Working Paper: CEPR ID: DP6206
Authors: Carlo A. Favero; Linlin Niu; Luca Sala
Abstract: 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.
Keywords: factor models; forecasting; large data set; term structure of interest rates; yield curve
JEL Codes: C33; C53; E43; E44
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| macro factors (E66) | forecasting performance (medium to long horizons) (C53) |
| financial factors (G29) | forecasting performance (short-run) (C53) |
| no-arbitrage models + macro data (E19) | forecasting performance (C53) |
| time-varying risk prices (G19) | forecasting performance (medium horizon) (C53) |
| time-varying risk prices (G19) | forecasting performance (short horizon) (C53) |
| complexity of no-arbitrage models (G19) | limitation in causal inference (C32) |