We use a Bayesian vector autoregression with stochastic volatility to forecast government bond yields. We form the conjugate prior from a no-arbitrage affine term structure model. The model improves on the accuracy of point and density forecasts from a no-change random walk and an affine term structure model with stochastic volatility. Our proposed approach may succeed by relaxing the no-arbitrage affine term structure model's requirements that yields obey a factor structure and that the factors follow a Markov process. In the term structure model, its cross-equation no-arbitrage restrictions on the factor loadings appear to play a marginal role in forecasting gains.

No-arbitrage priors, drifting volatilities, and the term structure of interest rates

Carriero, Andrea;Marcellino, Massimiliano
2021

Abstract

We use a Bayesian vector autoregression with stochastic volatility to forecast government bond yields. We form the conjugate prior from a no-arbitrage affine term structure model. The model improves on the accuracy of point and density forecasts from a no-change random walk and an affine term structure model with stochastic volatility. Our proposed approach may succeed by relaxing the no-arbitrage affine term structure model's requirements that yields obey a factor structure and that the factors follow a Markov process. In the term structure model, its cross-equation no-arbitrage restrictions on the factor loadings appear to play a marginal role in forecasting gains.
2021
2021
Carriero, Andrea; Clark, Todd E.; Marcellino, Massimiliano
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11565/4042720
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