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Title: Macro-yields modelling in the presence of asymmetrically distributed interest rates Authors:  Jingwen Shi - University of Warwick (United Kingdom) [presenting]
Abstract: The potential of the copula framework to handle non-Gaussianity in the context of macro-finance term structure modelling and forecasting is studied. The proposed non-Gaussian macro-yields model accounts for the asymmetry and tailedness in yield distributions through non-parametric marginal densities, as well as explicitly addressing the cross-sectional and serial dependence via the state-space inversion copula, and in doing so, retains a latent dynamic factor structure amenable to efficient implementation. Regardless of the maturities and forecast horizons, exploiting the informational content of macroeconomic data in a non-Gaussian setting improves both in-sample and out-of-sample forecasting performance relative to the Gaussian macro-yields model over the 1970:M12016:M12 period. Furthermore, the non-Gaussian macro-yields model demonstrates overwhelming superiority in predicting excess bond returns over the prominent macro-financial predictors and the expectations hypothesis. It also compares favourably with the random walk in forecasting the yield curve over medium- to long-term horizons. Lastly, this copula-based approach affords a technically convenient means of accommodating high-dimensional macroeconomic datasets.