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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.
From the Publisher
The mathematical techniques and models used in the forecasting of financial markets is growing more sophisticated than ever. This book focuses on the specific and highly topical issue of non-linear modelling high frequency financial data. It examines the exact nature of the time series considered and then models and forecasts these series by discriminating between the use of parametric and nonparametric (nonlinear) models. This book explores non-linearity as it refers to situations in which there is a high degree of apparent randomness to the way a particular financial measure--price, interest rate, exchange rate--moves with time.
See all Editorial Reviews
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.
From the Publisher
The mathematical techniques and models used in the forecasting of financial markets is growing more sophisticated than ever. This book focuses on the specific and highly topical issue of non-linear modelling high frequency financial data. It examines the exact nature of the time series considered and then models and forecasts these series by discriminating between the use of parametric and nonparametric (nonlinear) models. This book explores non-linearity as it refers to situations in which there is a high degree of apparent randomness to the way a particular financial measure--price, interest rate, exchange rate--moves with time.
See all Editorial Reviews
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12
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- Applications of Artificial Intelligence in Finance and Economics, Volume 19 (Advances in Econometrics) (Advances in Econometrics) by J.M. Binner on page 40
- An Introduction to High-Frequency Finance by Michel M. Dacorogna in Back Matter
- Derivatives and Internal Models by Hans-Peter Deutsch in Back Matter
- Physics of Finance: Gauge Modelling in Non-Equilibrium Pricing by Kirill Ilinski in Back Matter
- Statistics and Finance: An Interface by Wai-Sum Chan on page 140
See all 12 books citing this book
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