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Spot Volatility Estimation for High-Frequency Data: Adaptive Estimation in Practice

  • Till SabelEmail author
  • Johannes Schmidt-Hieber
  • Axel Munk
Conference paper
  • 1.7k Downloads
Part of the Lecture Notes in Statistics book series (LNS, volume 217)

Abstract

We develop further the spot volatility estimator introduced in Hoffmann et al. (Ann Inst H Poincaré (B) Probab Stat 48(4):1186–1216, 2012) from a practical point of view and make it applicable to the analysis of high-frequency financial data. In a first part, we adjust the estimator substantially in order to achieve good finite sample performance and to overcome difficulties arising from violations of the additive microstructure noise model (e.g. jumps, rounding errors). These modifications are justified by simulations. The second part is devoted to investigate the behavior of volatility in response to macroeconomic events. We give evidence that the spot volatility of Euro-BUND futures is considerably higher during press conferences of the European Central Bank. As an outlook, we present an estimator for the spot covolatility of two different prices.

Keywords

Wavelet Coefficient European Central Bank Microstructure Effect Mean Integrate Square Error Heston Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Support of DFG/SNF-Grant FOR 916, DFG postdoctoral fellowship SCHM 2807/1-1, and Volkswagen Foundation is gratefully acknowledged. We appreciate the help of CRC 649 “Economic Risk” for providing us with access to Eurex database. Parts of this work are taken from the PhD thesis Schmidt-Hieber [48]. We thank Marc Hoffmann, Markus Reiß, Markus Bibinger, and an anonymous referee for many helpful remarks and suggestions.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Till Sabel
    • 1
    Email author
  • Johannes Schmidt-Hieber
    • 2
  • Axel Munk
    • 1
  1. 1.Department of Mathematics and Computer Science, Institute for Mathematical StochasticsGeorg-August-University GöttingenGöttingenGermany
  2. 2.University of LeidenLeidenNetherlands

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