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Do Long Memory and Asymmetries Matter When Assessing Downside Return Risk?

Author

Listed:
  • Nico Katzke

    (Department of Economics, University of Stellenbosch)

  • Chris Garbers

    (Department of Economics, University of Stellenbosch)

Abstract
In this paper we set out to test whether, on sector level, returns series in South Africa exhibit long memory and asymmetries and, more specifically, whether these effects should be accounted for when assessing downside risk. The purpose of this analysis is not to identify the most optimal downside risk assessment model or to reaffirm the often regarded stylized fact of long memory and asymmetry in asset returns series. Rather we set out to establish whether accounting for these effects and allowing for more flexibility in second order persistence models lead to improved risk assessments. We use several variants of the widely used GARCH family of second order persistence models that control for these effects, and compare the downside risk estimates using Value-at-Risk measures of these different models and compare their out-of-sample performances. Our findings confirm that controlling for asymmetries and long memory in volatility models improve risk management calculations.

Suggested Citation

  • Nico Katzke & Chris Garbers, 2015. "Do Long Memory and Asymmetries Matter When Assessing Downside Return Risk?," Working Papers 06/2015, Stellenbosch University, Department of Economics.
    • Handle: RePEc:sza:wpaper:wpapers238
    as

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    File URL: https://www.ekon.sun.ac.za/wpapers/2015/wp062015/wp-06-2015.pdf
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    References listed on IDEAS

    as
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    Cited by:

    1. Cakan Esin & Rangan Gupta, 2017. "Does the US. macroeconomic news make the South African stock market riskier?," Journal of Developing Areas, Tennessee State University, College of Business, vol. 51(4), pages 17-27, October-D.
    2. Pramod Kumar Naik & Rangan Gupta & Puja Padhi, 2018. "The Relationship Between Stock Market Volatility And Trading Volume: Evidence From South Africa," Journal of Developing Areas, Tennessee State University, College of Business, vol. 52(1), pages 99-114, January-M.

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    More about this item

    Keywords

    Value-at-Risk; Expected Shortfall; GARCH; Fractional Integration; Kupiec back-testing procedure;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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