Gelişmekte olan piyasalarda yerel para cinsinden tahvil risk primleri: İleri düzey makine öğrenmesi tekniklerinden elde edilen içgörüler

Understanding the determinants of local currency bond risk premia is crucial for emerging market investors and policymakers. This study investigates the determinants of local currency bond risk premia in six emerging markets—Brazil, Hungary, Poland, Thailand, South Africa, and Turkey—through the application of advanced machine learning techniques. The analysis first utilizes yield curve-based variables, including forward rates, forward-spot spreads, and term premia. Subsequently, inflation, implied foreign exchange (FX) volatility, and macroeconomic indicators are incorporated to assess their individual and combined effects on predictive accuracy. The results highlight distinct regional patterns in the drivers of excess bond returns. Findings reveal that in Brazil, Hungary, Poland, and Thailand, yield curve-based variables—especially forward rates and term premia—exhibit strong predictive power, while macroeconomic factors and FX volatility offer limited value. In contrast, Turkey and South Africa display a fundamentally different structure, where inflation, macroeconomic indicators, and implied FX volatility serve as the primary predictors, and yield curve variables fail to explain bond risk premia. A diverse set of machine learning algorithms—including linear regression, principal component analysis (PCA), partial least squares (PLS), neural networks, random forests, XGBoost, and extremely randomized trees—were employed. Country-specific analysis reveals that algorithmic performance varies significantly across emerging markets, reflecting distinct economic structures and dominant predictive factors. In Hungary and Brazil, Neural Networks yield the highest predictive accuracy In Turkey, XGBoost delivers optimal results. Poland stands out with OLS + PCA demonstrating the utility of linear dimensionality reduction in a structured market environment. In Thailand, both Neural Networks and PCA-applied models exhibit nearly equivalent performance. For South Africa, the most effective predictions are achieved using an Extremely Randomized Trees model. This research highlights the importance of regional differences in the drivers of local currency bond risk premia and demonstrates the value of combining diverse data sources with advanced machine learning techniques. These findings provide valuable insights for policymakers, investors, and financial institutions seeking to refine risk assessment frameworks and improve strategic decision-making in emerging bond markets.