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Volatility Proxies for Discrete Time Models

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  • de Vilder, Robin G.
  • Visser, Marcel P.

Abstract

Discrete time volatility models typically employ a latent scale factor to represent volatility. High frequency data may be used to construct proxies for these scale factors. Examples are the intraday high-low range and the realized volatility. This paper develops a method for ranking and optimizing volatility proxies. It is possible to outperform the quadratic variation as a proxy for the discrete time scale factor. For the S&P 500 index data over the years 1988-2006 this is achieved by a proxy which puts, among other things, more weight on the highs than on the lows over intraday intervals.

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File URL: http://mpra.ub.uni-muenchen.de/4917/
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File URL: http://mpra.ub.uni-muenchen.de/11001/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 4917.

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Date of creation: 14 Sep 2007
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Handle: RePEc:pra:mprapa:4917

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Keywords: volatility proxy; realized volatility; quadratic variation; scale factor; arch/garch/stochastic volatility; intraday seasonality;

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References

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  14. Lan Zhang & Per A. Mykland & Yacine Ait-Sahalia, 2003. "A Tale of Two Time Scales: Determining Integrated Volatility with Noisy High Frequency Data," NBER Working Papers 10111, National Bureau of Economic Research, Inc.
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  16. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range-Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, 06.
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Cited by:
  1. Visser, Marcel P., 2008. "Forecasting S&P 500 Daily Volatility using a Proxy for Downward Price Pressure," MPRA Paper 11100, University Library of Munich, Germany.
  2. Marcel P. Visser, 2011. "GARCH Parameter Estimation Using High-Frequency Data," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 9(1), pages 162-197, Winter.

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