Description
Book SynopsisThis volume focuses on the following three themes: model and forecast combinations; structural change and long memory; and controlling downside risk and investment strategies.
Table of ContentsContributors.
About the Contributors.
Series Preface.
Preface
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.)
Index.
