Econometric Tools for Financial High-Frequency Data

Methods and models for high-frequency data are of growing importance in financial practice. Important tasks are high-frequency predictions of trading volumes, market depth, bid-ask spreads and trading costs to optimize order placement and order execution. Berlin researchers made significant contributions in this area. Groß-Klußmann and Hautsch (2011a) introduce a long memory autoregressive Poisson model to predict highly persistent processes of counts. They successfully apply the model to predict bid-ask spreads. Likewise, the setting can be employed to forecast market activity, e.g., the number of order arrivals, in equi-distant intervals. Hautsch, Schienle and Malec (2010) propose a novel approach to model serially dependent positive-valued variables which realize a non-trivial proportion of zero outcomes. This is a typical phenomenon in financial time series observed at high frequencies. Applying the framework to high-frequency trading volumes, they show that the introduced framework produces very accurate density forecasts.

Advances in high-frequency order book modeling have been made by Härdle, Hautsch and Mihoci (2009) who introduce a semiparametric dynamic factor approach to model high-dimensional order book curves.

Studying the impact of news on high-frequency market dynamics is of ongoing interest and particularly inspired by the increasingly important role of automated news feeds. Groß-Klußmann and Hautsch (2011b) analyze the impact of intra-day market reactions to news in stock-specific sentiment disclosures using pre-processed data from an automated news analytics tool based on linguistic pattern recognition. They provide evidence for significant reactions in volatility and trading activity after the arrival of news items which are indicated to be relevant. Hautsch, Hess and Veredas (2011) study the impact of the arrival of macroeconomic news on the informational and noise-driven components in high-frequency quote processes and their conditional variances.

Selected Publications