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Forecasting the yield curve in a data-rich environment: A no-arbitrage factor-augmented VAR approach

机译:在数据丰富的环境中预测收益率曲线:无套利因子增强的VAR方法

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摘要

This paper suggests a term structure model which parsimoniously exploits a broad macroeconomic information set. The model uses the short rate and the common components of a large number of macroeconomic variables as factors. Precisely, the dynamics ofthe short rate are modeled with a Factor-Augmented Vector Autoregression and the term structure is derived using parameter restrictions implied by no-arbitrage. The model has economic appeal and provides better out-of-sample yield forecasts at intermediate and long horizons than a number of previously suggested approaches. The forecast improvement is highly significant and particularly pronounced for short and medium-term maturities.
机译:本文提出了一种术语结构模型,该模型同时利用了广泛的宏观经济信息集。该模型将短期利率和大量宏观经济变量的共同组成部分作为因素。精确地,用因子增强矢量自回归对短期利率的动力学建模,并使用无套利隐含的参数限制来推导期限结构。与许多先前建议的方法相比,该模型具有经济吸引力,并且在中长期内提供了更好的样本外产量预测。预测的改进非常重要,特别是对于短期和中期到期而言。

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