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Multifrequency News and Stock Returns

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  • Laurent E. Calvet
  • Adlai J. Fisher

Abstract

Recent research documents that aggregate stock prices are driven by shocks with persistence levels ranging from daily intervals to several decades. Building on these insights, we introduce a parsimonious equilibrium model in which regime-shifts of heterogeneous durations affect the volatility of dividend news. We estimate tightly parameterized specifications with up to 256 discrete states on daily U.S. equity returns. The multifrequency equilibrium has significantly higher likelihood than the classic Campbell and Hentschel (1992) specification, while generating volatility feedback effects 6 to 12 times larger. We show in an extension that Bayesian learning about stochastic volatility is faster for bad states than good states, providing a novel source of endogenous skewness that complements the "uncertainty" channel considered in previous literature (e.g., Veronesi, 1999). Furthermore, signal precision induces a tradeoff between skewness and kurtosis, and economies with intermediate investor information best match the data.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 11441.

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Date of creation: Jun 2005
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Publication status: published as Calvet, Laurent E. and Adlai J. Fisher. "Multifrequency News and Stock Returns." Journal of Financial Economics 86, 1 (October 2007): 178-212.
Handle: RePEc:nbr:nberwo:11441

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Cited by:
  1. Larry G. Epstein & Emmanuel Farhi & Tomasz Strzalecki, 2013. "How Much Would You Pay to Resolve Long-Run Risk?," NBER Working Papers 19541, National Bureau of Economic Research, Inc.
  2. Ang, Andrew & Timmermann, Allan G, 2011. "Regime Changes and Financial Markets," CEPR Discussion Papers 8480, C.E.P.R. Discussion Papers.
  3. Laurent E. Calvet & Adlai J. Fisher, 2006. "Multifrequency Jump-Diffusions: An Equilibrium Approach," NBER Working Papers 12797, National Bureau of Economic Research, Inc.
  4. John M. Maheu & Thomas H. McCurdy & Yong Song, 2012. "Components of Bull and Bear Markets: Bull Corrections and Bear Rallies," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 391-403, February.
  5. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 7(2), pages 174-196, Spring.
  6. Beeler, Jason & Campbell, John Y., 2012. "The Long-Run Risks Model and Aggregate Asset Prices: An Empirical Assessment," Scholarly Articles 9887621, Harvard University Department of Economics.
  7. Ang, Andrew & Liu, Jun, 2005. "Risk, Return and Dividends," University of California at Los Angeles, Anderson Graduate School of Management qt1s25177n, Anderson Graduate School of Management, UCLA.
  8. John Y. Campbell & Stefano Giglio & Christopher Polk & Robert Turley, 2012. "An Intertemporal CAPM with Stochastic Volatility," NBER Working Papers 18411, National Bureau of Economic Research, Inc.
  9. Laurent E. Calvet & Adlai J. Fisher, 2005. "Multifrequency News and Stock Returns," NBER Working Papers 11441, National Bureau of Economic Research, Inc.
  10. Jason Beeler & John Y. Campbell, 2009. "The Long-Run Risks Model and Aggregate Asset Prices: An Empirical Assessment," NBER Working Papers 14788, National Bureau of Economic Research, Inc.
  11. Calvet, Laurent-Emmanuel & Czellar , Veronika, 2011. "state-observation sampling and the econometrics of learning models," Les Cahiers de Recherche 947, HEC Paris.
  12. Jensen, Mark J & Maheu, John M, 2013. "Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis," MPRA Paper 52132, University Library of Munich, Germany.
  13. repec:bos:wpaper:wp2013-002 is not listed on IDEAS

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