Nonlinear modelling of high frequency financial time series

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Wiley, Oct 27, 1998 - Business & Economics - 300 pages
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In the competitive and risky environment of todays 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

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Contents

Price Behavior
23
Empirical
49
A Frequency Domain Approach
69
Copyright

10 other sections not shown

Common terms and phrases

About the author (1998)

Christian Dunis is Executive Vice President, Global Head of Markets Research at Banque Nationale de Paris, France. BNP's Markets Research Group covers foreign exchange and fixed income strategies, quantitative market research and quantitative trading. Its 23-strong research staff is spread between London, Paris and Singapore.