Heteroskedastic behaviour of stock prices: Evidence from international financial markets

Abstract

Traditional financial models assume constant variance in asset returns however, empirical evidence highlights volatility persistence, which are effectively captured by ARCH and GARCH models. The aim of this study was to examine the heteroskedastic behaviour of stock prices across international financial markets. Using a GARCH (1,1) model with a student’s t-distribution, this study analyses daily stock returns from the NYSE, FTSE 100, Nikkei 225, and Shanghai Composite (SSE) between 2018 and 2023. The findings confirm significant autoregressive conditional heteroskedasticity in all markets, supporting the notion that financial volatility is dynamic and clustered. Developed markets, such as the NYSE and FTSE 100, exhibit lower baseline volatility but more persistent shocks, indicative of market efficiency and stability. In contrast, emerging markets, particularly SSE, demonstrate higher volatility and greater sensitivity to past shocks, reflecting heightened uncertainty. The results re-iterate the importance of incorporating dynamic volatility models in risk assessments. Given the increasing complexity of global financial systems, this study advocates for enhanced regulatory frameworks and adaptive econometric methodologies to account for evolving market structures and external macroeconomic shocks.

Keywords: {'name': 'Heteroskedasticity, Volatility Clustering, GARCH, Stock Markets, Financial'}

Authors & affiliations

Samuel Tabot Enow
Affiliation not supplied

Deposited references

Supplied references, not a list of publications citing this article.

Al Awadhi, A., Demirer, R., & Ulussever, T. (2021). Volatility jumps and cross-market linkages in emerging equities. Emerging Markets Review, 48, 100796.

Albuquerque, P., Pereira, E., & Tabak, B. (2022). Political risk and stock market volatility in emerging economies. Journal of International Money and Finance, 120, 102521.

Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2021). Geopolitical risks and stock market dynamics of the BRICS. Economic Systems, 45(1), 100795.

DOI: 10.3386/w26983

Bollerslev, T., Hood, B., Huss, J., & Pedersen, L. H. (2021). Risk everywhere: Modeling and managing volatility. Journal of Financial Econometrics, 19(3), 573–600.

Bouri, E., Demirer, R., Gupta, R., & Nel, J. (2021). COVID-19 and stock market volatility: A global analysis. Finance Research Letters, 43, 102007.

DOI: 10.1016/j.frl.2020.101716

Cont, R. (2020). Quantitative finance: Theory, tools, and applications. Wiley.

Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2022). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 82, 102199.

Danielsson, J., & Zigrand, J. P. (2021). Systemic risk and macroprudential regulation. Annual Review of Financial Economics, 13, 333–358.

Degiannakis, S., Filis, G., & Kizys, R. (2018). Forecasting oil price volatility: The role of asymmetric GARCH models. Energy Economics, 75, 503–519.

Enders, W. (2014). Applied econometric time series (4th ed.). Wiley.

DOI: 10.1093/rfs/hhz072

DOI: 10.2307/1912773

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

DOI: 10.47743/jopafl-2023-27-09

DOI: 10.1016/j.physa.2007.02.010

DOI: 10.1111/j.1540-6261.1993.tb05128.x

DOI: 10.1016/j.patcog.2024.110386

Hansen, P. R., & Lunde, A. (2023). Volatility forecasting: A comparative review of stochastic and machine learning approaches. Journal of Economic Surveys, 37(2), 521–556.

DOI: 10.1016/j.procs.2018.05.050

DOI: 10.1016/j.pacfin.2020.101480

Liu, J., Stambaugh, R. F., & Yuan, Y. (2021). Size and value in China. Journal of Financial Economics, 142(1), 342–366.

DOI: 10.1016/j.frl.2020.101735

DOI: 10.1016/j.ijforecast.2024.01.002

Obalade, A. A., & Muzindutsi, P. F. (2022). Volatility spillovers between frontier markets and global benchmarks. Research in International Business and Finance, 60, 101611.

Patton, A. J., & Sheppard, K. (2022). Good volatility, bad volatility, and the cross-section of stock returns. Journal of Applied Econometrics, 37(1), 1–23.

Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A hybrid LSTM-GARCH approach. Expert Systems with Applications, 158, 113603.

Wang, Y., Wei, Y., & Wu, C. (2023). VaR underestimation during the Russia-Ukraine conflict: A GARCH-EVT analysis. Journal of Banking & Finance, 148, 106742.

DOI: 10.1080/1331677X.2021.1967771