High-Frequency Statistics with Asynchronous and Irregular Data

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. Contents

• Laws of Large Numbers
• Random Observation Schemes
• Bootstrapping Asymptotic Laws

• Testing for (Common) JumpsTarget Groups

• Scientists andstudents in the field of mathematical statistics, econometrics and financial mathematics
• Practitioners in the field of financial mathematicsAbout the AuthorDr. 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.

Autorentext
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.

Inhalt
Laws of Large Numbers.- Random Observation Schemes.- Bootstrapping Asymptotic Laws.- Testing for (Common) Jumps.