Dynamic Return and Volatility Connectedness across Global Markets: A Time - Frequency Analysis
This paper examines return and volatility connectedness across eight global markets (Brent crude oil, gold, Bitcoin, the S&P 500, MSCI World, MSCI Emerging Markets, the US dollar index, and the US 10-year Treasury yield), using daily data from January 2016 to March 2026. A VAR-based connectedness framework is complemented by rolling-window estimation, frequency-domain decomposition, and a time-varying parameter VAR to capture the dynamics of cross-market spillovers. The equity subsystem is also analysed to identify intra-group transmission patterns.
Average return connectedness reaches 38.22% in the static model and approximately 44% in rolling estimations, peaking near 60% during periods of market stress. Equity markets, particularly MSCI World and the S&P 500, act as the main shock transmitters, while Bitcoin, crude oil, the US dollar index, and Treasury yields primarily absorb shocks. Frequency decomposition indicates that short-term spillovers (21.3%) exceed long-term spillovers (17.0%), highlighting the importance of investor sentiment and rapid repricing. Volatility connectedness is higher, at 44.33% in the static estimation, rising to a rolling-window average of 40.28% (range: 27.43%–87.47%). Frequency decomposition shows the opposite pattern to returns: volatility spillovers are predominantly long-term (35.77% versus 8.67% short-term). Bitcoin is predominantly a net receiver in both return and volatility spillovers. Within the equity subsystem, connectedness rises to 58.86%, suggesting limited diversification benefits during turbulent periods. Overall, the findings identify equity markets as the central channel of global financial spillovers and underline the diverging, horizon-specific nature of cross-market risk transmission across the return and volatility channels.
Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0, which permits any further distribution in any medium, provided the original work is properly cited.
Article's History: Received 29th of May, 2026; Revised 9th of July, 2026; Accepted 25th of July, 2026; Available online: 27th of July, 2026. Published as research article in the Volume XXI, Fall, Issue 4(94), 2026.
Gopiraju, C., Shashidhar, A., & Jessica, V. M. (2026). Dynamic return and volatility connectedness across global markets: A time-frequency analysis. Journal of Applied Economic Sciences, Volume XXI, Fall, 4(94), 1091 – 1108. https://doi.org/10.57017/jaes.v21.4(94).01
Credit Authorship Contribution Statement: Challa Gopiraju: Conceptualisation, Methodology, Software, Formal analysis, Investigation, Data curation, Resources, Validation, Writing (original draft), Writing (review and editing), Visualisation, Project administration. Achakkagari Shashidhar: Writing (review and editing), Visualisation, Project administration. V. Mary Jessica: Supervision, Conceptualisation, Validation, Writing (review and editing).
Acknowledgments: The authors gratefully acknowledge the School of Management Studies, University of Hyderabad, for the academic and institutional support provided during the course of this research.
Funding statement: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Data Availability Statement: The data used in this study are publicly available from Yahoo Finance (https://finance.yahoo.com), covering the period January 2016 to March 2026 at daily frequency. Estimation code is available from the corresponding author upon reasonable request.
Declaration of Generative AI Use: During the preparation of this work, the author(s) used Claude (Anthropic) to assist with citation verification, identification of supporting literature, and language editing. No AI tools were used to generate research ideas, design the methodology, analyse data, or draw conclusions. All figures and tables in this manuscript are exclusively data-driven outputs produced through the authors' own statistical software and estimation code. After using this tool, the author(s) reviewed and edited the content as needed and take full responsibility for the content of the published article.
Ethical Approval Statement: This study is based exclusively on publicly available secondary market data (Yahoo Finance) and does not involve human participants, personal data, or animal subjects. Therefore, ethical approval was not required.
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