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首页> 外文期刊>Journal of banking & finance >The Role Of No-arbitrage On Forecasting: Lessons From A Parametric Term Structure Model
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The Role Of No-arbitrage On Forecasting: Lessons From A Parametric Term Structure Model

机译：无套利在预测中的作用：参数期限结构模型的教训

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摘要

Parametric term structure models have been successfully applied to numerous problems in fixed income markets, including pricing, hedging, managing risk, as well as to the study of monetary policy implications. In turn, dynamic term structure models, equipped with stronger economic structure, have been mainly adopted to price derivatives and explain empirical stylized facts. In this paper, we combine flavors of those two classes of models to test whether no-arbitrage affects forecasting. We construct cross-sectional (allowing arbitrages) and arbitrage-free versions of a parametric polynomial model to analyze how well they predict out-of-sample interest rates. Based on US Treasury yield data, we find that no-arbitrage restrictions significantly improve forecasts. Arbitrage-free versions achieve overall smaller biases and root mean square errors for most maturities and forecasting horizons. Furthermore, a decomposition of forecasts into forward-rates and holding return premia indicates that the superior performance of no-arbitrage versions is due to a better identification of bond risk premium.
机译：参数期限结构模型已成功应用于固定收益市场中的许多问题，包括定价，对冲，风险管理以及货币政策影响研究。反过来，具有较强经济结构的动态期限结构模型已主要用于定价衍生产品并解释经验化的事实。在本文中，我们结合了这两类模型的特征来测试无套利是否会影响预测。我们构造参数多项式模型的横截面（允许套利）和无套利版本，以分析它们对样本外利率的预测能力。根据美国国债收益率数据，我们发现无套利限制可显着改善预测。对于大多数到期日和预测水平，无套利版本总体上具有较小的偏差和均方根误差。此外，将预测分解为远期利率和持有收益溢价的现象表明，无套利版本的出色表现是由于更好地确定了债券风险溢价。

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