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Predictive Inference for Integrated Volatility

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Author Info

  • Norman R. Swanson

    ()
    (Rutgers University)

  • Valentina Corradi

    ()
    (University of Warwick)

  • Walter Distaso

    ()
    (Queen Mary)

Abstract

In recent years, numerous volatility-based derivative products have been engineered. This has led to interest in constructing conditional predictive den- sities and con¯dence intervals for integrated volatility. In this paper, we propose nonparametric estimators of the aforementioned quantities, based on model free volatility estimators. We establish consistency and asymptotic normality for the feasible estimators and study their ¯nite sample properties through a Monte Carlo experiment. Finally, using data from the New York Stock Exchange, we provide an empirical application to volatility directional predictability.

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Bibliographic Info

Paper provided by Rutgers University, Department of Economics in its series Departmental Working Papers with number 201109.

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Length: 20 pages
Date of creation: 15 May 2011
Date of revision:
Handle: RePEc:rut:rutres:201109

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Keywords: Diffusions; realized volatility measures; kernels; microstructure noise; jumps;

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References

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Citations

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Cited by:
  1. Valentina Corradi & Norman Swanson & Walter Distaso, 2006. "Predictive Density Estimators for Daily Volatility Based on the Use of Realized Measures," Departmental Working Papers 200620, Rutgers University, Department of Economics.
  2. Bandi, Federico M. & Russell, Jeffrey R. & Yang, Chen, 2008. "Realized volatility forecasting and option pricing," Journal of Econometrics, Elsevier, vol. 147(1), pages 34-46, November.
  3. Diep Duong & Norman Swanson, 2013. "Empirical Evidence on the Importance of Aggregation, Asymmetry, and Jumps for Volatility Prediction," Departmental Working Papers 201321, Rutgers University, Department of Economics.
  4. Corradi, Valentina & Distaso, Walter & Fernandes, Marcelo, 2012. "International market links and volatility transmission," Journal of Econometrics, Elsevier, vol. 170(1), pages 117-141.
  5. Filip Zikes & Jozef Barunik, 2013. "Semiparametric Conditional Quantile Models for Financial Returns and Realized Volatility," Papers 1308.4276, arXiv.org.
  6. Matei, Marius, 2011. "Non-Linear Volatility Modeling of Economic and Financial Time Series Using High Frequency Data," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 116-141, June.

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