Forecasting Financial Markets

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 Partial table of contents:

MODELLING WITH HIGH FREQUENCY DATA.

Forecasting Foreign Exchange Rates Subject to De-Volatilization (B.Zhou).

Dynamic Strategies: A Correlation Study (E. Acar & P.Lequeux).

THE INFORMATIONAL CONTENT OF VOLATILITY MARKETS.

Using Option Prices to Estimate Realignment Probabilities in theEuropean Monetary System (A. Malz).

Efficiency Test with Overlapping Data: An Application to theCurrency Options Market (C. Dunis & A. Keller).

APPLICATIONS OF NEURAL NETWORKS AND GENETIC ALGORITHMS.

The Use of Error Feedback Terms in Neural Network Modelling ofFinancial Time Series ( A. Burgess & A. Refenes).

An Evolutionary Algorithm for Portfolio Selection within a DownsideFramework ( A. Loraschi & A. Tettamanzi).

Index. 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. 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