Studies in Computational Finance – serie

The contributors to this volume assert that dynamical systems theory and related nonlinear methods have had a major impact on the analysis of time series data from complex systems. Contemporary developments in mathematical methods of state-space reconstruction, time-delay embedding and surrogate data analysis, coupled with readily accessible and powerful computational facilities used in gathering and processing massive quantities of high-frequency data, have provided theorists and practitioners with opportunities for exploratory data analysis, modelling, forecasting and control. This book brings together an accessible set of chapters that deal with the application of nonlinear dynamics and associated algorithms to the study of economies and markets as complex systems. To make such methods readily useful in practice, the contributors have agreed to make available to readers upon request all computer programs used to implement the methods discussed in their respective chapters.

This text contains selected articles which, for the most part, were presented at the "Forecasting Financial Markets" Conference. "Forecasting Financial Markets" is an international conference on quantitative finance which is held in London in May every year. Since its inception in 1994, the conference has grown in scope and stature to become a key international meeting point for those interested in quantitative finance, with the participation of prestigious academic and research institutions from all over the world, including major central banks and quantitative fund managers. The editor has chosen to concentrate on advances in quantitative asset management and, accordingly, the papers in this book are organized around two major themes: advances in asset allocation and portfolio management, and modelling risk, return and correlation.

Modelling and Forecasting Financial Data brings together a coherent and accessible set of chapters on recent research results on this topic. To make such methods readily useful in practice, the contributors to this volume have agreed to make available to readers upon request all computer programs used to implement the methods discussed in their respective chapters. Modelling and Forecasting Financial Data is a valuable resource for researchers and graduate students studying complex systems in finance, biology, and physics, as well as those applying such methods to nonlinear time series analysis and signal processing.