"A modern book on financial econometrics has to consider interesting and relevant topics from the viewpoint of recently developed techniques that have been shown to actually work. This book delivers in all aspects&The many new techniques displayed include the use of loss functions based on economic rather than statistical criteria, the benefits of combining forecasts, dimension reduction, structural changes, and long memory fractional cointegration, neural networks, and high frequency data techniques applied to exchange rates. The authors provide plenty to think about." –– Professor Clive W J Granger, University of California, San Diego
"This book is a welcome addition to the literature on forecasting in financial markets." –– Professor Ken Holden, Liverpool Business School
Developments in Forecast Combination and Portfolio Choice brings together papers that address current frontier research within the field of quantitative finance. Focusing on three core themes of model and forecast combinations; structural change and long memory; and controlling downside risk and investment strategies, the book provides an authoritative collection of readings that are relevant to academics and practitioners alike.
About the Contributors.
THEME I MODEL AND FORECAST COMBINATIONS
What Exactly Should We Be Optimising? Criterion Risk in Multicomponent and Multimodel Forecasting (A. Neil Burgess).
A Meta–parameter Approach to the Construction of Forecasting Models for Trading Systems (Neville Towers and A. Neil Burgess).
The Use of Market Data and Model Combination to Improve Forecast Accuracy (Christian L. Dunis, Jason Laws and Sté phane Chauvin).
21 Nonlinear Ways to Beat the Market (George T. Albanis and Roy A. Batchelor).
Predcting High Performance Stocks Using Dimensionality Reduction Techniques Based on Neural Networks (George T. Albanis and Roy A. Batchelor).
THEME II STRUCTURAL CHANGE AND LONG MEMEORY
Structural Change and Long Memory in Volatility: New Evidence from Daily Exchange Rates (Michel Beine and Sé bastien Laurent).
Long–run Volatility Dependencies in Intraday Data and Mixture of Normal Distributions (Auré lie Boubel and Sé bastien Laurent).
Comparison of Parameter Esitmation Methods in Cyclical Long Memory Time Series (Laurent Ferrara and Dominique Guegan).
THEME III CONTROLLING DOWNSIDE RISK AND INVESTMENT STRATEGIES
Building a Mean Downside Risk Portfolio Frontier (Gustavo M. de Athayde).
Implementing Discrete–Time Dynamic Investment Strategies with Downside Risk: A Comparison of Returns and Investment Policies (Mattias Persson).
Portfolio Optimisation in a Downside Risk Framework (Riccardo Bramante and Barbara Cazzaniga).
The Three–moment CAPM: Theoretical Foundations and an Asset Pricing Model Comparison in a Unified Framework (Emmanuel Jurczwnko and Bertrand Maillet).
Stress–testing Correlations: An Application to Portfolio Risk Management (Frederick Bourgoin.)
ALLAN TIMMERMANN is Professor of Economics at University of California, San Diego. He is on the editorial board of the Journal of Forecasting and Journal of Business and Economic Statistics. His research is concerned with modelling the dynamics and predictability of returns in financial markets. Professor Timmermann has held positions at Birkbeck College and the London School of Economics.
JOHN MOODY is the Director of the Computational Finance program and a Professor of Computer Science at the Oregon Graduate Institute. His research interests include computational finance, time series analysis and machine learning. Professor Moody has held positions at Yale University and the Institute for Theoretical Physics.