A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables

Abstract

We describe the joint dynamics of bond yields and macroeconomic variables in a Vector Autoregression, where identifying restrictions are based on the absence of arbitrage. Using a term structure model with inflation and economic growth factors, together with latent variables, we investigate how macro variables affect bond prices and the dynamics of the yield curve. We find that the forecasting performance of a VAR improves when no-arbitrage restrictions are imposed and that models with macro factors forecast better than models with only unobservable factors.Variance decompositions show that macro factors explain up to 85% of the variation in bond yields. Macro factors primarily explain movements at the short end and middle of the yield curve while unobservable factors still account for most of the movement at the long end of the yield curve.

Macroeconomic shocks and the forward yield curve: how important is monetary policy?

“…Nonetheless, the emphasis of the exercise changes depending on the side of the literature addressed: financial or macro. Papers more on the financial side (Ang and Piazzesi 2003;Diebold, Rudebusch, and Borağan Aruoba 2006;Afonso and Martins 2012) make a more rigorous treatment of the yield curve, but their analysis based on the recursive identification of shocks complicates generalizations in terms of interpretable economic events. Papers in the macro tradition identify shocks more properly but usually refer to simple yields to maturity (Smets and Tsatsaronis 1997;Evans and Marshal 2007;Canova 2005).…”

Financial Big Data Solutions for State Space Panel Regression in Interest Rate Dynamics

“…A common practice in yield curve modelling is to use principal component analysis in order to reduce the dimensionality of the data and obtain features that explain the highest portion of the variance. Beginning with the study of Ang and Piazzesi (2003) or Emanuel Moench (2008), the authors used reduced information from inflation-linked and real economic activity-linked sets of U.S. financial markets by extracting the first principal component from each group of datasets and then adopting the Factor-Augmented Vector Autoregressive (FAVAR) model to calibrate the U.S. Treasury yield. The factor-augmented regression with static factors obtained by principal component analysis was introduced in Ludvigson and Ng (2010) to study the relation between bond excess returns and the macroeconomy.…”

Identification and Estimation of Gaussian Affine Term Structure Models with Regime Switching

“…If two different values for the structural parameters imply the identical reduced-form parameters, there is no way to use observable data to choose between the two. Based on this idea, [8] demonstrates that [4] [11] and [12] are in fact unidentified. We now explore the implications of this fact for [1] described in the previous section.…”

“…In [8], they have proposed that for any 3 × 3 real-valued matrix: 11 [8], this form cannot be extended to higher dimension, it has an advantage over others in that it can deal with the situation of Q κ having complex eigenvalues. This form is enough for us to …”