A No-Arbitrage Vector Autoregression of Term Structure Dynamics with Macroeconomic and Latent Variables
A No-Arbitrage Vector Autoregression of Term Structure Dynamics with Macroeconomic and Latent Variables
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DOI：
10.2139/ssrn.194748
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发表时间：
2000-06
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影响因子：
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通讯作者：
Andrew Ang;Monika Piazzesi
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文献类型：
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作者：
Andrew Ang;Monika Piazzesi
本文描述了债券收益率和宏观经济变量在向量自回归中的联合动态，其中识别限制是基于不存在套利。本文采用包含通货膨胀和经济增长因素的期限结构模型，研究宏观变量如何影响债券价格和收益率曲线的动态。设置适应宏观因素的高阶自回归滞后。宏观变量被传统的不可观察期限结构因素所增强。我们发现，当施加无套利限制时，VAR的预测性能有所提高。包含宏观因素的模型比仅包含不可观察因素的传统期限结构模型的预测效果更好。方差分解表明，宏观因素解释了高达85%的债券收益率变化。宏观因素主要解释了收益率曲线短端和中端的变动，而不可观察因素仍然解释了收益率曲线长端的大部分变动。
This paper describes the joint dynamics of bond yields and macroeconomic variables in a Vector Autoregression, where identifying restrictions are based on the absence of arbitrage. Using a term structure model with inflation and economic growth factors, we investigate how macro variables affect bond prices and the dynamics of the yield curve. The setup accommodates higher order autoregressive lags for the macro factors. The macro variables are augmented by traditional unobserved term structure factors. We find that the forecasting performance of a VAR improves when no-arbitrage restrictions are imposed. Models that incorporate macro factors forecast better than traditional term structure models with only unobservable factors. Variance decompositions show that macro factors explain up to 85% of the variation in bond yields. Macro factors primarily explain movements at the short end and middle of the yield curve while unobservable factors still account for most of the movement at the long end of the yield curve.