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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.

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Bibliographic Info

Paper provided by IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University in its series Working Papers with number 367.

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Date of creation: 2010
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Handle: RePEc:igi:igierp:367

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  1. John Geanakoplos & Michael Magill & Martine Quinzii, 2004. "Demography and the Long Run Behavior of the Stock Market," Levine's Bibliography 122247000000000643, UCLA Department of Economics.
  2. Pástor, Luboš & Stambaugh, Robert F, 2007. "Predictive Systems: Living with Imperfect Predictors," CEPR Discussion Papers 6076, C.E.P.R. Discussion Papers.
  3. Schotman, Peter & Tschernig, Rolf & Budek, Jan, 2008. "Long Memory and the Term Structure of Risk," University of Regensburg Working Papers in Business, Economics and Management Information Systems 427, University of Regensburg, Department of Economics.
  4. 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, 04.
  5. Martin Lettau & Stijn Van Nieuwerburgh, 2006. "Reconciling the Return Predictability Evidence," 2006 Meeting Papers 29, Society for Economic Dynamics.
  6. 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.
  7. Jules H. van Binsbergen & Ralph S.J. Koijen, 2010. "Predictive Regressions: A Present-value Approach," NBER Working Papers 16263, National Bureau of Economic Research, Inc.
  8. Andrew Ang & Geert Bekaert, 2001. "Stock Return Predictability: Is it There?," NBER Working Papers 8207, National Bureau of Economic Research, Inc.
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Cited by:
  1. M. Hashem Pesaran & Andreas Pick & Allan Timmermann, 2010. "Variable Selection, Estimation and Inference for Multi-period Forecasting Problems," DNB Working Papers 250, Netherlands Central Bank, Research Department.

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