Decision Technologies for Computational Finance

Proceedings of the fifth International Conference Computational Finance

Häftad, Engelska, 1998

Av Apostolos-Paul N. Refenes, Andrew N. Burgess, John E. Moody

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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.

Produktinformation

Utgivningsdatum1998-11-30
Mått160 x 240 x 27 mm
Vikt739 g
FormatHäftad
SpråkEngelska
SerieAdvances in Computational Management Science
Antal sidor479
FörlagKluwer Academic Publishers
ISBN9780792383093

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1: 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.- 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.- 2: Trading and Arbitrage Strategies.- Controlling Nonstationarity in Statistical Arbitrage Using a Portfolio of Cointegration Models.- Nonparametric Tests for Nonlinear Cointegration.- Comments on “A Nonparametric Test for Nonlinear Cointegration”.- Reinforcement Learning for Trading Systems and Portfolios: Immediate vs Future Rewards.- An Evolutionary Bootstrap Method for Selecting Dynamic Trading Strategies.- Discussion of “An Evolutionary Bootstrap Method for Selecting Dynamic Trading Strategies”.- Multi-Task Learning in a Neural Vector Error Correction Approach for Exchange Rate Forecasting.- Selecting Relative-Value Stocks with Nonlinear Cointegration.- 3: Volatility Modeling and Option Pricing.- Option Pricing with Neural Networks and a Homogeneity Hint.- Bootstrapping Garch(1,1) Models.- Using Illiquid Option Prices to Recover Probability Distributions.- Modeling 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.- 4: Term Structure and Factor Models.- EmpiricalAnalysis 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 at the German Stock Market.- 5: Corporate Distress Models.- Predicting Corporate Financial Distress Using Quantitative and Qualitative Data: A Comparison of Standard and Collapsible Neural Networks.- Credit Assessment Using Evolutionary MLP Natwork.- Exploring Corporate Bankruptcy with Two-Level Self-Organizing Map.- The Ex-Ante Classification of Takeover Targets Using Neural Networks.- 6: Advances on Methodology-Short Notes.- 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 Multitask 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 Optimized by Genetic Algorithms.- Exchange Rate Trading Using a Fast Retraining Procedure for Generalised RBF Networks.