Abstract
The availability of financial data recorded on high-frequency level has inspired a research area which over the last decade emerged to a major area in econometrics and statistics. The growing popularity of high-frequency econometrics is triggered by technological progress in trading systems and trade recording as well as an increasing importance of intraday trading, optimal trade execution, order placement and liquidity dynamics. Technological progress and the growing dominance of electronic trading allows to record market activity on high frequency and with high precision leading to advanced and comprehensive data sets. The informational limiting case is reached when all market events, e.g., in form of order messages, are recorded. Such recording schemes result in data bases which are even more detailed than transaction data and allow to reproduce the entire order flow as well as the underlying order book.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aït-Sahalia Y, Mykland P, Zhang L (2005) How often to sample a continuous-time process in the presence of market microstructure noise. Rev Financ Stud pp. 351–416
Barndorff-Nielsen O, Hansen P, Lunde A, Shephard N (2008a) Designing realized kernels to measure the ex-post variation of equity prices in the presence of noise. Econometrica 76:1481–1536
Bauwens L, Giot P (2000) The logarithmic ACD model: an application to the bid/ask quote process of two NYSE stocks. Annales d’Economie et de Statistique 60:117–149
Bauwens L, Hautsch N (2006) Stochastic conditional intensity processes. J Financ Econom 4:450–493
Carrasco M, Chen X (2002) Mixing and moment properties of various GARCH and stochastic volatility models. Econom Theory 18(1):17–39
Cox DR, Isham V (1980) Point processes. Chapman and Hall, London
Engle RF (2000) The econometrics of ultra-high-frequency data. Econometrica 68(1):1–22
Engle RF (2002) New frontiers for ARCH models. J Appl Econom 17:425–446
Engle RF, Russell JR (1998) Autoregressive conditional duration: a new model for irregularly spaced transaction data. Econometrica 66:1127–1162
Gerhard F, Hautsch N (2007) A dynamic semiparametric proportional hazard model. Stud Nonlinear Dynamics Econometrics 11, http://www.bepress.com/snde/vol11/iss2/art1
Ghysels E, Jasiak J (1998) GARCH for irregularly spaced financial data: The ACD-GARCH model. Stud Nonlinear Dynamics Econometrics 2:133–149
Grandell J (1976) Doubly stochastic poisson processes. Springer, Berlin
Hasbrouck J (1991) Measuring the information content of stock trades. J Finance 46:179–207
Hautsch N (2004) Modelling irregularly spaced financial data – theory and practice of dynamic duration models vol. 539 of Lecture Notes in Economics and Mathematical Systems. Springer, Berlin
Hawkes AG (1971) Spectra of some self-exciting and mutually exciting point processes. Biometrika 58:83–90
Jacod J, Li Y, Mykland P, Podolskij M, Vetter M (2009) Microstructure noise in the continuous case: the pre-averaging approach. Stochastic Process Appl 119:2249–2276
Russell JR (1999) Econometric modeling of multivariate irregularly-spaced high-frequency data. Working Paper, University of Chicago
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Hautsch, N. (2012). Introduction. In: Econometrics of Financial High-Frequency Data. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21925-2_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-21925-2_1
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21924-5
Online ISBN: 978-3-642-21925-2
eBook Packages: Business and EconomicsEconomics and Finance (R0)