Do local and global factors impact the emerging markets' sovereign yield curves? Evidence from a data-rich environment

This paper investigates the relation between yield curve and macroeconomic factors for ten 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 VAR analysis based on the system 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, 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.PostprintPeer reviewe

Do local and global factors impact the emerging markets' sovereign yield curves? Evidence from a data-rich environment

This paper investigates the relation between yield curve and macroeconomic factors for ten 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 VAR analysis based on the system 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, 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

Do local and global factors impact the emerging markets' sovereign yield curves? Evidence from a data-rich environment

This paper investigates the relation between yield curve and macroeconomic factors for ten 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 VAR analysis based on the system 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, 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