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Determinants of Systemic Risk and Information Dissemination

Author

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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.

Suggested Citation

  • Marcelo Bianconi & Xiaxin Hua & Chih Ming Tan, 2013. "Determinants of Systemic Risk and Information Dissemination," Working Paper series 67_13, Rimini Centre for Economic Analysis.
  • Handle: RePEc:rim:rimwps:67_13
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    1. N. Bloom, 2016. "Fluctuations in uncertainty," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 4.
    2. 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.
    3. 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.
    4. Huina Mao & Scott Counts & Johan Bollen, 2011. "Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data," Papers 1112.1051, arXiv.org.
    5. Len Umantsev & Victor Chernozhukov, 2001. "Conditional value-at-risk: Aspects of modeling and estimation," Empirical Economics, Springer, vol. 26(1), pages 271-292.
    6. 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.
    7. 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.
    8. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    9. Jegadeesh, Narasimhan & Wu, Di, 2013. "Word power: A new approach for content analysis," Journal of Financial Economics, Elsevier, vol. 110(3), pages 712-729.
    10. Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    11. 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.
    12. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    13. Thomas Dimpfl & Stephan Jank, 2016. "Can Internet Search Queries Help to Predict Stock Market Volatility?," European Financial Management, European Financial Management Association, vol. 22(2), pages 171-192, March.
    14. 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.
    15. Chao, Shih-Kang & Härdle, Wolfgang Karl & Wang, Weining, 2012. "Quantile regression in risk calibration," SFB 649 Discussion Papers 2012-006, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    16. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    17. Daron Acemoglu & Asuman Ozdaglar & Alireza Tahbaz-Salehi, 2015. "Systemic Risk and Stability in Financial Networks," American Economic Review, American Economic Association, vol. 105(2), pages 564-608, February.
    18. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    19. 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.
    20. 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.
    21. 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.
    22. Matthias W. Uhl, 2014. "Reuters Sentiment and Stock Returns," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 15(4), pages 287-298, October.
    23. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    24. Diego García, 2013. "Sentiment during Recessions," Journal of Finance, American Finance Association, vol. 68(3), pages 1267-1300, June.
    25. 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.
    26. Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
    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.
    28. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    29. 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, June.
    30. 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, February.
    31. Hailiang Chen & Prabuddha De & Yu (Jeffrey) Hu & Byoung-Hyoun Hwang, 2014. "Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media," The Review of Financial Studies, Society for Financial Studies, vol. 27(5), pages 1367-1403.
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    2. Madhavi Latha Challa & Venkataramanaiah Malepati & Siva Nageswara Rao Kolusu, 2018. "Forecasting risk using auto regressive integrated moving average approach: an evidence from S&P BSE Sensex," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 4(1), pages 1-17, December.
    3. Boubaker, Sabri & Karim, Sitara & Naeem, Muhammad Abubakr & Rahman, Molla Ramizur, 2024. "On the prediction of systemic risk tolerance of cryptocurrencies," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    4. Shan Jiang & Hsinchun Chen, 2019. "Examining patterns of scientific knowledge diffusion based on knowledge cyber infrastructure: a multi-dimensional network approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1599-1617, December.
    5. Yao, Yanzhen & Li, Jianping & Zhu, Xiaoqian & Wei, Lu, 2017. "Expected default based score for identifying systemically important banks," Economic Modelling, Elsevier, vol. 64(C), pages 589-600.
    6. Song, Jianhua & Zhang, Zhepei & So, Mike K.P., 2021. "On the predictive power of network statistics for financial risk indicators," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    7. Hikmet Akyol & Selim Basar, 2024. "Empirical Analysis of Turkish Banking Sector Institutional and Macroeconomic Determinants of Risks," Istanbul Journal of Economics-Istanbul Iktisat Dergisi, Istanbul University, Faculty of Economics, vol. 73(74-1), pages 59-98, June.
    8. Shuting Liu & Qifa Xu & Cuixia Jiang, 2021. "Systemic risk of China’s commercial banks during financial turmoils in 2010-2020: A MIDAS-QR based CoVaR approach," Applied Economics Letters, Taylor & Francis Journals, vol. 28(18), pages 1600-1609, October.
    9. Tom Marty & Bruce Vanstone & Tobias Hahn, 2020. "News media analytics in finance: a survey," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(2), pages 1385-1434, June.
    10. Silva, Walmir & Kimura, Herbert & Sobreiro, Vinicius Amorim, 2017. "An analysis of the literature on systemic financial risk: A survey," Journal of Financial Stability, Elsevier, vol. 28(C), pages 91-114.

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    More about this item

    Keywords

    conditional value-at-risk; VIX; externality; consumer sentiment;
    All these keywords.

    JEL classification:

    • G00 - Financial Economics - - General - - - General
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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