Computational Finance 1999

Overview

Computational finance, an exciting new cross-disciplinary research area, draws extensively on the tools and techniques of computer science, statistics, information systems, and financial economics. This book covers the techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. These methods are applied to a wide range of problems in finance, including risk management, asset allocation, style analysis, dynamic trading and ...

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Overview

Computational finance, an exciting new cross-disciplinary research area, draws extensively on the tools and techniques of computer science, statistics, information systems, and financial economics. This book covers the techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. These methods are applied to a wide range of problems in finance, including risk management, asset allocation, style analysis, dynamic trading and hedging, forecasting, and option pricing. The book is based on the sixth annual international conference Computational Finance 1999, held at New YorkUniversity's Stern School of Business.

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Product Details

  • ISBN-13: 9780262511070
  • Publisher: MIT Press
  • Publication date: 4/24/2000
  • Pages: 733
  • Product dimensions: 6.90 (w) x 9.00 (h) x 1.40 (d)

Meet the Author

Blake LeBaron is Assistant Professor of Economics at the University of Wisconsin,Madison.

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Table of Contents

Preface
Contributors
Introduction 1
Importance Sampling and Stratification for Value-at-Risk 7
Confidence Intervals and Hypothesis Testing for the Sharpe and Treynor Performance Measures: A Bootstrap Approach 25
Conditional Value at Risk 41
Advances in Importance Sampling 53
Arbitrage and the APT - A Note 67
Bayesian Network Models of Portfolio Risk and Return 87
Change of Measure in Monte Carlo Integration via Gibbs Sampling with an Application to Stochastic Volatility Models 109
Comparing Models of Intra-day Seasonal Volatility in the Foreign Exchange Market 125
A Symbolic Dynamics Approach to Volatility Prediction 153
Does Volatility Timing Matter? 153
Goodness of Fit, Stability and Data Mining 173
A Bayesian Approach to Estimating Mutual Fund Returns 189
Independent Component Ordering in ICA Analysis of Financial Data 201
Curved Gaussian Models with Application to Modeling Foreign Exchange Rates 213
Nonparametric Efficiency Testing of Asian Foreign Exchange Markets 229
Term Structure of Interactions of Foreign Exchange Rates 247
Exchange Rates and Fundamentals: Evidence from Out-of-Sample Forecasting Using Neural Networks 267
Trading Models as Specification Tools 285
Statistical Arbitrage Models of the FTSE 100 297
Implementing Trading Strategies for Forecasting Models 313
Using Nonlinear Neurogenetic Models with Profit Related Objective Functions to Trade the US T-bond Future 327
Parameter Tuning in Trading Algorithms Using ASTA 343
Hedge Funds Styles 359
Optimization of Technical Trading Strategy Using Split Search Genetic Algorithms 369
Trading Mutual Funds with Piece-wise Constant Models 387
Minimizing Downside Risk via Stochastic Dynamic Programming 403
An Optimal Binary Predictor for an Investor in a Futures Market 417
An Introduction to Risk Neutral Forecasting 433
Temporal-Difference Learning and Applications in Finance 447
Technical Trading Creates a Prisoner's Dilemma: Results from an Agent-Based Model 465
Cycles of Market Stability and Instability Due to Endogenous Use of Technical Trading Rules 481
Relative Performance of Incentive Mechanisms in Delegated Investments: A Computational Study 495
Rules Extractions from Banks' Bankrupt Data Using Connectionist and Symbolic Learning Algorithms 515
Evaluating Bank Lending Policy and Consumer Credit Risk 535
Loan Duration and Bank Lending Policy 549
Estimation of Stochastic Volatility Models for the Purpose of Option Pricing 567
Option Pricing via Genetic Programming 583
Nonparametric Testing of ARCH for Option Pricing 599
A Computational Framework for Contingent Claim Pricing and Hedging under Time Dependent Asset Processes 613
A Framework for Comparative Analysis of Statistical and Machine Learning Methods: An Application to the Black-Scholes Option Pricing Equation 635
Option Pricing with the Efficient Method of Moments 661
Option Valuation with the Genetic Programming Approach 689
Contact Information 705
Keyword Index 709
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