英文标题：
《Modeling joint probability distribution of yield curve parameters》
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作者：
Jarek Duda, Ma{\\l}gorzata Snarska
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最新提交年份：
2018
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英文摘要：
  US Yield curve has recently collapsed to its most flattened level since subprime crisis and is close to the inversion. This fact has gathered attention of investors around the world and revived the discussion of proper modeling and forecasting yield curve, since changes in interest rate structure are believed to represent investors expectations about the future state of economy and have foreshadowed recessions in the United States. While changes in term structure of interest rates are relatively easy to interpret they are however very difficult to model and forecast due to no proper economic theory underlying such events. Yield curves are usually represented by multivariate sparse time series, at any point in time infinite dimensional curve is portrayed via relatively few points in a multivariate space of data and as a consequence multimodal statistical dependencies behind these curves are relatively hard to extract and forecast via typical multivariate statistical methods.We propose to model yield curves via reconstruction of joint probability distribution of parameters in functional space as a high degree polynomial. Thanks to adoption of an orthonormal basis, the MSE estimation of coefficients of a given function is an average over a data sample in the space of functions. Since such polynomial coefficients are independent and have cumulant-like interpretation ie.describe corresponding perturbation from an uniform joint distribution, our approach can also be extended to any d-dimensional space of yield curve parameters (also in neighboring times) due to controllable accuracy. We believe that this approach to modeling of local behavior of a sparse multivariate curved time series can complement prediction from standard models like ARIMA, that are using long range dependencies, but provide only inaccurate prediction of probability distribution, often as just Gaussian with constant width.
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中文摘要：
美国收益率曲线最近已跌至次贷危机以来最平坦的水平，接近反转。这一事实引起了世界各地投资者的关注，并重新引发了对正确建模和预测收益率曲线的讨论，因为利率结构的变化被认为代表了投资者对未来经济状况的预期，并预示着美国的衰退。虽然利率期限结构的变化相对容易解释，但由于此类事件背后没有合适的经济理论，因此很难对其进行建模和预测。收益率曲线通常由多元稀疏时间序列表示，在任何时间点，通过多元数据空间中相对较少的点来描绘无限维曲线，因此，这些曲线背后的多模态统计相关性相对难以通过典型的多元统计方法提取和预测。我们建议通过将函数空间中参数的联合概率分布重建为高次多项式来建模屈服曲线。由于采用了正交基，给定函数系数的均方误差估计是函数空间中数据样本的平均值。由于这些多项式系数是独立的，并且具有类似累积量的解释，即描述来自均匀联合分布的相应扰动，由于精度可控，我们的方法也可以扩展到屈服曲线参数的任何d维空间（也在相邻时间）。我们相信，这种对稀疏多元曲线时间序列的局部行为建模的方法可以补充ARIMA等标准模型的预测，这些模型使用长距离依赖关系，但只能提供不准确的概率分布预测，通常就像等宽高斯分布一样。
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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：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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