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Measuring and modeling risk using high-frequency data

Author

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  • Härdle, Wolfgang Karl
  • Hautsch, Nikolaus
  • Pigorsch, Uta
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 af 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 incorpration and the DJIA index.

Suggested Citation

  • Härdle, Wolfgang Karl & Hautsch, Nikolaus & Pigorsch, Uta, 2008. "Measuring and modeling risk using high-frequency data," SFB 649 Discussion Papers 2008-045, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    • Handle: RePEc:zbw:sfb649:sfb649dp2008-045
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    References listed on IDEAS

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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. Dannewald, Till & Hildebrandt, Lutz, 2008. "A brand specific investigation of international cost shock threats on price and margin with a manufacturer-wholesaler-retailer model," SFB 649 Discussion Papers 2008-070, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. repec:hum:wpaper:sfb649dp2008-072 is not listed on IDEAS
    4. repec:hum:wpaper:sfb649dp2008-069 is not listed on IDEAS
    5. Zhang, Zhengjun & Zhu, Bin, 2016. "Copula structured M4 processes with application to high-frequency financial data," Journal of Econometrics, Elsevier, vol. 194(2), pages 231-241.
    6. Weber, Enzo, 2008. "Structural dynamic conditional correlation," SFB 649 Discussion Papers 2008-069, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    7. repec:hum:wpaper:sfb649dp2008-070 is not listed on IDEAS

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    More about this item

    Keywords

    Realized volatility; realized betas; volatility modeling;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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