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Adaptive Detection of Multiple Change-Points in Asset Price Volatility

In: Long Memory in Economics

Author

Listed:
  • Marc Lavielle

    (Université René Descartes and Université Paris-Sud)

  • Gilles Teyssière

    (Université Paris 1)

Abstract

Summary This chapter considers the multiple change-point problem for time series, including strongly dependent processes, with an unknown number of change-points. We propose an adaptive method for finding the segmentation, i.e., the sequence of change-points τ with the optimal level of resolution. This optimal segmentation $$ \hat \tau $$ is obtained by minimizing a penalized contrast function J(τ, y)+ßpen(τ). For a given contrast function J(τ, y) and a given penalty function pen(τ), the adaptive procedure for automatically choosing the penalization parameter β is such that the segmentation $$ \hat \tau $$ does not strongly depend on β. This algorithm is applied to the problem of detection of change-points in the volatility of financial time series, and compared with Vostrikova’s (1981) binary segmentation procedure.

Suggested Citation

  • Marc Lavielle & Gilles Teyssière, 2007. "Adaptive Detection of Multiple Change-Points in Asset Price Volatility," Springer Books, in: Gilles Teyssière & Alan P. Kirman (ed.), Long Memory in Economics, pages 129-156, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-34625-8_5
    DOI: 10.1007/978-3-540-34625-8_5
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    Citations

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    Cited by:

    1. Sensoy, Ahmet & Sobaci, Cihat, 2014. "Effects of volatility shocks on the dynamic linkages between exchange rate, interest rate and the stock market: The case of Turkey," Economic Modelling, Elsevier, vol. 43(C), pages 448-457.
    2. Tang, Yusui & Ma, Feng, 2023. "The volatility of natural resources implications for sustainable development: Crude oil volatility prediction based on the multivariate structural regime switching," Resources Policy, Elsevier, vol. 83(C).
    3. Kyungwon Kim & Jae Wook Song, 2018. "Managing Bubbles in the Korean Real Estate Market: A Real Options Framework," Sustainability, MDPI, vol. 10(8), pages 1-25, August.
    4. Sensoy, Ahmet, 2013. "Dynamic relationship between precious metals," Resources Policy, Elsevier, vol. 38(4), pages 504-511.
    5. Charakopoulos, Avraam & Karakasidis, Theodoros, 2022. "Backward Degree a new index for online and offline change point detection based on complex network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    6. Turhan, M. Ibrahim & Sensoy, Ahmet & Ozturk, Kevser & Hacihasanoglu, Erk, 2014. "A view to the long-run dynamic relationship between crude oil and the major asset classes," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 286-299.
    7. Sensoy, Ahmet & Soytas, Ugur & Yildirim, Irem & Hacihasanoglu, Erk, 2014. "Dynamic relationship between Turkey and European countries during the global financial crisis," Economic Modelling, Elsevier, vol. 40(C), pages 290-298.
    8. Turhan, M. Ibrahim & Sensoy, Ahmet & Hacihasanoglu, Erk, 2014. "A comparative analysis of the dynamic relationship between oil prices and exchange rates," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 32(C), pages 397-414.
    9. Marie Hušková & Zuzana Prášková, 2014. "Comments on: Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 265-269, June.
    10. Kyungwon Kim & Jae Wook Song, 2020. "Detecting Possible Reduction of the Housing Bubble in Korea for Different Residential Types and Regions," Sustainability, MDPI, vol. 12(3), pages 1-31, February.
    11. Mengchen Wang & Trevor Harris & Bo Li, 2023. "Asynchronous Changepoint Estimation for Spatially Correlated Functional Time Series," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(1), pages 157-176, March.
    12. Shu, Lei & Chen, Yu & Zhang, Weiping & Wang, Xueqin, 2022. "Spatial rank-based high-dimensional change point detection via random integration," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    13. Ma, Feng & Wang, Jiqian & Wahab, M.I.M. & Ma, Yuanhui, 2023. "Stock market volatility predictability in a data-rich world: A new insight," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1804-1819.
    14. Qian, Lihua & Zeng, Qing & Li, Tao, 2022. "Geopolitical risk and oil price volatility: Evidence from Markov-switching model," International Review of Economics & Finance, Elsevier, vol. 81(C), pages 29-38.
    15. Nick James & Max Menzies, 2021. "Collective correlations, dynamics, and behavioural inconsistencies of the cryptocurrency market over time," Papers 2107.13926, arXiv.org, revised Dec 2021.

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