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Determinants of Systemic Risk and Information Dissemination

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  • Marcelo Bianconi

    ()
    (Department of Economics, Tufts University, USA)

  • Xiaxin Hua

    ()
    (Department of Economics, Clark University, USA)

  • Chih Ming Tan

    ()
    (Department of Economics, University of North Dakota, USA)

Abstract

We study the effects of two measures of information dissemination on the determination of systemic risk. One measure is print-media consumer sentiment based while the other is volatility based. We find evidence that while the volatility measure (VIX) of future expectations has a more significant direct impact upon systemic risk of financial firms under distress, a consumer sentiment measure based on print-media news does impact upon firm’s financial stress via the externality of other firm’s financial stress. This latter effect is robust even though the VIX and the consumer sentiment have dynamic feedback in the short one and two-day horizon in levels, and contemporaneously in volatility. In reference to the internet bubble of the 1990s, the consumer sentiment measure predicts larger systemic risk in the whole period of exuberance while the VIX predicts a sharp larger systemic risk in the height of the bubble. Our evidence suggests that print-media consumer sentiment might be dominated by the VIX when predicting systemic risk.

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Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 67_13.

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Date of creation: Dec 2013
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Handle: RePEc:rim:rimwps:67_13

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Keywords: conditional value-at-risk; VIX; externality; consumer sentiment;

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  1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
  2. Dean Croushore, 2008. "Frontiers of real-time data analysis," Working Papers 08-4, Federal Reserve Bank of Philadelphia.
  3. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2011. "When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 321-340, March.
  4. Daron Acemoglu & Asuman Ozdaglar & Alireza Tahbaz-Salehi, 2013. "Systemic Risk and Stability in Financial Networks," NBER Working Papers 18727, National Bureau of Economic Research, Inc.
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  7. S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2008. "Real-time measurement of business conditions," Working Papers 08-19, Federal Reserve Bank of Philadelphia.
  8. Lars Peter Hansen, 2013. "Challenges in Identifying and Measuring Systemic Risk," NBER Chapters, in: Risk Topography: Systemic Risk and Macro Modeling, pages 15-30 National Bureau of Economic Research, Inc.
  9. Cheung, Yin-Wong & Ng, Lilian K., 1996. "A causality-in-variance test and its application to financial market prices," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 33-48.
  10. Markus K. Brunnermeier, 2009. "Deciphering the Liquidity and Credit Crunch 2007-2008," Journal of Economic Perspectives, American Economic Association, vol. 23(1), pages 77-100, Winter.
  11. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, 06.
  12. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, 02.
  13. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
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