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Analyzing the Spectrum of Asset Returns: Jump and Volatility Components in High Frequency Data

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  • Yacine A�t-Sahalia
  • Jean Jacod
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    Abstract

    This paper reports some of the recent developments in the econometric analysis of semimartingales estimated using high frequency financial returns. It describes a simple yet powerful methodology to decompose asset returns sampled at high frequency into their base components (continuous, small jumps, large jumps), determine the relative magnitude of the components, and analyze the finer characteristics of these components such as the degree of activity of the jumps. We incorporate to effect of market microstructure noise on the test statistics, apply the methodology to high frequency individual stock returns, transactions and quotes, stock index returns and compare the qualitative features of the estimated process for these different data and discuss the economic implications of the results.( JEL C58, G12, G13)

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    File URL: http://www.aeaweb.org/articles.php?doi=10.1257/jel.50.4.1007
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    Bibliographic Info

    Article provided by American Economic Association in its journal Journal of Economic Literature.

    Volume (Year): 50 (2012)
    Issue (Month): 4 (December)
    Pages: 1007-50

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    Handle: RePEc:aea:jeclit:v:50:y:2012:i:4:p:1007-50

    Note: DOI: 10.1257/jel.50.4.1007
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    Citations

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    Cited by:
    1. Yacine Ait-Sahalia & Jianqing Fan & Yingying Li, 2011. "The Leverage Effect Puzzle: Disentangling Sources of Bias at High Frequency," NBER Working Papers 17592, National Bureau of Economic Research, Inc.
    2. Lars Winkelmann, 2013. "Quantitative forward guidance and the predictability of monetary policy - A wavelet based jump detection approach -," SFB 649 Discussion Papers SFB649DP2013-016, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Deniz Erdemlioglu & Sébastien Laurent & Christopher J. Neely, 2013. "Which continuous-time model is most appropriate for exchange rates?," Working Papers 2013-024, Federal Reserve Bank of St. Louis.
    4. Vortelinos, Dimitrios I., 2014. "Optimally sampled realized range-based volatility estimators," Research in International Business and Finance, Elsevier, vol. 30(C), pages 34-50.

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