通过债券价格（例如政府债券）进行观察。资本市场通常会为特定且流动性很强的市场工具创建基准收益率曲线，或者为发行人创建基准收益率曲线，其中特定期限的个人收益率信息的许多不同报价是可观察的。从离散的可观察收益率数据报价构建连续收益率曲线的标准方法是数学模型函数、插值或回归算法的拟合。本文将这些标准方法扩展到包括人工智能算法，其优点是避免对收益率曲线的数学模型函数进行任何假设，并且在概念上可以轻松适应任何市场变化。目前，资本市场中使用最广泛的无风险收益率曲线是隔夜指数掉期（OIS）曲线，它是从可观察的 OIS 中衍生出来的，在本文中用作基准曲线来推导和比较不同的收益率曲线拟合。