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Determinants of Systemic Risk and Information Dissemination

  • Marcelo Bianconi
  • Xiaxin Hua
  • Chih Ming Tan

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 Department of Economics, Tufts University in its series Discussion Papers Series, Department of Economics, Tufts University with number 0776.

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Date of creation: 2013
Date of revision:
Handle: RePEc:tuf:tuftec:0776
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  1. Nicholas Bloom, 2013. "Fluctuations in Uncertainty," NBER Working Papers 19714, National Bureau of Economic Research, Inc.
  2. Len Umantsev & Victor Chernozhukov, 2001. "Conditional value-at-risk: Aspects of modeling and estimation," Empirical Economics, Springer, vol. 26(1), pages 271-292.
  3. Robert Engle & Simone Manganelli, 2000. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Econometric Society World Congress 2000 Contributed Papers 0841, Econometric Society.
  4. Lars Peter Hansen, 2012. "Challenges in Identifying and Measuring Systemic Risk," NBER Working Papers 18505, National Bureau of Economic Research, Inc.
  5. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
  6. 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.
  7. Domenico Giannone & Lucrezia Reichlin & David H Small, 2007. "Nowcasting GDP and Inflation: The Real-Time Informational Content of Macroeconomic Data Releases," Money Macro and Finance (MMF) Research Group Conference 2006 164, Money Macro and Finance Research Group.
  8. Joseph, Kissan & Babajide Wintoki, M. & Zhang, Zelin, 2011. "Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1116-1127, October.
  9. Shih-Kang Chao & Wolfgang Karl Härdle & Weining Wang, 2012. "Quantile Regression in Risk Calibration," SFB 649 Discussion Papers SFB649DP2012-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  10. 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.
  11. S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2008. "Real-Time Measurement of Business Conditions," NBER Working Papers 14349, National Bureau of Economic Research, Inc.
  12. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
  13. Gertler, Mark & Kiyotaki, Nobuhiro, 2010. "Financial Intermediation and Credit Policy in Business Cycle Analysis," Handbook of Monetary Economics, in: Benjamin M. Friedman & Michael Woodford (ed.), Handbook of Monetary Economics, edition 1, volume 3, chapter 11, pages 547-599 Elsevier.
  14. Merton, Robert C, 1987. " A Simple Model of Capital Market Equilibrium with Incomplete Information," Journal of Finance, American Finance Association, vol. 42(3), pages 483-510, July.
  15. Kuan, Chung-Ming & Yeh, Jin-Huei & Hsu, Yu-Chin, 2009. "Assessing value at risk with CARE, the Conditional Autoregressive Expectile models," Journal of Econometrics, Elsevier, vol. 150(2), pages 261-270, June.
  16. Corredor, Pilar & Ferrer, Elena & Santamaria, Rafael, 2013. "Investor sentiment effect in stock markets: Stock characteristics or country-specific factors?," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 572-591.
  17. Dimpfl, Thomas & Jank, Stephan, 2011. "Can internet search queries help to predict stock market volatility?," CFR Working Papers 11-15, University of Cologne, Centre for Financial Research (CFR).
  18. 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.
  19. Jegadeesh, Narasimhan & Wu, Di, 2013. "Word power: A new approach for content analysis," Journal of Financial Economics, Elsevier, vol. 110(3), pages 712-729.
  20. Dimitrios Bisias & Mark Flood & Andrew W. Lo & Stavros Valavanis, 2012. "A Survey of Systemic Risk Analytics," Working Papers 12-01, Office of Financial Research, US Department of the Treasury.
  21. Campbell, John & Thompson, Samuel P., 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Scholarly Articles 2622619, Harvard University Department of Economics.
  22. Dimitrios Bisias & Mark Flood & Andrew W. Lo & Stavros Valavanis, 2012. "A Survey of Systemic Risk Analytics," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 255-296, October.
  23. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  24. 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.
  25. Diego García, 2013. "Sentiment during Recessions," Journal of Finance, American Finance Association, vol. 68(3), pages 1267-1300, 06.
  26. 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.
  27. 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.
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