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Asymmetric Realized Volatility Risk

Author

Listed:
  • David E. Allen

    (University of Sydney, University of South Australia, Australia)

  • Michael McAleer

    (National Tsing Hua University, Taiwan; Erasmus University Rotterdam, the Netherlands; Complutense University Madrid, Spain)

  • Marcel Scharth

    (University of New South Wales, Australia)

Abstract

In this paper we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly Gaussian, this unpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Explicitly modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility model, which incorporates the fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&P 500 index and eight other indexes and stocks.

Suggested Citation

  • David E. Allen & Michael McAleer & Marcel Scharth, 2014. "Asymmetric Realized Volatility Risk," Tinbergen Institute Discussion Papers 14-075/III, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20140075
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    References listed on IDEAS

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    Cited by:

    1. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
    2. Xu, Yongdeng, 2022. "Exponential High-Frequency-Based-Volatility (EHEAVY) Models," Cardiff Economics Working Papers E2022/5, Cardiff University, Cardiff Business School, Economics Section.

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    More about this item

    Keywords

    Realized volatility; volatility of volatility; volatility risk; value-at-risk; forecasting; conditional heteroskedasticity;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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