Navigating Uncertainty: The Multi-Method Analysis of Interest Rate Volatility and Portfolio Performance Across Developed and Emerging Markets

Abstract

This study investigates the impact of interest rate volatility on portfolio returns across ten developed and emerging economies United States, United Kingdom, Germany, Japan, India, China, Brazil, South Africa, Mexico, and Turkey over the period 2010–2025. Utilizing a comprehensive multi-method approach, the analysis combines Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), Autoregressive Distributed Lag (ARDL) models, Mixed-Moment Quantile Regression (MMQR), Granger causality, and System Generalized Method of Moments (GMM) to examine both long-run and short-run dynamics, heterogeneous effects across portfolios, predictive relationships, and robustness of findings. The results consistently indicate that higher interest rate volatility negatively affects portfolio returns, with stronger impacts observed in high-return portfolios. Long-run estimations (FMOLS and DOLS) confirm a persistent negative association, while ARDL models reveal both immediate and long-term adverse effects, with the error correction term highlighting a swift adjustment toward equilibrium following shocks. MMQR analysis demonstrates that the sensitivity of portfolios to interest rate volatility varies across the return distribution, emphasizing the heterogeneous nature of investor responses. Granger causality tests establish that interest rate volatility, equity allocation, and market volatility significantly predict portfolio returns, suggesting potential avenues for proactive risk management. System GMM estimations reinforce the robustness of results while addressing endogeneity and dynamic panel concerns. The study contributes to the literature by providing a cross-country, multi-method examination of interest rate volatility and portfolio performance, highlighting the role of portfolio characteristics and macroeconomic factors in mitigating adverse effects. Policy implications include the need for central banks to stabilize interest rate environments and for investors to adopt dynamic asset allocation strategies to manage risk effectively. Limitations include annual frequency data and a limited country sample, pointing to opportunities for future research on intraday dynamics, broader markets, and alternative asset classes.

Received: 2026-03-30 | Revised: 2026-05-25 | Accepted: 2026-05-30 | Published: 2026-06-02

Declarations

Ethics and Guidelines: Not applicable.

Consent to participate: Not applicable.

Consent to publish: The authors have provided consent to publish.

Competing interests: The authors declare no competing interests.

Data availability statement: Data will be made available on reasonable request from the corresponding author.

Funding: This research received no external funding.

Clinical Trial Number: Not Applicable.

Declaration of using generative AI: During the preparation of this work the author(s) used ChatGPT in order to correct the grammatical errors. After using this tool/service, the author(s) reviewed and edited the content as needed and take full responsibility for the content of the published article.

Author Contributions: Conceptualization, H.J.T.; methodology, H.J.T.; formal analysis, H.J.T.; writing—original draft preparation, H.J.T.; writing—review and editing, H.J.T. All authors have read and agreed to the published version of the manuscript.