This paper investigates the relation between yield curve and macroeconomic factors for 10 emerging sovereign bond markets using the sample from January 2006 to April 2019. To this end, the diffusion indices obtained under four categories (global variables, inflation, domestic financial variables, and economic activity) are incorporated by estimating dynamic panel data regressions together with the yield curve factors. Besides, in order to capture dynamic interaction between yield curve and macroeconomic/financial factors, a panel vector autoregressive (VAR) analysis based on the system generalized method of moments (GMM) approach is utilized. Empirical results suggest that the level factor responds to shocks originated from inflation, domestic financial variables, and global variables. Furthermore, the slope factor is affected by shocks in global variables, and the curvature factor appears to be influenced by domestic financial variables. We also show that macroeconomic/financial factors captures significant predictive information over yield curve factors by running individual country factor-augmented predictive regressions and variable selection algorithms such ridge regression, least absolute shrinkage operator (LASSO), and Elastic Net. Our findings have important implications for policymakers and fund managers by explaining the underlying forces of movements in the yield curve and forecasting accurately dynamics of yield curve factors.

中文翻译：

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本地和全球因素会影响新兴市场的主权收益率曲线吗？来自数据丰富环境的证据

本文以 2006 年 1 月至 2019 年 4 月为样本，研究了 10 个新兴主权债券市场收益率曲线与宏观经济因素的关系。活动）通过估计动态面板数据回归和收益率曲线因素来合并。此外，为了捕捉收益率曲线与宏观经济/金融因素之间的动态相互作用，利用基于系统广义矩量法（GMM）方法的面板向量自回归（VAR）分析。实证结果表明，水平因子对源自通胀、国内金融变量和全球变量的冲击做出反应。此外，斜率因子受全球变量冲击的影响，曲率因子似乎受国内金融变量的影响。我们还表明，宏观经济/金融因素通过运行个别国家因素增强预测回归和变量选择算法（如岭回归、最小绝对收缩算子 (LASSO) 和弹性网络）来捕获对收益率曲线因素的重要预测信息。我们的研究结果通过解释收益率曲线变动的潜在力量和准确预测收益率曲线因素的动态，对政策制定者和基金经理具有重要意义。我们还表明，宏观经济/金融因素通过运行个别国家因素增强预测回归和变量选择算法（如岭回归、最小绝对收缩算子 (LASSO) 和弹性网络）来捕获对收益率曲线因素的重要预测信息。我们的研究结果通过解释收益率曲线变动的潜在力量和准确预测收益率曲线因素的动态，对政策制定者和基金经理具有重要意义。我们还表明，宏观经济/金融因素通过运行个别国家因素增强预测回归和变量选择算法（如岭回归、最小绝对收缩算子 (LASSO) 和弹性网络）来捕获对收益率曲线因素的重要预测信息。我们的研究结果通过解释收益率曲线变动的潜在力量和准确预测收益率曲线因素的动态，对政策制定者和基金经理具有重要意义。