Econometric Analysis of High Frequency Data

Helmut Herwartz ()
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Helmut Herwartz: Christian Albrechts-Universität zu Kiel

Chapter 7 in Modern Econometric Analysis, 2006, pp 87-102 from Springer

Abstract: Abstract Owing to enormous advances in data acquisition and processing technology the study of high (or ultra) frequency data has become an important area of econometrics. At least three avenues of econometric methods have been followed to analyze high frequency financial data: Models in tick time ignoring the time dimension of sampling, duration models specifying the time span between transactions and, finally, fixed time interval techniques. Starting from the strong assumption that quotes are irregularly generated from an underlying exogeneous arrival process, fixed interval models promise feasibility of familiar time series techniques. Moreover, fixed interval analysis is a natural means to investigate multivariate dynamics. In particular, models of price discovery are implemented in this venue of high frequency econometrics. Recently, a sound statistical theory of ‘realized volatility’ has been developed. In this framework high frequency log price changes are seen as a means to observe volatility at some lower frequency.

Keywords: Price Discovery; Stochastic Volatility Model; Vector Error Correction Model; High Frequency Data; Quasi Maximum Likelihood (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-32693-9_7

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DOI: 10.1007/3-540-32693-6_7

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