High-Frequency Statistics with Asynchronous and Irregular Data

  • Ole┬áMartin

Table of contents

  1. Front Matter
    Pages I-XIII
  2. Theory

    1. Front Matter
      Pages 1-1
    2. Ole Martin
      Pages 3-8
    3. Ole Martin
      Pages 9-71
    4. Ole Martin
      Pages 73-109
    5. Ole Martin
      Pages 111-135
    6. Ole Martin
      Pages 137-153
  3. Applications

    1. Front Matter
      Pages 155-155
    2. Ole Martin
      Pages 157-174
    3. Ole Martin
      Pages 175-205
    4. Ole Martin
      Pages 207-240
    5. Ole Martin
      Pages 241-303
  4. Back Matter
    Pages 305-323

About this book


Ole Martin extends well-established techniques for the analysis of high-frequency data based on regular observations to the more general setting of asynchronous and irregular observations. Such methods are much needed in practice as real data usually comes in irregular form. In the theoretical part he develops laws of large numbers and central limit theorems as well as a new bootstrap procedure to assess asymptotic laws. The author then applies the theoretical results to estimate the quadratic covariation and to construct tests for the presence of common jumps. The simulation results show that in finite samples his methods despite the much more complex setting perform comparably well as methods based on regular data.

  • Laws of Large Numbers 
  • Random Observation Schemes
  • Bootstrapping Asymptotic Laws
  • Testing for (Common) Jumps
Target Groups
  • Scientists and students in the field of mathematical statistics, econometrics and financial mathematics
  • Practitioners in the field of financial mathematics
About the Author
Dr. Ole Martin completed his PhD at the Kiel University (CAU), Germany. His research focuses on high-frequency statistics for semimartingales with the aim to develop methods based on irregularly observed data.


High-frequency statistics Asynchronous data Irregular data Asynchronous observations Random observations Quadratic covariation Test for jumps Test for common jumps Bootstrap Laws of large numbers Central limit theorems Bootstrapping asymptotic laws Random observation schemes Estimating quadratic covariation Common jumps

Authors and affiliations

  • Ole┬áMartin
    • 1
  1. 1.Department of MathematicsKiel University (CAU)KielGermany

Bibliographic information

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