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An empirical investigation of the long memory effect on the relationship between downside risk and stock returns: Evidence from international stock markets

Samuel Tabot Enow

International Journal of Research in Business and Social Science · 2025 · Vol. 14 / Issue 8 · 213-219

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Overview

Abstract

Long memory suggests that past returns can influence future returns over extended periods, indicating potential predictability. The aim of this study was to empirically examines the interplay between long memory effects, downside risk, and stock returns across five major international equity markets (S&P 500, FTSE 100, Nikkei 225, DAX, and ASX) from 2000 to 2022. An ARFIMA model which quantify fractional differencing was used in conjunction with a correlation matrix, regression and Granger causality test to investigate the interplay. The findings revealed a significant long memory across all markets, with Nikkei 225 exhibiting the strongest persistence, attributed to Japan’s prolonged economic stagnation. Counterintuitively, lagged downside risk positively predicts returns, aligning with behavioural theories of investor overreaction to tail events. Bidirectional causality between risk and returns underscores reflexive market dynamics. The study bridges a critical gap by integrating long memory into downside risk models, demonstrating that persistent volatility amplifies tail risk premiums. The findings of this study suggest that financial market regulators need to seriously considers adaptive regulatory tools due to the dual long memory in returns and downside risk necessitates. To the author´s knowledge, this study is the first to bridge a critical gap in the literature by systematically integrating long memory effects with downside risk metrics so as to provide a more robust framework for understanding risk-return dynamics, as it accounts for the persistent nature of volatility and tail risk, which traditional short-memory models often overlook.

Keywords: {'name': 'Long memory effect; downside risk; Stock returns; financial markets'}

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Samuel Tabot Enow
Research Associate, The IIE VEGA School

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Adekoya, O. B., & Oliyide, J. A. (2021). Data limitations in frontier markets: Implications for tail risk estimation. Emerging Markets Review, 49, 100789.

Adrian, T., Covas, F., & Liang, N. (2023). Tail-risk dynamics in interconnected markets. Journal of Financial Economics, 147(2), 345–367.

Aloui, C., & Hkiri, B. (2022). Oil shocks and equity tail risks in Middle Eastern markets. Energy Economics, 112, 106135.

DOI: 10.1016/0304-4076(95)01732-1

Baillie, R. T., Bollerslev, T., & Mikkelsen, H. O. (2023). ARFIMA model selection using AIC/BIC: A comparative study. Journal of Time Series Analysis, 44(3), 412–430.

Bali, T. G., Brown, S. J., & Caglayan, M. O. (2020). Downside risk in developed and emerging markets. Journal of Banking & Finance, 118, 105889.

Bali, T. G., Engle, R. F., & Murray, S. (2022). Downside volatility and expected stock returns. Review of Financial Studies, 35(12), 5643–5682.

Bank of England. (2023). Financial stability report: Domestic sectors and global shocks. https://www.bankofengland.co.uk

DOI: 10.1016/0304-405X(77)90017-4

Bekaert, G., & Hoerova, M. (2021). Volatility feedback and market fragility. Journal of Financial Economics, 142(1), 1–22.

DOI: 10.1016/j.jeconom.2021.04.013

DOI: 10.1016/j.irfa.2023.102678

Chordia, T., Roll, R., & Subrahmanyam, A. (2022). Liquidity and market feedback. Journal of Financial Markets, 58, 100723.

DOI: 10.1080/713665670

Deutsche Bundesbank. (2023). Industrial slowdowns and equity risk premiums. https://www.bundesbank.de

DOI: 10.47743/jopafl-2023-27-09

Enow, S. T. (2023). Exploring volatility clustering in financial markets and its implication. Journal of Economic & Social Development, 10(2), 81–85.

DOI: 10.1016/S1566-0141(02)00042-0

DOI: 10.1016/j.jfineco.2014.10.010

DOI: 10.1057/s41283-022-00110-0

Gupta, R., & Sharma, T. (2023). Nonlinear risk-return relationships in global equity markets. Finance Research Letters, 54, 103785.

DOI: 10.2307/2329964

International Monetary Fund. (2023). Global financial stability report: Geopolitical fragmentation and market interconnectedness. IMF. https://www.imf.org

Javed, F., Kristoufek, L., & Reboredo, J. C. (2023). Structural breaks and long memory in financial markets. Physica A: Statistical Mechanics and its Applications, 615, 128543.

DOI: 10.2307/1914185

DOI: 10.1016/j.pacfin.2023.101947

Kuroda, H. (2021). Monetary policy and volatility persistence in Japan. Bank of Japan Working Paper Series, 21-E-3.

DOI: 10.1080/1540496X.2023.2205545

DOI: 10.1016/j.jedc.2022.104522

Nguyen, T. N., Pham, T. H., & Vo, X. V. (2023). Downside risk premiums in U.S. equities. Journal of Empirical Finance, 72, 1–18.

Peters, E. E. (2020). Fractal market analysis: Applying chaos theory to investment and economics. Wiley.

DOI: 10.1016/j.jbankfin.2005.06.005

Reboredo, J. C., Rivera-Castro, M. A., & Ugolini, A. (2022). Long memory and algorithmic trading during COVID-19. Finance Research Letters, 47, 102677.

DOI: 10.1002/ijfe.143

DOI: 10.3905/joi.3.3.59

DOI: 10.1016/j.eswa.2022.119245

World Trade Organization. (2023). Global trade fragmentation and supply chain resilience. https://www.wto.org

Zhou, X., Li, Z., & Zhang, C. (2022). Asymmetric risk-return dynamics in European equities. European Financial Management, 28(5), 1483–1510.