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Decision Technologies for Computational Finance : Proceedings of the fifth International Conference Computational Finance - Apostolos-Paul N. Refenes

Decision Technologies for Computational Finance

Proceedings of the fifth International Conference Computational Finance

By: Apostolos-Paul N. Refenes (Editor), Andrew N. Burgess (Editor), John E. Moody (Editor)


Published: 30th November 1998
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This volume contains selected papers that were presented at the International Conference "Computational Finance" held at the London Business School. Formerly known as "Neural Networks in the Capital Markets" (NNCM), this series of meetings has emerged as a multi-disciplinary international conference and provided an international focus for 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 organized in six parts: market dynamics and risk; trading and arbitrage strategies; volatility and options; term-structure and factor models; corporate distress models; and advances in methodology.

Market Dynamics and Risk
Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management
Stability Analysis and Forecasting Implications
Time-Varying Risk Premia
A Data Matrix to Investigate Independence, Over-Reaction and/or Shock Persistence in Financial Data
Forecasting, High-Frequency Exchange Rates Using Cross Bicorrelations in
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
Trading and Arbitrage Strategies Controlling Nonstationarity in Statistical Arbitrage Using a Portfolio of Cointegration Models
Non-Parametric Test for Nonlinear Cointegration
Comments on `A Non-Parametric Test for Nonlinear Cointegration''
Reinforcement Learning for Trading Systems and Portfolios: Immediate and Future Rewards
An Evolutionary Bootstrap Method for Selecting Dynamic Trading Strategies
Discussion on `An Evolutionary Bootstrap Method for Selecting Dynamic Trading Strategies''
Multitask Learning in a Neural VEC Approach for Exchange Rate Forecasting
Selecting Relative Value Stocks with Nonlinear Cointegration
Volatility Modelling and Option Pricing
Option Pricing with Neural Networks and a Homogeneity Hint
Bootstrapping GARCH(1,1) Models
Using Option Prices to Recover Probability Distributions
Modelling Financial Time Series Using State-Space Models
Forecasting Properties of Neural Network Generated Volatility Estimates
Interest Rates Structure Dynamics: A Non-Parametric Approach
State Space ARCH: Forecasting Volatility with a Stochastic Coefficient Model
Term Structure and Factor Models
Empirical Analysis of the Australian and Canadian Money Market Yield Curves: Results Using Panel Data
Time-Varying Factor Sensitivities in Equity Investment Management
Discovering Structure in Finance Using Independent Component Analysis
Fitting No Arbitrage Term Structure Models Using a Regularisation Term
Quantification of Sector Allocation in the German Stock Market
Corporate Distress Models
Predicting Corporate Financial Distress Using Quantitative and Qualitative Data: A Comparison of Traditional and Collapsible Neural Networks
Credit Assessment Using Evolutionary MLP Networks
Exploring Corporate Bankruptcy with Two-Levels Self-Organising Map
The Ex-Ante Classification of Take-Over Targets Using Neural Networks
Advances on Methodology endash
Forecasting Non-Stationary Financial Data with oIIR-Filters and Composed Threshold Models
Portfolio Optimisation with Cap Weight Restrictions
Are Neural Network and Econometric Forecasts Good for Trading? Stochastic Variance Models as a Filter Rule
Incorporating Prior Knowledge about Financial Markets through Neural Multi-Task Learning
Predicting Time-Series with a Committee of Independent Experts Based on Fuzzy Rules
Multiscale Analysis of Time-Series Based on a Neuro-Fuzzy Chaos Methodology Applied to Financial Data
On the Market Timing Ability of Neural Networks: An Empirical Study Testing the Forecasting Performance
Currency Forecasting Using Recurrent RBF Networks Optimised by Genetic Algorithms
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9780792383086
ISBN-10: 0792383087
Series: Advances in Computational Management Science
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 479
Published: 30th November 1998
Publisher: Springer
Country of Publication: NL
Dimensions (cm): 23.5 x 15.5  x 2.6
Weight (kg): 1.92