Nonlinear Modelling of High Frequency Financial Time Series

Nonlinear Modelling of High Frequency Financial Time Series Edited by Christian Dunis and Bin Zhou In the competitive and risky environment of today's financial markets, daily prices and models based upon low frequency price series data do not provide the level of accuracy required by traders and a growing number of risk managers. To improve results, more and more researchers and practitioners are turning to high frequency data. Nonlinear Modelling of High Frequency Financial Time Series presents the latest developments and views of leading international researchers and market practitioners, in modelling high frequency data in finance. Combining both nonlinear modelling and intraday data for financial markets, the editors provide a fascinating foray into this extremely popular discipline. This book evolves around four major themes. The first introductory section focuses on high frequency financial data. The second part examines the exact nature of the time series considered: several linearity tests are presented and applied and their modelling implications assessed. The third and fourth parts are dedicated to modelling and forecasting these financial time series.

CHRISTIAN L. DUNIS is Girobank Professor of Banking and Finance at Liverpool Business School, and Director of its Centre for International Banking, Economics and Finance. He is also a consultant to asset management firms, a Visiting Professor of International Finance at Venice International University and an Official Reviewer attached to the European Commission for the evaluation of applications to finance of emerging software technologies. He is an Editor of the European Journal of Finance, and has widely published in the field of financial markets analysis and forecasting. He has organised the Forecasting Financial Markets Conference since 1994.