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Converting 1-Day Volatility to h-Day Volatitlity: Scaling by Root-h is Worse Than You Think

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Listed:
  • Francis X. Diebold
  • Andrew Hickman
  • Atsushi Inoue
  • Til Schuermann
Abstract
We show that the common practice of converting 1-day volatility estimates to h-day estimates by scaling by the sqaure root of h is inappropriate and produces overestimates of the variability of long-horizon volatility. We conclude that volatility models are best tailored to tasks: if interest centers on long-horizon volatility, then a long-horizon volatility model should be used. Economic considerations, however, confound even that prescription and point to important directions for future research.

Suggested Citation

  • Francis X. Diebold & Andrew Hickman & Atsushi Inoue & Til Schuermann, 1997. "Converting 1-Day Volatility to h-Day Volatitlity: Scaling by Root-h is Worse Than You Think," Center for Financial Institutions Working Papers 97-34, Wharton School Center for Financial Institutions, University of Pennsylvania.
    • Handle: RePEc:wop:pennin:97-34
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    References listed on IDEAS

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    Cited by:

    1. Odening, Martin & Hinrichs, Jan, 2003. "Die Quantifizierung von Marktrisiken in der Tierproduktion mittels Value-at-Risk und Extreme-Value-Theory," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 52(02), pages 1-11.
    2. Wolff, Christian & Lehnert, Thorsten, 2001. "Modelling Scale-Consistent VaR with the Truncated Lévy Flight," CEPR Discussion Papers 2711, C.E.P.R. Discussion Papers.
    3. Del Brio, Esther B. & Mora-Valencia, Andrés & Perote, Javier, 2014. "VaR performance during the subprime and sovereign debt crises: An application to emerging markets," Emerging Markets Review, Elsevier, vol. 20(C), pages 23-41.
    4. Torben G. Andersen & Tim Bollerslev & Peter Christoffersen & Francis X. Diebold, 2007. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," NBER Chapters, in: The Risks of Financial Institutions, pages 513-544, National Bureau of Economic Research, Inc.
    5. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2017. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 649-677.
    6. Gonzalez-Rivera, Gloria & Lee, Tae-Hwy & Mishra, Santosh, 2004. "Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood," International Journal of Forecasting, Elsevier, vol. 20(4), pages 629-645.
    7. Saadi, Samir & Rahman, Abdul, 2008. "Evidence of non-stationary bias in scaling by square root of time: Implications for Value-at-Risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(3), pages 272-289, July.
    8. Odening, Martin & Hinrichs, Jan, 2002. "Assessment Of Market Risk In Hog Production Using Value-At-Risk And Extreme Value Theory," 2002 Annual meeting, July 28-31, Long Beach, CA 19907, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    9. Mark R. Manfredo & Raymond M. Leuthold, 1998. "Agricultural Applications of Value-at-Risk Analysis: A Perspective," Finance 9805002, University Library of Munich, Germany.
    10. Peter F. Christoffersen & Francis X. Diebold, 2000. "How Relevant is Volatility Forecasting for Financial Risk Management?," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 12-22, February.
    11. Th'eophile Griveau-Billion & Ben Calderhead, 2019. "A Dynamic Bayesian Model for Interpretable Decompositions of Market Behaviour," Papers 1904.08153, arXiv.org, revised Jan 2020.
    12. Kam Fong Chan & Christopher Gan & Patricia A. McGraw, 2003. "A Hedging Strategy for New Zealand’s Exporters in Transaction Exposure to Currency Risk," Multinational Finance Journal, Multinational Finance Journal, vol. 7(1-2), pages 25-54, March-Jun.
    13. Wang, Jying-Nan & Du, Jiangze & Hsu, Yuan-Teng, 2018. "Measuring long-term tail risk: Evaluating the performance of the square-root-of-time rule," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 120-138.
    14. Peter F. Christoffersen & Francis X. Diebold & Til Schuermann, 1998. "Horizon problems and extreme events in financial risk management," Economic Policy Review, Federal Reserve Bank of New York, vol. 4(Oct), pages 109-118.
    15. Kavussanos, Manolis G. & Dimitrakopoulos, Dimitris N., 2011. "Market risk model selection and medium-term risk with limited data: Application to ocean tanker freight markets," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 258-268.
    16. Kai Schindelhauer & Chen Zhou, 2018. "Value-at-Risk prediction using option-implied risk measures," DNB Working Papers 613, Netherlands Central Bank, Research Department.
    17. Agnieszka Borowska & Lennart Hoogerheide & Siem Jan Koopman, 2019. "Bayesian Risk Forecasting for Long Horizons," Tinbergen Institute Discussion Papers 19-018/III, Tinbergen Institute.
    18. Smith, J.Q. & Santos, Antonio A.F., 2006. "Second-Order Filter Distribution Approximations for Financial Time Series With Extreme Outliers," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 329-337, July.
    19. Danielsson, Jon & Zigrand, Jean-Pierre, 2006. "On time-scaling of risk and the square-root-of-time rule," Journal of Banking & Finance, Elsevier, vol. 30(10), pages 2701-2713, October.
    20. Fritzsch, Simon & Timphus, Maike & Weiß, Gregor, 2024. "Marginals versus copulas: Which account for more model risk in multivariate risk forecasting?," Journal of Banking & Finance, Elsevier, vol. 158(C).
    21. Amy S. K. Wong, 2006. "Basel II and the Risk Management of Basket Options with Time-Varying Correlations," International Journal of Central Banking, International Journal of Central Banking, vol. 2(4), December.
    22. Wang, Jying-Nan & Yeh, Jin-Huei & Cheng, Nick Ying-Pin, 2011. "How accurate is the square-root-of-time rule in scaling tail risk: A global study," Journal of Banking & Finance, Elsevier, vol. 35(5), pages 1158-1169, May.
    23. Simon Fritzsch & Maike Timphus & Gregor Weiss, 2021. "Marginals Versus Copulas: Which Account For More Model Risk In Multivariate Risk Forecasting?," Papers 2109.10946, arXiv.org.

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