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Return predictability and intertemporal asset allocation: Evidence from a bias-adjusted VAR model

Author

Listed:
  • Tom Engsted
  • Thomas Q. Pedersen

    (School of Economics and Management, University of Aarhus, Denmark and CREATES)

Abstract

We extend the VAR based intertemporal asset allocation approach from Campbell et al. (2003) to the case where the VAR parameter estimates are adjusted for small-sample bias. We apply the analytical bias formula from Pope (1990) using both Campbell et al.'s dataset, and an extended dataset with quarterly data from 1952 to 2006. The results show that correcting the VAR parameters for small-sample bias has both quantitatively and qualitatively important e¤ects on the strategic intertemporal part of optimal portfolio choice, especially for bonds: for intermediate values of risk-aversion, the intertemporal hedging demand for bonds - and thereby the total demand for bonds - is strongly reduced by the bias-adjustment. We also investigate the robustness of the results by changing the lag-length and one of the state variables of the VAR.

Suggested Citation

  • Tom Engsted & Thomas Q. Pedersen, 2008. "Return predictability and intertemporal asset allocation: Evidence from a bias-adjusted VAR model," CREATES Research Papers 2008-27, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2008-27
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    Citations

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    Cited by:

    1. Massimo Guidolin & Alexei G. Orlov, 2022. "Can Investors Benefit from Hedge Fund Strategies? Utility-Based, Out-of-Sample Evidence," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 12(03), pages 1-61, September.
    2. Michael D. Bauer & Glenn D. Rudebusch & Jing Cynthia Wu, 2011. "Unbiased estimate of dynamic term structure models," Working Paper Series 2011-12, Federal Reserve Bank of San Francisco.
    3. Amélie Charles & Olivier Darné & Jae H. Kim, 2016. "Stock Return Predictability: Evaluation based on prediction intervals," Working Papers hal-01295037, HAL.
    4. Amélie Charles & Olivier Darné & Jae H. Kim, 2022. "Stock return predictability: Evaluation based on interval forecasts," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 363-385, April.
    5. Martin M. Andreasen & Andrew Meldrum, 2014. "Dynamic term structure models: The best way to enforce the zero lower bound," CREATES Research Papers 2014-47, Department of Economics and Business Economics, Aarhus University.
    6. Tom Engsted & Thomas Q. Pedersen, 2014. "Bias-Correction in Vector Autoregressive Models: A Simulation Study," Econometrics, MDPI, vol. 2(1), pages 1-27, March.
    7. Bart Diris & Franz Palm & Peter Schotman, 2015. "Long-Term Strategic Asset Allocation: An Out-of-Sample Evaluation," Management Science, INFORMS, vol. 61(9), pages 2185-2202, September.
    8. Thomas Q. Pedersen, 2008. "Intertemporal Asset Allocation with Habit Formation in Preferences: An Approximate Analytical Solution," CREATES Research Papers 2008-60, Department of Economics and Business Economics, Aarhus University.
    9. Thomas Q. Pedersen, 2015. "Predictable Return Distributions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 114-132, March.
    10. Martin M Andreasen & Andrew Meldrum, 2015. "Dynamic term structure models: the best way to enforce the zero lower bound in the United States," Bank of England working papers 550, Bank of England.

    More about this item

    Keywords

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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