Analysis of financial time series data by using point process models

Abstract

In these decades, high-frequency financial data, which are big data containing detailed information on each transaction, have become available, and there has been growing needs for methods to analyze such data. Recently, point processes have been recognized as a useful tool to analyze high-frequency data. Point processes are probability models of timings of occurrences of events, and Hawkes processes, a type of point processes, can reproduce actually observed occurrence patterns of transactions or orders. In this article, we introduce a basic concept of point processes, and review our recent study on high-frequency financial data analysis using Hawkes processes.