英文标题：
《A Noisy Principal Component Analysis for Forward Rate Curves》
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作者：
Marcio Laurini and Alberto Ohashi
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最新提交年份：
2014
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英文摘要：
  Principal Component Analysis (PCA) is the most common nonparametric method for estimating the volatility structure of Gaussian interest rate models. One major difficulty in the estimation of these models is the fact that forward rate curves are not directly observable from the market so that non-trivial observational errors arise in any statistical analysis. In this work, we point out that the classical PCA analysis is not suitable for estimating factors of forward rate curves due to the presence of measurement errors induced by market microstructure effects and numerical interpolation. Our analysis indicates that the PCA based on the long-run covariance matrix is capable to extract the true covariance structure of the forward rate curves in the presence of observational errors. Moreover, it provides a significant reduction in the pricing errors due to noisy data typically founded in forward rate curves.
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中文摘要：
主成分分析（PCA）是估计高斯利率模型波动结构最常用的非参数方法。估计这些模型的一个主要困难是，远期利率曲线无法从市场上直接观察到，因此在任何统计分析中都会出现非平凡的观察误差。在这项工作中，我们指出，由于存在由市场微观结构效应和数值插值引起的测量误差，经典的PCA分析不适合估计远期利率曲线的因素。我们的分析表明，基于长期协方差矩阵的主成分分析能够在存在观测误差的情况下提取前向速率曲线的真实协方差结构。此外，由于通常在远期利率曲线中发现的噪声数据，它显著降低了定价误差。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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