英文标题：
《Forecasting interest rates through Vasicek and CIR models: a
  partitioning approach》
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作者：
Giuseppe Orlando, Rosa Maria Mininni and Michele Bufalo
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最新提交年份：
2019
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英文摘要：
  The aim of this paper is to propose a new methodology that allows forecasting, through Vasicek and CIR models, of future expected interest rates (for each maturity) based on rolling windows from observed financial market data. The novelty, apart from the use of those models not for pricing but for forecasting the expected rates at a given maturity, consists in an appropriate partitioning of the data sample. This allows capturing all the statistically significant time changes in volatility of interest rates, thus giving an account of jumps in market dynamics. The performance of the new approach is carried out for different term structures and is tested for both models. It is shown how the proposed methodology overcomes both the usual challenges (e.g. simulating regime switching, volatility clustering, skewed tails, etc.) as well as the new ones added by the current market environment characterized by low to negative interest rates.
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中文摘要：
本文的目的是提出一种新的方法，允许通过Vasicek和CIR模型，根据观察到的金融市场数据的滚动窗口预测未来预期利率（每个到期日）。除了使用这些模型不用于定价，而是用于预测给定到期日的预期利率之外，其新颖之处在于对数据样本进行了适当的划分。这允许捕捉利率波动的所有统计上显著的时间变化，从而说明市场动态的跳跃。对不同的期限结构进行了新方法的性能测试，并对两种模型进行了测试。本文展示了所提出的方法是如何克服通常的挑战（例如模拟制度转换、波动性聚类、斜尾等）以及当前以低至负利率为特征的市场环境所增加的新挑战的。
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分类信息：

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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