Term Structure Forecasting: No-Arbitrage Restrictions vs Large Information Set

This paper addresses the issue of forecasting the term structure. We provide a unified state-space modelling framework that encompasses different existing discrete-time yield curve models. Within such framework we analyze the impact on forecasting performance of two crucial modelling choices, i.e. the imposition of no-arbitrage restrictions and the size of the information set used to extract factors. Using US yield curve data, we find that: a. macro factors are very useful in forecasting at medium/long forecasting horizon; b. financial factors are useful in short run forecasting; c. no-arbitrage models are effective in shrinking the dimensionality of the parameter space and, when supplemented with additional macro information, are very effective in forecasting; d. within no-arbitrage models, assuming time-varying risk price is more favourable than assuming constant risk price for medium horizon-maturity forecast when yield factors dominate the information set, and for short horizon and long maturity forecast when macro factors dominate the information set; e. however, given the complexity and the highly non-linear parameterization of no-arbitrage models, it is very difficult to exploit within this type of models the additional information offered by large macroeconomic datasets.