The Dynamics of Economic Functions: Modeling and Forecasting the Yield Curve
The Dynamics of Economic Functions: Modeling and Forecasting the Yield Curve
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DOI：
10.1198/016214508000000922
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发表时间：
2008-03
影响因子：
3.7
通讯作者：
Clive G. Bowsher;R. Meeks
中科院分区：
文献类型：
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作者：
Clive G. Bowsher;R. Meeks
介绍了函数信号加噪声（FSN）模型，为经济函数时间序列的建模和预测提供了一种新的、通用的方法。基本的、连续的经济函数（或“信号”）是一个自然的三次样条，其动态演变是由样条节点处的坐标（或“y值”）的协整向量自回归驱动的。自然三次样条提供灵活的横截面拟合，并导致线性状态空间模型。该FSN模型实现了降维，提供了一个连贯的描述所观察到的收益率曲线及其动态的横截面尺寸N变大，并可以可行地估计和用于预测时，N是大的。模型的整合和协整性质的推导。然后，FSN模型被应用于预测36维收益率曲线的美国国债在1个月的未来地平线。该方法始终优于动态Nelson-Siegel和随机游走预测的基础上，均方预测误差标准和经济相关的损失函数来自对交易算法的实现利润。分析还强调了在一个具体的设置的危险，试图推断模型预测的相对经济价值的基础上，其相关的均方预测误差。
The class of functional signal plus noise (FSN) models is introduced that provides a new, general method for modeling and forecasting time series of economic functions. The underlying, continuous economic function (or “signal”) is a natural cubic spline whose dynamic evolution is driven by a cointegrated vector autoregression for the ordinates (or “y-values”) at the knots of the spline. The natural cubic spline provides flexible cross-sectional fit and results in a linear state-space model. This FSN model achieves dimension reduction, provides a coherent description of the observed yield curve and its dynamics as the cross-sectional dimension N becomes large, and can be feasibly estimated and used for forecasting when N is large. The integration and cointegration properties of the model are derived. The FSN models are then applied to forecasting 36-dimensional yield curves for U.S. Treasury bonds at the 1-month-ahead horizon. The method consistently outperforms the dynamic Nelson–Siegel and random walk forecasts on the basis of both mean squared forecast error criteria and economically relevant loss functions derived from the realized profits of pairs trading algorithms. The analysis also highlights in a concrete setting the dangers of attempting to infer the relative economic value of model forecasts on the basis of their associated mean squared forecast errors.