High-Frequency Statistics with Asynchronous and Irregular Data [electronic resource] / by Ole Martin.

By: Martin, Ole [author.]Contributor(s): SpringerLink (Online service)Material type: Text

TextSeries: Mathematische Optimierung und Wirtschaftsmathematik | Mathematical Optimization and EconomathematicsPublisher: Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Spektrum, 2019Edition: 1st ed. 2019Description: XIII, 323 p. 34 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783658284183Subject(s): Probabilities | Statistics | Economics, Mathematical | Probability Theory and Stochastic Processes | Statistics for Business, Management, Economics, Finance, Insurance | Quantitative FinanceAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 519.2 LOC classification: QA273.A1-274.9QA274-274.9Online resources: Click here to access online

Contents:

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

In: Springer eBooksSummary: 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) 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.

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

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) 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.