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Long Memory and the Term Structure of Risk

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  • Peter C. Schotman
  • Rolf Tschernig
  • Jan Budek

Abstract

This paper explores the implications of asset return predictability for long-term portfolio choice when return-forecasting variables are fractionally integrated. For important predictor variables, like the dividend-price ratio, and nominal and real interest rates, we estimate orders of integration around 0.8. This leads to substantial increases of the estimated long-term risk of stocks, bonds, and cash compared to estimates obtained from a stationary VAR. Results are sensitive to the inclusion of the short-term nominal interest rate in the prediction equation of excess stock returns. Jointly with the dividend-price ratio it has significant predictive power, but contrary to the dividend-price ratio the nominal interest rate does not induce mitigating effects through mean reversion. Copyright The Author 2008. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org., Oxford University Press.

Suggested Citation

  • Peter C. Schotman & Rolf Tschernig & Jan Budek, 2008. "Long Memory and the Term Structure of Risk," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 459-495, Fall.
    • Handle: RePEc:oup:jfinec:v:6:y:2008:i:4:p:459-495
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbn010
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    Cited by:

    1. Gil-Alana, Luis A. & Moreno, Antonio, 2012. "Uncovering the US term premium: An alternative route," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 1181-1193.
    2. Caporale, Guglielmo Maria & Gil-Alana, Luis Alberiko & Poza, Carlos, 2022. "The COVID-19 pandemic and the degree of persistence of US stock prices and bond yields," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 118-123.
    3. Tobias Hartl & Roland Weigand, 2018. "Multivariate Fractional Components Analysis," Papers 1812.09149, arXiv.org, revised Jan 2019.
    4. Tschernig, Rolf & Weber, Enzo & Weigand, Roland, 2014. "Long- versus medium-run identification in fractionally integrated VAR models," Economics Letters, Elsevier, vol. 122(2), pages 299-302.
    5. Todea, Alexandru, 2016. "Cross-correlations between volatility, volatility persistence and stock market integration: the case of emergent stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 87(C), pages 208-215.
    6. Daniela Osterrieder, 2013. "Interest Rates with Long Memory: A Generalized Affine Term-Structure Model," CREATES Research Papers 2013-17, Department of Economics and Business Economics, Aarhus University.
    7. Daniela Osterrieder & Peter C. Schotman, 2012. "The Volatility of Long-term Bond Returns: Persistent Interest Shocks and Time-varying Risk Premiums," CREATES Research Papers 2012-35, Department of Economics and Business Economics, Aarhus University.
    8. 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.
    9. 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.
    10. Chevillon, Guillaume & Mavroeidis, Sophocles, 2018. "Perpetual learning and apparent long memory," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 343-365.
    11. Gündüz, Yalin & Kaya, Orcun, 2014. "Impacts of the financial crisis on eurozone sovereign CDS spreads," Journal of International Money and Finance, Elsevier, vol. 49(PB), pages 425-442.

    More about this item

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • 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

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