{"product_id":"nonlinear-modelling-of-high-frequency-financial-time-series-isbn-9780471974642","title":"Nonlinear Modelling of High Frequency Financial Time Series","description":"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. HIGH FREQUENCY MODELS IN FINANCE: MOTIVATIONS AND THEORETICAL ISSUES.\u003cbr\u003e \u003cbr\u003e Modelling with High Frequency Data: A Growing Interest for Financial Economists and Fund Managers (M. Gavridis).\u003cbr\u003e \u003cbr\u003e High Frequency Foreign Exchange Rates: Price Behavior Analysis and 'True Price' Models (J. Moody \u0026amp; L. Wu).\u003cbr\u003e \u003cbr\u003e DETECTING NONLINEARITIES IN HIGH FREQUENCY DATA: EMPIRICAL TESTS AND MODELLING IMPLICATIONS.\u003cbr\u003e \u003cbr\u003e Testing Linearity with Information-Theoretic Statistics and the Bootstrap (F. Acosta).\u003cbr\u003e \u003cbr\u003e Testing for Linearity: A Frequency Domain Approach (J. Drunat, et al.).\u003cbr\u003e \u003cbr\u003e Stochastic or Chaotic Dynamics in High Frequency Financial Data (D. Guégan \u0026amp; L. Mercier).\u003cbr\u003e \u003cbr\u003e F-consistency, De-volatization and Normalization of High Frequency Financial Data (B. Zhou).\u003cbr\u003e \u003cbr\u003e PARAMETRIC MODELS FOR NONLINEAR FINANCIAL TIME SERIES.\u003cbr\u003e \u003cbr\u003e High Frequency Financial Time Series Data: Some Stylized Facts and Models of Stochastic Volatility (E. Ghysels, et al.).\u003cbr\u003e \u003cbr\u003e Modelling Short-term Volatility with GARCH and HARCH Models (M. Dacorogna, et al.).\u003cbr\u003e \u003cbr\u003e High Frequency Switching Regimes: A Continuous-time Threshold Process (R. Dacco' \u0026amp; S. Satchell).\u003cbr\u003e \u003cbr\u003e Modelling Burst Phenomena: Bilinear and Autoregressive Exponential Models (J. Drunat, et al.).\u003cbr\u003e \u003cbr\u003e NON-PARAMETRIC MODELS FOR NONLINEAR FINANCIAL TIME SERIES.\u003cbr\u003e \u003cbr\u003e Application of Neural Networks to Forecast High Frequency Data: Foreign Exchange (P. Bolland, et al.).\u003cbr\u003e \u003cbr\u003e An Application of Genetic Algorithms to High Frequency Trading Models: A Case Study (C. Dunis, et al.).\u003cbr\u003e \u003cbr\u003e High Frequency Exchange Rate Forecasting by the Nearest Neighbours Method (H. Alexandre, et al.).\u003cbr\u003e \u003cbr\u003e Index.  \u003cp\u003e\u003cstrong\u003eCHRISTIAN L. DUNIS\u003c\/strong\u003e 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.  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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989695709413,"sku":"NP9780471974642","price":195.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780471974642.jpg?v=1761785138","url":"https:\/\/k12savings.com\/es\/products\/nonlinear-modelling-of-high-frequency-financial-time-series-isbn-9780471974642","provider":"K12savings","version":"1.0","type":"link"}