Applications of Point Processes in Empirical Economics and Finance

Standard statistical methods in the empirical economics and finance literature are mostly applicable to data that is aggregated on equally spaced time points. However, a key characteristic of many economic and financial variables is that they occur randomly and are observed irregularly in time. Since the pathbreaking work of Robert Engle in the last years of the twentieth century, there are new approaches that do not require aggregated data but are able to account for their irregular timing nature.These developments were mainly supported by the increasing availability of high frequency transaction data due to the implementation of electronic order recording systems at stock exchanges all over the world. Typically, financial markets data are irregularly observed along the time axis. As pointed out by some authors, time series analysis of fixed time interval data annihilates the natural timing dependence of transaction data and possibly neglects relevant information. Further, the selection of inappropriate equidistant aggregation schemes and the exclusion of data points might lead to misspecifications. Hence, the inclusion of all events in an empirical analysis provides additional information about the timing relation of transaction variables and allows to revisit old and to analyze new questions delivered by financial markets theory. The statistical modeling framework to account for characteristics of irregularly spaced event data is provided by the theory of point processes. A point process statistically describes the history of events that occur consecutively in time. A process consisting of points at which we simultaneously observe variables that mark the points is conceived as marked point process.This thesis' aim is to present new univariate and multivariate empirical point processes applied in the field of financial and monetary econometrics. In particular, this thesis analyzes the following topics. In the second chapter, we suggest a univariate discrete marked point process model for the federal funds rate target and investigate its point and probability forecast performance. Chapter three presents a model for daily return variation. Total daily return variation is disentangled into a continuous and jump variation component. While daily continuous variation is modeled by an autoregressive conditional time series model, irregularly occurring jumps are conceived as a univariate marked point process. Finally, the fourth chapter introduces a new information share that measures the home and foreign market share in price discovery. For this purpose, a multivariate point process based on high frequency transaction data is used.