Riassunto
This volume contains selected papers that were presented at the International Conference COMPUTATIONAL FINANCE 1997 held at London Business School on December 15-17 1997. Formerly known as Neural Networks in the Capital Markets (NNCM), this series of meetings has emerged as a truly multi-disciplinary international conference and provided an international focus for innovative research on the application of a multiplicity of advanced decision technologies to many areas of financial engineering. It has drawn upon theoretical advances in financial economics and robust methodological developments in the statistical, econometric and computer sciences. To reflect its multi-disciplinary nature, the NNCM conference has adopted the new title COMPUTATIONAL FINANCE. The papers in this volume are organised in six parts. Market Dynamics and Risk, Trading and Arbitrage strategies, Volatility and Options, Term-Structure and Factor models, Corporate Distress Models and Advances on Methodology. This years' acceptance rate (38%) reflects both the increasing interest in the conference and the Programme Committee's efforts to improve the quality of the meeting year-on-year. I would like to thank the members of the programme committee for their efforts in refereeing the papers. I also would like to thank the members of the computational finance group at London Business School and particularly Neil Burgess, Peter Bolland, Yves Bentz, and Nevil Towers for organising the meeting.
Contenuti
Part 1: Market Dynamics and Risk. Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management; F.X. Diebold, et al. Stability Analysis and Forecasting Implications; J. del Hoyo, J.G. Llorente. Time-Varying Risk Premia; M. Steiner, S. Schneider. A Data Matrix to Investigate Independence, Over-Reaction and/or Shock Persistence in Financial Data; R. Dacco, S.E. Satchell. Forecasting, High-Frequency Exchange Rates Using Cross Bicorrelations in; C. Brooks, M. Hinich. Stochastic Lotka-Volterra Systems of Competing Auto-Catalytic Agents Lead Generically to Truncated Pareto Power Wealth Distribution, Truncated Levy-Stable Intermittent Market Returns, Clustered Volatility, Booms and Crashes; S. Solomon. Part 2: Trading and Arbitrage Strategies Controlling Nonstationarity in Statistical Arbitrage Using a Portfolio of Cointegration Models; A.N. Burgess. Non-Parametric Test for Nonlinear Cointegration; J. Breitung. Comments on `A Non-Parametric Test for Nonlinear Cointegration'; H. White. Reinforcement Learning for Trading Systems and Portfolios: Immediate and Future Rewards; J.E. Moody, et al. An Evolutionary Bootstrap Method for Selecting Dynamic Trading Strategies; B. LeBaron. Discussion on `An Evolutionary Bootstrap Method for Selecting Dynamic Trading Strategies'; A.S. Weigend. Multitask Learning in a Neural VEC Approach for Exchange Rate Forecasting; F. Rauscher. Selecting Relative Value Stocks with Nonlinear Cointegration; C. Kollias, K. Metaxas. Part 3: Volatility Modelling and Option Pricing. Option Pricing with Neural Networks and a Homogeneity Hint; R. Garcia, R. Gencay. Bootstrapping GARCH(1,1) Models; G. Maerker. Using Option Prices to Recover ProbabilityDistributions; F. Gonzales-Mirand, A.N. Burgess. Modelling Financial Time Series Using State-Space Models; J. Timmer, A.S. Weigend. Forecasting Properties of Neural Network Generated Volatility Estimates; P. Ahmed, S. Swidle. Interest Rates Structure Dynamics: A Non-Parametric Approach; M. Cottrell, et al. State Space ARCH: Forecasting Volatility with a Stochastic Coefficient Model; A. Veiga, et al. Part 4: Term Structure and Factor Models. Empirical Analysis of the Australian and Canadian Money Market Yield Curves: Results Using Panel Data; S.H. Babbs, K.B. Nowman. Time-Varying Factor Sensitivities in Equity Investment Management; Y. Bentz, J.T. Connor. Discovering Structure in Finance Using Independent Component Analysis; D. Back, A.S. Weigend. Fitting No Arbitrage Term Structure Models Using a Regularisation Term; N. Towers, J.T. Connor. Quantification of Sector Allocation in the German Stock Market; E. Steurer. Part 5: Corporate Distress Models. Predicting Corporate Financial Distress Using Quantitative and Qualitative Data: A Comparison of Traditional and Collapsible Neural Networks; Q. Booker, et al. Credit Assessment Using Evolutionary MLP Networks; E.F.F. Mendes, A. Carvalho. Exploring Corporate Bankruptcy with Two-Levels Self-Organising Map; K. Kiviluoto, P. Gergius. The Ex-Ante Classification of Take-Over Targets Using Neural Networks; D. Fairclough, J. Hunter. Part 6: Advances on Methodology &endash; Short Notes. Forecasting Non-Stationary Financial Data with oIIR-Filters and Composed Threshold Models; M. Wildi. Portfolio Optimisation with Cap Weight Restrictions; N. Wagner. Are Neural Network and Econometric Forecasts Good for Trading? Stochastic Variance M
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