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Volatility persistence in stock market

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  • Chuang, Hongwei

Abstract

By using the unique dataset that consists of all brokers’ daily trading information, I propose an empirical methodology to construct a Granger-causality financial network to study the relationship between dynamics of volatility and information diffusion in a stock market. The financial network I proposed not only provides a new way to describe mutual interconnectedness of brokers in the market, but the empirical results also show the financial network density is positively correlated to the realized volatility of the market.

Suggested Citation

  • Chuang, Hongwei, 2015. "Volatility persistence in stock market," Economics Letters, Elsevier, vol. 133(C), pages 64-67.
    • Handle: RePEc:eee:ecolet:v:133:y:2015:i:c:p:64-67
    DOI: 10.1016/j.econlet.2015.05.018
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    References listed on IDEAS

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    1. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    2. Lobato, Ignacio N & Velasco, Carlos, 2000. "Long Memory in Stock-Market Trading Volume," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(4), pages 410-427, October.
    3. Bollerslev, Tim & Jubinski, Dan, 1999. "Equity Trading Volume and Volatility: Latent Information Arrivals and Common Long-Run Dependencies," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 9-21, January.
    4. Epps, Thomas W & Epps, Mary Lee, 1976. "The Stochastic Dependence of Security Price Changes and Transaction Volumes: Implications for the Mixture-of-Distributions Hypothesis," Econometrica, Econometric Society, vol. 44(2), pages 305-321, March.
    5. Ozsoylev, Han N. & Walden, Johan, 2011. "Asset pricing in large information networks," Journal of Economic Theory, Elsevier, vol. 146(6), pages 2252-2280.
    6. Peter M. DeMarzo & Dimitri Vayanos & Jeffrey Zwiebel, 2003. "Persuasion Bias, Social Influence, and Unidimensional Opinions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(3), pages 909-968.
    7. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    8. Han N. Ozsoylev & Johan Walden & M. Deniz Yavuz & Recep Bildik, 2014. "Investor Networks in the Stock Market," The Review of Financial Studies, Society for Financial Studies, vol. 27(5), pages 1323-1366.
    9. Brad M. Barber & Yi-Tsung Lee & Yu-Jane Liu & Terrance Odean, 2009. "Just How Much Do Individual Investors Lose by Trading?," The Review of Financial Studies, Society for Financial Studies, vol. 22(2), pages 609-632, February.
    10. Andersen, Torben G, 1996. "Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility," Journal of Finance, American Finance Association, vol. 51(1), pages 169-204, March.
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    Cited by:

    1. Gil-Alana, Luis A. & Infante, Juan & Martín-Valmayor, Miguel Angel, 2023. "Persistence and long run co-movements across stock market prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 347-357.
    2. Chuang, Hongwei, 2016. "Brokers’ financial network and stock return," The North American Journal of Economics and Finance, Elsevier, vol. 36(C), pages 172-183.

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    More about this item

    Keywords

    Financial network; Realized volatility; Long memory; Systemic risk;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • G2 - Financial Economics - - Financial Institutions and Services

    Statistics

    Access and download statistics

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