Nonlinear Modelling of High Frequency Financial Time Series

ISBN-13: 9780471974642 / Angielski / Twarda / 1998 / 320 str.

Dunis; Ping Zhou; Christian L. Dunis

Opis

The mathematical techniques and models used in the forecasting of financial markets grow ever more sophisticated - as books, traders, analysts and investors seek to gain an edge on their competitors. This text focuses on the issue of non-linear modelling of high frequency financial data. Non-linearity refers to situations in which there is a high degree of apparent randomness to the way in which a particular financial measure, price, interest rate, or exchange rate moves with time.

Spis treści

HIGH FREQUENCY MODELS IN FINANCE: MOTIVATIONS AND THEORETICAL ISSUES.

Modelling with High Frequency Data: A Growing Interest for Financial Economists and Fund Managers (M. Gavridis).

High Frequency Foreign Exchange Rates: Price Behavior Analysis and ′True Price′ Models (J. Moody & L. Wu).

DETECTING NONLINEARITIES IN HIGH FREQUENCY DATA: EMPIRICAL TESTS AND MODELLING IMPLICATIONS.

Testing Linearity with Information–Theoretic Statistics and the Bootstrap (F. Acosta).

Testing for Linearity: A Frequency Domain Approach (J. Drunat, et al.).

Stochastic or Chaotic Dynamics in High Frequency Financial Data (D. Guégan & L. Mercier).

F–consistency, De–volatization and Normalization of High Frequency Financial Data (B. Zhou).

PARAMETRIC MODELS FOR NONLINEAR FINANCIAL TIME SERIES.

High Frequency Financial Time Series Data: Some Stylized Facts and Models of Stochastic Volatility (E. Ghysels, et al.).

Modelling Short–term Volatility with GARCH and HARCH Models (M. Dacorogna, et al.).

High Frequency Switching Regimes: A Continuous–time Threshold Process (R. Dacco′ & S. Satchell).

Modelling Burst Phenomena: Bilinear and Autoregressive Exponential Models (J. Drunat, et al.).

NON–PARAMETRIC MODELS FOR NONLINEAR FINANCIAL TIME SERIES.

Application of Neural Networks to Forecast High Frequency Data: Foreign Exchange (P. Bolland, et al.).

An Application of Genetic Algorithms to High Frequency Trading Models: A Case Study (C. Dunis, et al.).

High Frequency Exchange Rate Forecasting by the Nearest Neighbours Method (H. Alexandre, et al.).

Index.

O autorze

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 (CIBEF). 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.

Blurb

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.