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Realized Volatility and Long Memory: An Overview

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  • Esfandiar Maasoumi
  • Michael McAleer

Abstract

The challenge of modeling, estimating, testing, and forecasting financial volatility is both intellectually worthwhile and also central to the successful analysis of financial returns and optimal investment strategies. In each of the three primary areas of volatility modeling, namely, conditional (or generalized autoregressive conditional heteroskedasticity) volatility, stochastic volatility and realized volatility (RV), numerous univariate volatility models of individual financial assets and multivariate volatility models of portfolios of assets have been established. This special issue has eleven innovative articles, eight of which are focused directly on RV and three on long memory, while two are concerned with both RV and long memory.

Suggested Citation

  • Esfandiar Maasoumi & Michael McAleer, 2008. "Realized Volatility and Long Memory: An Overview," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 1-9.
  • Handle: RePEc:taf:emetrv:v:27:y:2008:i:1-3:p:1-9 DOI: 10.1080/07474930701853459
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    References listed on IDEAS

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    9. Timothy Halliday, 2006. "Income Risk and Health," Working Papers 200612, University of Hawaii at Manoa, Department of Economics.
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    Citations

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    Cited by:

    1. Thomas Haan & Theo Offerman & Randolph Sloof, 2017. "Discrimination in the Labour Market: The Curse of Competition between Workers," Economic Journal, Royal Economic Society, vol. 127(603), pages 1433-1466, August.
    2. Janus, Paweł & Koopman, Siem Jan & Lucas, André, 2014. "Long memory dynamics for multivariate dependence under heavy tails," Journal of Empirical Finance, Elsevier, pages 187-206.
    3. Allen, David E. & Gao, Jiti & McAleer, Michael, 2009. "Modelling and managing financial risk: An overview," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2521-2524.
    4. Laurini, Márcio Poletti & Hotta, Luiz Koodi, 2013. "Indirect Inference in fractional short-term interest rate diffusions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 109-126.
    5. Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, vol. 161(2), pages 284-303, April.
    6. Alexandra Chronopoulou & Frederi Viens, 2012. "Estimation and pricing under long-memory stochastic volatility," Annals of Finance, Springer, pages 379-403.

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