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Demographics and the Econometrics of the Term Structure of Stock Market Risk

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  • Carlo A. Favero
  • Andrea Tamoni

Abstract

The term structure of the stock market risk, defined as the per period conditional variance of cumulative returns, is measured in the strategic asset allocation literature (e.g. Campbell and Viceira (2002), (2005)) via multi-step ahead predictions from a VAR model of the joint process for one-period returns and their predictor, the dividend-price ratio. In this paper we modify the dynamic dividend growth model to allow for a time varying linearization point driven by the age structure of population. This specification leads to a decomposition of the dividend-price prices into an high volatility little persistence “noise” component, and a low volatility high persistence “information” component. The dividend-price ratio is mean reverting toward the time-varying mean and its deviations from it have a predicting power for returns that increases with the horizon. As a result of these two effects, the forward solution of the model delivers a negative sloping term structure of stock market risk. Direct regressions of returns at different horizons on the relevant predictors are much better suited to capture this feature than VAR based multi-period iterated forecasts. This evidence is very little affected by parameters’ uncertainty and is robust to the existence of "imperfect predictiors", as a parsimoniuos parameterization is very precisely estimated and no-projections for future variables are needed in the direct regression approach.

Suggested Citation

  • Carlo A. Favero & Andrea Tamoni, 2010. "Demographics and the Econometrics of the Term Structure of Stock Market Risk," Working Papers 367, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  • Handle: RePEc:igi:igierp:367
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    File URL: ftp://ftp.igier.unibocconi.it/wp/2010/367.pdf
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    References listed on IDEAS

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    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Martin Lettau & Stijn Van Nieuwerburgh, 2008. "Reconciling the Return Predictability Evidence," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1607-1652, July.
    3. John Geanakoplos & Michael Magill & Martine Quinzii, 2003. "Demography and the Long Run Behavior of the Stock Market," Levine's Working Paper Archive 506439000000000269, David K. Levine.
    4. Andrew Ang & Geert Bekaert, 2001. "Stock Return Predictability: Is it There?," NBER Working Papers 8207, National Bureau of Economic Research, Inc.
    5. JULES H. van BINSBERGEN & RALPH S. J. KOIJEN, 2010. "Predictive Regressions: A Present-Value Approach," Journal of Finance, American Finance Association, vol. 65(4), pages 1439-1471, August.
    6. Lubos Pástor & Robert F. Stambaugh, 2009. "Predictive Systems: Living with Imperfect Predictors," Journal of Finance, American Finance Association, vol. 64(4), pages 1583-1628, August.
    7. Ľuboš Pástor & Robert F. Stambaugh, 2012. "Are Stocks Really Less Volatile in the Long Run?," Journal of Finance, American Finance Association, vol. 67(2), pages 431-478, April.
    8. Peter C. Schotman & Rolf Tschernig & Jan Budek, 2008. "Long Memory and the Term Structure of Risk," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 6(4), pages 459-495, Fall.
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    Cited by:

    1. Pesaran, M. Hashem & Pick, Andreas & Timmermann, Allan, 2011. "Variable selection, estimation and inference for multi-period forecasting problems," Journal of Econometrics, Elsevier, vol. 164(1), pages 173-187, September.

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