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Realized Volatility Risk

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Author Info

  • David E. Allen
  • Michael McAleer

    ()
    (University of Canterbury)

  • Marcel Scharth

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. Carefully modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility (DARV) model, which incorporates the important 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.

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File URL: http://www.econ.canterbury.ac.nz/RePEc/cbt/econwp/1026.pdf
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Bibliographic Info

Paper provided by University of Canterbury, Department of Economics and Finance in its series Working Papers in Economics with number 10/26.

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Length: 39 pages
Date of creation: 01 May 2010
Date of revision:
Handle: RePEc:cbt:econwp:10/26

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Keywords: Realized volatility; volatility of volatility; volatility risk; value-at-risk; forecasting; conditional heteroskedasticity;

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References

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Citations

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Cited by:
  1. Vincenzo Candila, 2013. "A Comparison Of The Forecasting Performances Of Multivariate Volatility Models," Working Papers 3_228, Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno.
  2. Jensen, Mark J & Maheu, John M, 2013. "Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis," MPRA Paper 52132, University Library of Munich, Germany.
  3. Siem Jan Koopman & Marcel Scharth, 2011. "The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures," Tinbergen Institute Discussion Papers 11-132/4, Tinbergen Institute.
  4. Manabu Asai & Michael McAleer, 2013. "Leverage and Feedback E ects on Multifactor Wishart Stochastic Volatility for Option Pricing," Documentos del Instituto Complutense de Análisis Económico 2013-02, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales.
  5. Cathy Ning & Dinghai Xu & Tony Wirjanto, 2009. "Modeling Asymmetric Volatility Clusters Using Copulas and High Frequency Data," Working Papers 006, Ryerson University, Department of Economics.
  6. Manabu Asai & Michael McAleer & Marcelo C. Medeiros, 2009. "Asymmetry and Leverage in Realized Volatility," CARF F-Series CARF-F-167, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
  7. Manabu Asai & Michael McAleer & Marcelo C. Medeiros, 2011. "Asymmetry and Long Memory in Volatility Modelling," Documentos del Instituto Complutense de Análisis Económico 2011-29, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales.
  8. Bonato, Matteo & Caporin, Massimiliano & Ranaldo, Angelo, 2012. "Forecasting Realized (Co)Variances with a Bloc Structure Wishart Autoregressive Model," Working Papers on Finance 1211, University of St. Gallen, School of Finance.
  9. Federico M. Bandi & Roberto Reno, 2009. "Nonparametric Stochastic Volatility," Global COE Hi-Stat Discussion Paper Series gd08-035, Institute of Economic Research, Hitotsubashi University.

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