Today? s financial markets are characterised by a large number of participants, with different appetites for risk, different time horizons, different motivations and reactions to unexpected news. The mathematical techniques and models used in the forecasting of financial markets have therefore grown ever more sophisticated as traders, analysts and investors seek to gain an edge on their competitors. Written by leading international researchers and practitioners, this book focuses on three major themes of today? s state of the art financial research: modelling with high frequency data, the information content of volatility markets, and applications of neural networks and genetic algorithms to financial time series. Forecasting Financial Markets includes empirical applications to present the very latest thinking on these complex techniques, 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.
