Today s financial markets are characterised by a large number ofparticipants, with different appetites for risk, different timehorizons, different motivations and reactions to unexpected news.The mathematical techniques and models used in the forecasting offinancial markets have therefore grown ever more sophisticated astraders, analysts and investors seek to gain an edge on theircompetitors. Written by leading international researchers andpractitioners, this book focuses on three major themes of today sstate of the art financial research: modelling with high frequencydata, the information content of volatility markets, andapplications of neural networks and genetic algorithms to financialtime series. Forecasting Financial Markets includes empiricalapplications to present the very latest thinking on these complextechniques, including: * High frequency exchange rates
* Intraday volatility
* Autocorrelation and variance ratio tests
* Conditional volatility
* GARCH processes
* Chaotic systems
* Nonlinearity
* Stochastic and EXPAR models
* Artificial neural networks
* Genetic algorithms
About the Author
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.
