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Measuring and Modeling Risk Using High-Frequency Data

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Author Info

  • Wolfgang Härdle
  • Nikolaus Hautsch
  • Uta Pigorsch

Abstract

Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be obtained by summing over squared high-frequency returns. In turn, this so-called realized volatility can be used for more accurate model evaluation and description of the dynamic and distributional structure of volatility. Moreover, non-parametric measures of systematic risk are attainable, that can straightforwardly be used to model the commonly observed time-variation in the betas. The discussion of these new measures and methods is accompanied by an empirical illustration using high-frequency data of the IBM incorporation and of the DJIA index.

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Bibliographic Info

Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2008-045.

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Length: 23 pages
Date of creation: Jun 2008
Date of revision:
Handle: RePEc:hum:wpaper:sfb649dp2008-045

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Keywords: Realized Volatility; Realized Betas; Volatility Modeling;

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Cited by:
  1. Weber, Enzo & Zhang, Yanqun, 2012. "Common influences, spillover and integration in Chinese stock markets," Journal of Empirical Finance, Elsevier, vol. 19(3), pages 382-394.
  2. Till Dannewald & Lutz Hildebrandt, 2008. "A Brand Specific Investigation of International Cost Shock Threats on Price and Margin with a Manufacturer-Wholesaler-Retailer Model," SFB 649 Discussion Papers SFB649DP2008-070, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  3. Enzo Weber, 2008. "Structural Dynamic Conditional Correlation," SFB 649 Discussion Papers SFB649DP2008-069, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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