Natural Gas Price Impact on the Euro Area Sovereign Bonds - The feasibility of a simplistic approach
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- This master’s thesis presents a comprehensive analysis of the relationship between the Title Transfer Facility (TTF) natural gas contract price and sovereign bond yields using some simplistic methods. In fact, the relationship between commodity prices and sovereign bond yields has become a topic of interest due to its potential implications for financial markets and the macroeconomic landscape. This dissertation investigates the impact of natural gas prices on sovereign bond yields in the European Union (EU) and analyzes their evolving relationship. The introduction provides an overview of the factors influencing sovereign bond yields, and the complexity of the natural gas market in the EU, emphasizing the importance of understanding the expected behavior of bond yields in response to changes in natural gas prices. The key research aims to fill the gap in understanding the specific relationship between natural gas prices and sovereign bond yields, providing a simple and comprehensive understanding of this contemporary and current topic. The selected countries—Germany, Italy, Spain, Hungary, and France—offer insights into the varying degrees of dependence in natural gas, specially of Russian origin, diversification needs, and the influence of energy mixes, providing a comprehensive analysis of natural gas dynamics in Europe. The analysis is divided into four sections: Ordinary Least Squares (OLS) regression, Least Absolute Selection and Shrinkage Operator (LASSO) regression, Shapley Values, and a periodical analysis. The results obtained from these methods provide an initial evidence of the existing relationship between the TTF variable and bond yields. The OLS regression results reveal a significant association between the TTF variable and changes in yields, particularly in the longer term. The LASSO regression further validates these findings, demonstrating the importance of the TTF variable in explaining bond yield variations. The Shapley Values analysis quantitatively assesses the variable contributions, consistently highlighting the impact of the TTF variable on yields, albeit with some variations across countries and time periods. The periodical analysis reveals shifts in the average contribution of the TTF variable during different periods, further emphasizing its relevance. However, the validation and discussion of the applied models highlight challenges associated with the underlying assumptions and limitations of the statistical methods. Violations of the OLS assumptions, such as autocorrelation, heteroskedasticity, and non-normality in the residuals, compromise the accuracy and reliability of the estimated coefficients. The presence of mis-specification and possible omitted variable bias raises concerns about the interpretation and robustness of the results. In fact the reliance on machine learning techniques, including LASSO and Shapley Values, presents both advantages and limitations. Biased variable selection and the assumption of linearity in these methods can affect the interpretation of the results. Additionally, the non-linear relationship between the explanatory and dependent variables, as well as the reactive and memory-based nature of the data, pose challenges to model specification. In conclusion, while the statistical methods applied provide some insights into the relationship between the TTF variable and bond yields, careful consideration of the underlying assumptions, model validation, and alternative regression techniques is essential. The limitations of machine learning techniques emphasize the need for rigorous statistical review and validation to ensure accurate interpretation of the results. The complex data characteristics require meticulous model specification and further investigation of alternative methodologies.