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Absolute Return Volatility

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  • Cotter, John

Abstract

The use of absolute return volatility has many modelling benefits. An illustration is given for the market risk measure, minimum capital requirements.

Suggested Citation

  • Cotter, John, 2004. "Absolute Return Volatility," MPRA Paper 3530, University Library of Munich, Germany, revised 2005.
  • Handle: RePEc:pra:mprapa:3530
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    File URL: https://mpra.ub.uni-muenchen.de/3530/1/MPRA_paper_3530.pdf
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    References listed on IDEAS

    as
    1. Brooks, C & Clare, A D & Persand, G, 2002. "A Note on Estimating Market-Based Minimum Capital Risk Requirements: A Multivariate GARCH Approach," Manchester School, University of Manchester, vol. 70(5), pages 666-681, September.
    2. C. Brooks & A. D. Clare & G. Persand, 2002. "A Note on Estimating Market–based Minimum Capital Risk Requirements: A Multivariate GARCH Approach," Manchester School, University of Manchester, vol. 70(5), pages 666-681, September.
    3. John Cotter, 2004. "Minimum capital requirement calculations for UK futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 24(2), pages 193-220, February.
    4. Longin, Francois M., 2000. "From value at risk to stress testing: The extreme value approach," Journal of Banking & Finance, Elsevier, vol. 24(7), pages 1097-1130, July.
    5. Neil Shephard & Ole E. Barndorff-Nielsen, 2003. "Power variation and stochastic volatility: a review and some new results," Economics Series Working Papers 2003-W19, University of Oxford, Department of Economics.
    6. Longin, Francois M, 1996. "The Asymptotic Distribution of Extreme Stock Market Returns," The Journal of Business, University of Chicago Press, vol. 69(3), pages 383-408, July.
    7. Ole E. Barndorff-Nielsen & Svend Erik Graversen & Neil Shephard, 2003. "Power variation & stochastic volatility: a review and some new results," Economics Papers 2003-W19, Economics Group, Nuffield College, University of Oxford.
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    Cited by:

    1. Luisa Bisaglia & Matteo Grigoletto, 2018. "A new time-varying model for forecasting long-memory series," Papers 1812.07295, arXiv.org.
    2. Luisa Bisaglia & Matteo Grigoletto, 2021. "A new time-varying model for forecasting long-memory series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 139-155, March.
    3. Arouxet, M. Belén & Bariviera, Aurelio F. & Pastor, Verónica E. & Vampa, Victoria, 2022. "Covid-19 impact on cryptocurrencies: Evidence from a wavelet-based Hurst exponent," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).

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    JEL classification:

    • G0 - Financial Economics - - General

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