Nonlinear modelling of high frequency financial time series

Nonlinear modelling of high frequency financial time series

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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 seriesThis methodology, however, has no simple equivalent for multivariate time series, and the predictions of future prices cannot be related back to real time. The methodology we present ... The underlying states are updated even though this movement is not observed in the market data until the next tick occurs. The model is estimated by applying an augmented Kalman filter using a missing data framework.

Title:Nonlinear modelling of high frequency financial time series
Author: Christian Dunis, Bin Zhou
Publisher:John Wiley & Sons Inc - 1998-10-27

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