A0853
Title: A data-rich yield curve factor model
Authors: Andrea Trovato - Ca Foscari University (Italy) [presenting]
Roberto Casarin - University Ca' Foscari of Venice (Italy)
Davide Raggi - University of Bologna (Italy)
Abstract: A data-enriched term-structure model for interest rates is presented following a general term-structure architecture based on non-arbitrage arguments. The state-space model includes the traditional Vasicek measurement equation with the spot rate as a state process, which is augmented with further measurement equations on bonds at different maturities. The model incorporates additional exogenous variables related to monetary policy, equity market volatility and macroeconomic fundamentals. All the measurement equations are driven by the state process, and the augmentation allows for an improved estimation of the latent interest rate. A Bayesian inference framework is proposed based on an efficient posterior approximation procedure. An innovative and more interpretable way is presented to estimate the risk premium as a combination of explicit sources of risk. The model is applied to the US yield curve sampled at a monthly frequency from December 2000 up to the end of May 2024. The augmented framework and the Bayesian inference allow for generating projections of the yield curve under different market scenarios. The simulation provides great support to decisions of tactical asset allocation since it allows for evaluating the coherence of the portfolio allocation with the predominant regime implied in the market yield curve.
