A widespread approach to modelling the interaction between macroeconomic variables and the yield curve relies on three latent factors usually interpreted as the level, slope, and curvature (Diebold et al., 2006). This approach is inherently focused on the conditional mean of the yields and postulates a dynamic linear model where the latent factors smoothly change over time. However, periods of deep crisis, such as the Great Recession and the recent pandemic, have highlighted the importance of statistical models that account for asymmetric shocks and are able to forecast the tails of a variable's distribution. A new version of the dynamic three-factor model is proposed to address this issue based on quantile regressions. The novel approach leverages the potential of quantile regression to model the entire (conditional) distribution of the yields instead of restricting to its mean. An application to US data from the 1970s shows the significant heterogeneity of the interactions between financial and macroeconomic variables across different quantiles. Moreover, an out-of-sample forecasting exercise showcases the proposed method's advantages in predicting the yield distribution tails compared to the standard conditional mean model. Finally, by inspecting the posterior distribution of the three factors during the recent major crises, new evidence is found that supports the greater and longer-lasting negative impact of the great recession on the yields compared to the COVID-19 pandemic.

翻译：一种广泛用于建模宏观经济变量与收益率曲线之间关系的方法，依赖于通常解释为水平、斜率和曲率的三个潜因子（Diebold等，2006）。该方法本质上侧重于收益率的条件均值，并假设一个潜因子随时间平滑变化的动态线性模型。然而，深度危机时期（如大衰退和近期疫情）凸显了考虑非对称冲击并能预测变量分布尾部的统计模型的重要性。为解决这一问题，本文提出了一种基于分位数回归的动态三因子模型新版本。该新方法利用分位数回归的潜力，将建模范围从收益率均值扩展到整个（条件）分布。基于1970年代美国数据的实证分析显示，金融变量与宏观经济变量在不同分位数上的交互作用存在显著异质性。此外，样本外预测实验表明，与标准的条件均值模型相比，所提方法在预测收益率分布尾部方面具有优势。最后，通过考察近期重大危机期间三个因子的后验分布，本文发现了新证据，表明大衰退对收益率的负面影响比COVID-19疫情更大且更持久。