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What Ties Return Volatilities to Price Valuations and Fundamentals?

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  • Alexander David
  • Pietro Veronesi

Abstract

Stock and Treasury bond comovement, volatilities, and their relations to their price valuations and fundamentals change stochastically over time, both in magnitude and direction. These stochastic changes are explained by a general equilibrium model in which agents learn about composite economic and inflation regimes. We estimate our model using both fundamentals and asset prices, and find that inflation news signal either positive or negative future real economic growth depending on the times, thereby affecting the direction of stock/bond comovement. The learning dynamics generate strong non-linearities between volatilities and price valuations. We find empirical support for numerous predictions of the model.

Suggested Citation

  • Alexander David & Pietro Veronesi, 2009. "What Ties Return Volatilities to Price Valuations and Fundamentals?," NBER Working Papers 15563, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:15563
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    2. Hui Guo & Zijun Wang & Jian Yang, 2013. "Time-Varying Risk-Return Trade-off in the Stock Market," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(4), pages 623-650, June.
    3. Bakshi, Gurdip & Chabi-Yo, Fousseni, 2012. "Variance bounds on the permanent and transitory components of stochastic discount factors," Journal of Financial Economics, Elsevier, vol. 105(1), pages 191-208.
    4. Nonejad, Nima, 2017. "Forecasting aggregate stock market volatility using financial and macroeconomic predictors: Which models forecast best, when and why?," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 131-154.
    5. 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.
    6. Bakshi, Gurdip & Chabi-Yo, Fousseni, 2011. "Variance Bounds on the Permanent and Transitory Components of Stochastic Discount Factors," Working Paper Series 2011-11, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    7. Paye, Bradley S., 2012. "‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables," Journal of Financial Economics, Elsevier, vol. 106(3), pages 527-546.
    8. Christian Conrad & Karin Loch, 2015. "Anticipating Long‐Term Stock Market Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1090-1114, November.
    9. Banegas, Ayelen & Gillen, Ben & Timmermann, Allan & Wermers, Russ, 2013. "The cross section of conditional mutual fund performance in European stock markets," Journal of Financial Economics, Elsevier, vol. 108(3), pages 699-726.
    10. Łukasz Kwiatkowski, 2011. "Bayesian Analysis of a Regime Switching In-Mean Effect for the Polish Stock Market," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 3(4), pages 187-219, December.
    11. Yiqun Mou & Lars A. Lochstoer & Michael Johannes, 2011. "Learning about Consumption Dynamics," 2011 Meeting Papers 306, Society for Economic Dynamics.

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    More about this item

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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