Statistical properties, dynamic conditional correlation, and scaling analysis: evidence from international financial markets high-frequency data

Samuel Tabot Enow

doi:10.20525/ijrbs.v14i4.4033

Authors

Samuel Tabot Enow Research Associate, The IIE VEGA School

DOI:

https://doi.org/10.20525/ijrbs.v14i4.4033

Keywords:

High frequency data; dynamic conditional correlations; scaling behaviour; Financial Markets

Abstract

High-frequency data offers unparalleled insights into market dynamics, facilitating the analysis of statistical properties, dynamic correlations, and scaling behaviours with a precision previously unattainable. These Statistical Properties are essential framework for understanding and modelling the interactions between global financial markets over time. The aim of this study was to investigate the dynamic conditional correlations and scaling behaviour of high-frequency data from January,1 2010 to December 31, 2020. Using the S&P 500, FTSE 100, Nikkei 225, HKEX and DAX as sampled financial markets, the results revealed significant differences in correlation patterns across the markets, as well as fractal-like scaling behaviour. The relationship between the S&P 500-FTSE 100 and S&P 500-DAX supports trend-following strategies, while the Nikkei 225-HKEX and Nikkei 225-FTSE 100 may be closer to random-walk behaviour. This study advances the frontier of knowledge on high-frequency data and offers insights into the temporal relationships and scaling properties between financial markets.

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References

Abdulgaffar, M., John, N. A., Adedokun, L. A., Anthony, K. A., Micah, E. E. M., & Mohammed, A. (2023). Fractal geometry in high-frequency trading: Modelling market microstructure and price dynamics. Saudi Journal of Economics and Finance, 7(11), 484–488. DOI: https://doi.org/10.36348/sjef.2023.v07i11.002

Afzal, F., Haiying, P., Afzal, F., Mahmood, A., & Ikram, A. (2021). Value-at-risk analysis for measuring stochastic volatility of stock returns: Using GARCH-based dynamic conditional correlation model. Sage Open, 11(1), 1–11. DOI: https://doi.org/10.1177/21582440211005758

Andersen, T. G., Bollerslev, T., Diebold, F. X., & Ebens, H. (2001). The distribution of stock returns. Journal of Financial Economics, 61(1), 3–58. DOI: https://doi.org/10.1016/S0304-405X(01)00072-1

Bauwens, L., & Xu, Y. (2023). DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations. International Journal of Forecasting, 39(2), 938–955. DOI: https://doi.org/10.1016/j.ijforecast.2022.03.005

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI: https://doi.org/10.1016/0304-4076(86)90063-1

Chiu, E., Wu, H., & Yu, J. (2010). Dynamic correlation and portfolio optimization: An empirical study on financial market uncertainty. Mathematics and Computers in Simulation, 80(10), 2019–2031.

Chong, Y. Y., Lam, K. S., & So, M. K. (2003). Dynamic conditional correlation: A new approach to asset allocation. Journal of Risk and Financial Management, 6(2), 81–99.

Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2), 223–236. DOI: https://doi.org/10.1088/1469-7688/1/2/304

Di Matteo, T. (2007). Multi-scaling in finance. Quantitative Finance, 7(1), 21–36. DOI: https://doi.org/10.1080/14697680600969727

Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate GARCH models. Journal of Business & Economic Statistics, 20(3), 339–350. DOI: https://doi.org/10.1198/073500102288618487

Fama, E. F. (1965). The behaviour of stock-market prices. The Journal of Business, 38(1), 34–105. DOI: https://doi.org/10.1086/294743

Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: Measuring stock market comovements. Journal of Finance, 57(5), 2223–2261. DOI: https://doi.org/10.1111/0022-1082.00494

Hasbrouck, J. (2007). Empirical market microstructure. Oxford University Press. DOI: https://doi.org/10.1093/oso/9780195301649.001.0001

Jarantow, S. W., Pisors, E. D., & Chiu, M. L. (2023). Introduction to the use of linear and nonlinear regression analysis in quantitative biological assays. Current Protocols, 3, e801. DOI: https://doi.org/10.1002/cpz1.801

Jiang, B., & Yin, J. (2013). Ht-index for quantifying the fractal or scaling structure of geographic features. Annals of the American Association of Geographers, 104(3), 1–14. DOI: https://doi.org/10.1080/00045608.2013.834239

Jiang, Z. Q., Xie, W. J., Zhou, W. X., & Sornette, D. (2019). Multifractal analysis of financial markets: A review. Reports on Progress in Physics, 82(12), 125901. DOI: https://doi.org/10.1088/1361-6633/ab42fb

Kang, W. M., Ratti, R. A., & Yoon, Y. (2003). Dynamic correlation analysis of international stock markets. International Review of Economics & Finance, 12(4), 471–494.

Karanasos, M., Yfanti, S., & Karoglou, M. (2016). Multivariate FIAPARCH modelling of financial markets with dynamic correlations in times of crisis. International Review of Financial Analysis, 45, 332–349. DOI: https://doi.org/10.1016/j.irfa.2014.09.002

Lo, A. W. (1991). Long-term memory in stock market prices. Econometrica, 59(5), 1279–1313. DOI: https://doi.org/10.2307/2938368

Longin, F., & Solnik, B. (2001). Extreme correlation of international equity markets. The Journal of Finance, 56(2), 649–676. DOI: https://doi.org/10.1111/0022-1082.00340

Mandelbrot, B. (1963). The variation of certain speculative prices. The Journal of Business, 36(4), 394–419. DOI: https://doi.org/10.1086/294632

Mandelbrot, B. (1967). The fractal geometry of nature. W. H. Freeman and Company.

Mandelbrot, B. (2004). The (mis)behaviour of markets: A fractal view of risk, ruin, and reward. Basic Books.

Sahabuddin, M., Islam, Md. A., Tabash, M. I., Anagreh, S., Akter, R., & Rahman, Md. M. (2022). Co-movement, portfolio diversification, investors' behaviour and psychology: Evidence from developed and emerging countries’ stock markets. Journal of Risk and Financial Management, 15(319), 1–16. DOI: https://doi.org/10.3390/jrfm15080319

Zhang, L., & Hua, L. (2025). Major issues in high-frequency financial data analysis: A survey of solutions. Mathematics, 13(3), 347. DOI: https://doi.org/10.3390/math13030347