Term Structure Forecasting: No-arbitrage Restrictions vs. Large Information Set
Carlo Favero (),
Linlin Niu () and
Luca Sala ()
No 318, Working Papers from IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University
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 favorable 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.
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Working Paper: Term Structure Forecasting: No-Arbitrage Restrictions vs Large Information Set (2007)
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