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 encom-
passes 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 restric-
tions 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 effec-
tive 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 favorable than assuming constant risk price for medium
horizon-maturity forecast when yield factors dominate the informa-
tion set, and for short horizon and long maturity forecast when macro
factors dominate the information set; e. however, given the complex-
ity 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.