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News and network structures in equity market volatility

Author

Listed:
  • Adam Clements

    () (QUT)

  • Yin Liao

    () (QUT)

Abstract

An understanding of the linkages between assets is important for understanding the stability of markets. Network analysis provides a natural framework within which to examine such linkages. This paper examines the impact of firm specific news arrivals on the interconnections at an individual firm and overall portfolio level. While a great deal of research has focused on the impact of news on the volatility of a single asset, much less attention has been paid to the role of news in explaining the links between assets. It is found that the both the volume of news and its associated sentiment are important drivers the connectedness between individual stocks and the overall market structure. Firms that experience negative news arrivals during periods of market stress become more centrally important in the market structure.

Suggested Citation

  • Adam Clements & Yin Liao, "undated". "News and network structures in equity market volatility," NCER Working Paper Series 110, National Centre for Econometric Research.
  • Handle: RePEc:qut:auncer:2016_01
    as

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    File URL: http://www.ncer.edu.au/papers/documents/WP110.pdf
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    References listed on IDEAS

    as
    1. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    2. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    3. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
    4. 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.
    5. Smales, Lee A., 2014. "News sentiment and the investor fear gauge," Finance Research Letters, Elsevier, vol. 11(2), pages 122-130.
    6. Riordan, Ryan & Storkenmaier, Andreas & Wagener, Martin & Sarah Zhang, S., 2013. "Public information arrival: Price discovery and liquidity in electronic limit order markets," Journal of Banking & Finance, Elsevier, vol. 37(4), pages 1148-1159.
    7. Kalev, Petko S. & Liu, Wai-Man & Pham, Peter K. & Jarnecic, Elvis, 2004. "Public information arrival and volatility of intraday stock returns," Journal of Banking & Finance, Elsevier, vol. 28(6), pages 1441-1467, June.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Networks; news; volatility; sentiment;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G00 - Financial Economics - - General - - - General

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