Luciano Vereda

Yield Curve Forecasts and the Predictive Power of Macro Variables in a VAR Framework

Recent macro-finance papers have documented the importance of adding information from macro variables in order to improve out-of-sample forecasting performance of bond yields. This paper aims at investigating the reasons for this success. We use Diebold and Li’s dynamic version of the Nelson and Siegel exponential approximation of the yield curve to estimate the factors that govern its dynamics. Factors and macro variables are modeled simultaneously in a VAR framework, which is then used to forecast the factors. Our main conclusions are (i) this framework is useful in forecasting slope and curvature factors, but not the level factor; and (ii) to get good results in forecasting the level factor, one needs a macro model which incorporates variables related to long-run trends and expectations.

Flight to Quality in a Hierarchical Factor Model

We use a hierarchical factor model to study the behavior of yield curves derived from the market prices of corporate bonds issued by industrial companies and traded in the U.S. market. The sample is comprised by curves of different risk levels, which allows identifying four global forces and interpreting some of them as manifestations of flight to quality. The main contributions of this paper are detecting movements that can be assigned to flight to quality through a hierarchical factor model, an econometric tool not yet used in empirical studies about the phenomenon, and adding new insights to the literature about the driving factors of corporate curves.