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Volatility Clustering: A Nonlinear Theoretical Approach

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Abstract
This paper verifies the endogenous mechanism and economic intuition on volatility clustering using the coexistence of two locally stable attractors proposed by Gaunersdorfer, Hommes and Wagener (2008). By considering a simple asset pricing model with two types of boundedly rational traders, fundamentalists and trend followers, and noise traders, we provide conditions on the coexistence of locally stable steady state and invariant cycle of the underlying nonlinear deterministic financial market model and show numerically that the interaction of the coexistence of the deterministic dynamics and noise processes can endogenously generate volatility clustering and long range dependence in volatility observed in financial markets. Economically, volatility clustering occurs when neither the fundamental nor trend following traders dominate the market and when traders switch more often between the two strategies.

Suggested Citation

  • Xue-Zhong He & Kai Li & Chuncheng Wan, 2015. "Volatility Clustering: A Nonlinear Theoretical Approach," Research Paper Series 365, Quantitative Finance Research Centre, University of Technology, Sydney.
    • Handle: RePEc:uts:rpaper:365
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    3. He, Xue-Zhong & Li, Kai, 2015. "Profitability of time series momentum," Journal of Banking & Finance, Elsevier, vol. 53(C), pages 140-157.
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Xue-Zhong He & Kai Li & Chuncheng Wang, 2018. "Time-varying economic dominance in financial markets: A bistable dynamics approach," Published Paper Series 2018-1, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    2. Xue-Zhong He & Youwei Li, 2017. "The adaptiveness in stock markets: testing the stylized facts in the DAX 30," Journal of Evolutionary Economics, Springer, vol. 27(5), pages 1071-1094, November.
    3. Dimitrios Vortelinos & Konstantinos Gkillas (Gillas) & Costas Syriopoulos & Argyro Svingou, 2017. "Asymmetric and nonlinear inter-relations of US stock indices," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 14(1), pages 78-129, December.
    4. Li, Kai, 2021. "Nonlinear effect of sentiment on momentum," Journal of Economic Dynamics and Control, Elsevier, vol. 133(C).
    5. Sabiou M. Inoua, 2020. "News-Driven Expectations and Volatility Clustering," JRFM, MDPI, vol. 13(1), pages 1-14, January.
    6. Zhao, Xiaojun & Zhang, Na & Zhang, Yali & Xu, Chao & Shang, Pengjian, 2024. "Equity markets volatility clustering: A multiscale analysis of intraday and overnight returns," Journal of Empirical Finance, Elsevier, vol. 77(C).
    7. Venelina Nikolova & Juan E. Trinidad Segovia & Manuel Fernández-Martínez & Miguel Angel Sánchez-Granero, 2020. "A Novel Methodology to Calculate the Probability of Volatility Clusters in Financial Series: An Application to Cryptocurrency Markets," Mathematics, MDPI, vol. 8(8), pages 1-15, July.
    8. He, Xue-Zhong & Li, Kai & Santi, Caterina & Shi, Lei, 2022. "Social interaction, volatility clustering, and momentum," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 125-149.
    9. Sabiou Inoua, 2023. "News-driven Expectations and Volatility Clustering," Papers 2309.04876, arXiv.org.
    10. He, Xue-Zhong & Li, Youwei & Zheng, Min, 2019. "Heterogeneous agent models in financial markets: A nonlinear dynamics approach," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 135-149.
    11. Zhao, Dongxu & Li, Kai, 2022. "Bounded rationality, adaptive behaviour, and asset prices," International Review of Financial Analysis, Elsevier, vol. 80(C).
    12. Zheng, Min & Liu, Ruipeng & Li, Youwei, 2018. "Long memory in financial markets: A heterogeneous agent model perspective," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 38-51.
    13. Zheng, Huanhuan, 2020. "Coordinated bubbles and crashes," Journal of Economic Dynamics and Control, Elsevier, vol. 120(C).
    14. Trinidad Segovia, J.E. & Fernández-Martínez, M. & Sánchez-Granero, M.A., 2019. "A novel approach to detect volatility clusters in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).

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    More about this item

    Keywords

    volatility clustering; fundamentalists and trend followers; bounded rationality; stability; coexisting attractors;
    All these keywords.

    JEL classification:

    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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