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Continuous-time models, realized volatilities, and testable distributional implications for daily stock returns

Author Info

  • Torben G. Andersen

    (Department of Finance, Kellogg School of Management, Northwestern University, Evanston, IL, USA, NBER and CREATES)

  • Tim Bollerslev

    (Department of Economics, Duke University, Durham, NC, USA, and CREATES)

  • Per Frederiksen

    (Equity Trading and Derivatives, Nordea Markets, Copenhagen, Denmark)

  • Morten �rregaard Nielsen

    (Department of Economics, Queen's University, Kingston, Ontario, Canada, and CREATES)

Abstract

We provide an empirical framework for assessing the distributional properties of daily speculative returns within the context of the continuous-time jump diffusion models traditionally used in asset pricing finance. Our approach builds directly on recently developed realized variation measures and non-parametric jump detection statistics constructed from high-frequency intra-day data. A sequence of simple-to-implement moment-based tests involving various transformations of the daily returns speak directly to the importance of different distributional features, and may serve as useful diagnostic tools in the specification of empirically more realistic continuous-time asset pricing models. On applying the tests to the 30 individual stocks in the Dow Jones Industrial Average index, we find that it is important to allow for both time-varying diffusive volatility, jumps, and leverage effects to satisfactorily describe the daily stock price dynamics. Copyright © 2009 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/jae.1105
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 25 (2010)
Issue (Month): 2 ()
Pages: 233-261

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Handle: RePEc:jae:japmet:v:25:y:2010:i:2:p:233-261
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Find related papers by JEL classification:
  • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
  • G1 - Financial Economics - - General Financial Markets

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