Dynamic Modelling of Irregular Times, Prices and Volumes at High Frequencies
Author(s)
Shenai, Nikhil
Type
Thesis
Abstract
This thesis undertakes an investigation into time series at high frequency. The three main
channels of information in high frequency data - irregular time intervals (durations), prices
and volumes - are all explored and modelled to improve current understanding, while accounting
for the long memory property, a crucial stylised fact found in the literature. In
doing so, we make use of the theory of point processes, econometric techniques such as
Whittle estimation and Kalman Filter forecasting, and also sophisticated computing architecture
including database systems and programming languages across multiple software
environments.
channels of information in high frequency data - irregular time intervals (durations), prices
and volumes - are all explored and modelled to improve current understanding, while accounting
for the long memory property, a crucial stylised fact found in the literature. In
doing so, we make use of the theory of point processes, econometric techniques such as
Whittle estimation and Kalman Filter forecasting, and also sophisticated computing architecture
including database systems and programming languages across multiple software
environments.
Date Issued
2012
Date Awarded
2012-03
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Zaffaroni, Paolo
Creator
Shenai, Nikhil
Publisher Department
Imperial College Business School
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)