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Clustering financial time series with variance ratio statistics

Author

Listed:
  • Joao A. Bastos

    (CEMAPRE, School of Economics and Management (ISEG), Technical University of Lisbon)

  • Jorge Caiado

    (CEMAPRE, School of Economics and Management (ISEG), Technical University of Lisbon)

Abstract

This study introduces a new distance measure for clustering financial time series based on variance ratio test statistics. The proposed metric attempts to assess the level of interdependence of time series from the point of view of return predictability. Simulation results show that this metric aggregates better time series according to their serial dependence structure than a metric based on the sample autocorrelations. An empirical application of this approach to international stock market returns is presented. The results suggest that this metric discriminates reasonably well stock markets according to size and level of development. Furthermore, despite the substantial evolution of individual variance ratio statistics, the clustering pattern remains fairly stable across different time periods.

Suggested Citation

  • Joao A. Bastos & Jorge Caiado, 2009. "Clustering financial time series with variance ratio statistics," CEMAPRE Working Papers 0904, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.
    • Handle: RePEc:cma:wpaper:0904
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    Cited by:

    1. Sipan Aslan & Ceylan Yozgatligil & Cem Iyigun, 2018. "Temporal clustering of time series via threshold autoregressive models: application to commodity prices," Annals of Operations Research, Springer, vol. 260(1), pages 51-77, January.
    2. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    3. Lúcio, Francisco & Caiado, Jorge, 2022. "COVID-19 and Stock Market Volatility: A Clustering Approach for S&P 500 Industry Indices," Finance Research Letters, Elsevier, vol. 49(C).
    4. Erniel B. Barrios & Paolo Victor T. Redondo, 2024. "Nonparametric Test for Volatility in Clustered Multiple Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 861-876, February.
    5. Fabrizio Durante & Roberta Pappadà & Nicola Torelli, 2015. "Clustering of time series via non-parametric tail dependence estimation," Statistical Papers, Springer, vol. 56(3), pages 701-721, August.
    6. B. Lafuente-Rego & P. D’Urso & J. A. Vilar, 2020. "Robust fuzzy clustering based on quantile autocovariances," Statistical Papers, Springer, vol. 61(6), pages 2393-2448, December.
    7. Caiado, Jorge & Lúcio, Francisco, 2023. "Stock market forecasting accuracy of asymmetric GARCH models during the COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 68(C).
    8. Fabrizio Durante & Roberta Pappadà & Nicola Torelli, 2014. "Clustering of financial time series in risky scenarios," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(4), pages 359-376, December.
    9. Dias, José G. & Vermunt, Jeroen K. & Ramos, Sofia, 2015. "Clustering financial time series: New insights from an extended hidden Markov model," European Journal of Operational Research, Elsevier, vol. 243(3), pages 852-864.
    10. Anna CZAPKIEWICZ & Pawel MAJDOSZ, 2014. "Grouping Stock Markets with Time-Varying Copula-GARCH Model," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 64(2), pages 144-159, March.
    11. Abdollahi, Hooman & Junttila, Juha-Pekka & Lehkonen, Heikki, 2024. "Clustering asset markets based on volatility connectedness to political news," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 93(C).
    12. Pierpaolo D’Urso & Livia Giovanni & Riccardo Massari, 2021. "Trimmed fuzzy clustering of financial time series based on dynamic time warping," Annals of Operations Research, Springer, vol. 299(1), pages 1379-1395, April.
    13. Jorge Caiado & Nuno Crato & Pilar Poncela, 2020. "A fragmented-periodogram approach for clustering big data time series," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(1), pages 117-146, March.
    14. João A. Bastos & Jorge Caiado, 2021. "On the classification of financial data with domain agnostic features," Working Papers REM 2021/0185, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    15. Ekaterina Dorodnykh, 2013. "What Drives Stock Exchange Integration?," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 6(2), pages 47-79, September.
    16. Albino, Andreia & Caiado, Jorge & Crato, Nuno, 2024. "Time series clustering using fragmented autocorrelations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 650(C).
    17. Galagedera, Don U.A., 2013. "A new perspective of equity market performance," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 333-357.

    More about this item

    Keywords

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    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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