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Purpose: To investigate the role of domestic and foreign economic policy uncertainty (EPU) in driving the corporate bond yields in emerging markets. Design/methodology/approach: The study utilizes monthly data from January 2008 to June 2023 from the selected emerging economies. The data analysis is conducted using univariate, bivariate and multivariate statistical techniques. The study includes bond market liquidity and global volatility (VIX) as control variables. Findings: Domestic EPU has a significant role in driving corporate bond yields in these markets. The study finds weak evidence to support the role of the USA EPU in influencing corporate bond yields in emerging economies. Domestic EPU holds more weight and influence than the EPU originating from the United States of America. Research limitations/implications: The findings provide useful insights to policymakers about the potential impact of policy uncertainty on corporate bond yields and enable them to make informed decisions regarding economic policies that maintains financial stability. Understanding the relationship between EPU and corporate bond yields enables investors to optimize their investment decisions in emerging market economies, opens the scope for further research on the interaction between EPU and volatility and other attributes of fixed income markets. Originality/value: Focuses specifically on the emerging market economies in Asia, providing an in-depth analysis of the dynamics and challenges faced by these countries, Explores the influence of both domestic and the USA EPU on corporate bond yields in emerging markets, offering valuable insights into the transmission channels and impact of EPU from various sources. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to examine the return sensitivity of public farmland and timberland real estate investment trusts (REITs) to private-equity farmland, timberland and real estate, long-term corporate bonds and large- and small-cap stocks. The study also examines time-dependent contributions of selected asset classes to farmland and timberland REIT volatility. Design/methodology/approach: The authors use a multi-factor asset pricing model under a seemingly unrelated regression framework to evaluate farmland and timberland REIT returns, and a state-space model with the Kalman filter to evaluate the time-dependent contributors of farmland and timberland REIT volatility. The authors first perform orthogonalized regressions to obtain pure independent factors, and then decompose volatility into individual asset components. Findings: Significant loadings on financial assets are found for both farmland and timberland REITs, suggesting that they are generally driven by some common state variables. Large-cap stocks are found to be the major contributor of farmland and timberland REIT volatility, despite some differing patterns over time. Originality/value: Empirical analysis of farmland REIT is very scarce. The authors compare the risk-return characteristics of farmland and timberland REITs under a state-space framework with the Kalman filter. This study can improve the understanding of the roles of farmland and timberland REITs in a multi-asset portfolio. [ABSTRACT FROM AUTHOR]

This study introduces two Markov chain time approaches, time-homogeneous and nonhomogeneous models, for analyzing farm credit risk migration as alternatives to the traditional discrete-time (cohort) method. The Markov chain models are found to produce more accurate, reliable transition probability rates using the 3 x I migration measurement method used by farm lenders. Compared to corporate bond ratings migration results, this study obtained larger mean differences in singular value decomposition between the cohort matrix and each of the Markov chain matrices. This finding suggests that the omission of transient, indirect migration activities under the cohort method is more costly when applied to farm credit analysis. This discrepancy could lead to understated transition probability estimates which, in turn, could produce misleading indicators of farm loan portfolio quality. [ABSTRACT FROM AUTHOR]