An analysis of the relationship between real yields and gold prices : a pre and post Covid analysis

Abstract

This dissertation examines the relationship between real rates—measured via U.S. 10-year Treasury Inflation-Protected Securities (TIPS) —and gold prices, comparing the pre-COVID (2010–2019) and post-COVID (2020–2024) periods. Monthly data was collected from Bloomberg. The DXY index – which tracks the strength of the dollar against a basket of other major currencies – was used to control for any gold movement which was explained by currency changes. This helps disentangle the effects of these two important variables on the price of gold. A Vector Autoregressive (VAR) model was employed to analyse gold’s relationship with real rates, in line with Lis Andriani, Fajrin Satria Dwi Kesumah and Luthfi Firdaus in their paper, Causality test on gold prices and economic risk(November2023). Impulse Response Functions were used to assess the dynamic relationships amongst variables. The analysis also contributes to the literature by assessing European real rates. Several robustness checks were carried out to support the empirical results found in the main regression. The pre-COVID results show a strong and statistically significant inverse link between 10-year TIPS yields and gold prices. This is in line with our apriori expectation and consistent with the literature. The post-COVID results suggests a non-significant relationship between real rates and gold. The study highlights that post-Covid gold prices might be driven by central bank buying, geopolitical risks and safe-haven demand as per the Financial Stability Review May 2025 by the ECB. The diminished explanatory power of traditional variables suggests that relationships have become more state-dependent in the post-pandemic period. Findings have implications for central banks, portfolio managers, and policymakers navigating an environment where traditional macro-financial linkages are less stable. The weakening of traditional macro-financial linkages has profound implications for central banks, portfolio managers, and policymakers. For central banks, the transmission of monetary policy becomes increasingly uncertain. Conventional models that rely on stable relationships between interest rates, credit growth, and asset prices may no longer provide reliable guidance (Blanchard, 2019). This necessitates a recalibration of policy frameworks, with greater emphasis on forward-looking indicators such as market-based measures of risk premia and volatility, rather than historical correlations alone (Borio, 2021). Portfolio managers similarly face challenges, as the breakdown of established cross-asset correlations undermines the effectiveness of conventional diversification strategies. In environments of heightened instability, safe-haven assets may not perform as expected, and traditional hedging mechanisms may fail (Ilmanen, 2012). Consequently, asset managers are compelled to adopt more dynamic allocation strategies, incorporating scenario analysis and stress testing to account for nonlinear risks and regime shifts (Ang & Chen, 2010). For broader economic policymakers, the erosion of predictable linkages complicates the design and execution of both fiscal and regulatory interventions. For example, fiscal stimulus may fail to generate the anticipated credit expansion if financial institutions reassess risk exposures independently of macroeconomic fundamentals (Reinhart & Rogoff, 2011). This environment reinforces the need for macroprudential policies and closer coordination between monetary, fiscal, and regulatory authorities to ensure systemic stability (Claessens et al., 2014). Taken together, these developments underscore the imperative for policymakers and market participants to transition from reliance on static, model-driven approaches toward adaptive frameworks capable of responding to evolving market dynamics and structural uncertainty.