Decision Technologies for Computational Finance: Proceedings of the fifth International Conference Computational Finance

472 Pages·1998·15.216 MB·English

by  Francis X. Diebold, Til Schuermann, John D. Stroughair (auth.), Apostolos-Paul N. Refenes, Andrew N. Burgess, John E. Moody (eds.)

DECISION TECHNOLOGIES FOR COMPUTATIONAL FINANCE
Advances in Computational Management Science
VOLUME 2
SERIES EDITORS
Hans Amman, University ofA msterdam, The Netherlands
Berc Rustem, Imperial College, London, U.K.
EDITORIAL BOARD
Christophe Deissenberg, University ofN antes, France
Arthur Farley, University of Oregon, U.SA.
Manfred Gilli, University ofG eneva, Switzerland
David Kendrick, University of Texas at Austin, U.S.A.
David Luenberger, Stanford University, U.S.A.
Rik Maes, University ofA msterdam, The Netherlands
Istvan Maros, Imperial College, U.K.
John Mulvey, Princeton University, U.SA.
Anna Nagurney, University ofM assachusetts at Amherst, USA.
Soren Nielsen, University of Texas at Austin, U.SA.
Louis Pau, Ericsson, Alvsjo, Sweden
Edison Tse, Stanford University, U.S.A.
Andy Whinston, University of Texas at Austin, U.S.A.
The titles published in this series are listed at the end oft his volume.
Decision Technologies
for Cotnputational Finance
Proceedings of the fifth International Conference
Computational Finance
Edited by
Apostolos-Paul N. Refenes
London Business School, U.K.
Andrew N. Burgess
London Business School, U.K.
and
John E. Moody
Oregon Graduate Institute,
Portland, U.S.A.
SPR1NGER-SCIENCE+BUSINESS MEDIA, B.V.
A c.I.P. CataIogue record for this book is available from the Library of Congress.
ISBN 978-0-7923-8309-3 ISBN 978-1-4615-5625-1 (eBook)
DOI 10.1007/978-1-4615-5625-1
Printed on acid-free paper
Ali Rights Reserved
© 1998 Springer Science+Business Media Dordrecht
Originally published by Kluwer Academic Publishers in 1998
Softcover reprint of the hardcover 1s t edition 1998
No part of the material protected by this copyright notice may be reproduced or
utilized in any form or by any means, electronic or mechanical,
including photocopying, recording or by any information storage and
retrieval system, without written permission from the copyright owner
COMPUTATIONAL FINANCE 1997
Programme Committee
Dr A. Refenes, London Business School (Chairman)
Dr Y. Abu-Mostafa, Caltech
Dr A. Atiya, Cairo University
Dr N. Biggs, London School of Economics
Dr J. Cowan, Chicago University
Dr R. Gencay, University of Windsor
Dr M. Jabri, Sydney University
Dr B. LeBaron, University of Wisconsin
Dr A. Lo, MIT Sloan School
Dr J. Moody, Oregon Graduate Institute
Dr C. Pedreira, Catholic University, PUC-Rio
Dr M. Steiner, Augsburg Universitaet
Dr D. Tavella, Align Risk Analysis
Dr A. Timermann, UCSD
Dr A. Weigend, New York University
Dr H. White, UCSD
Dr L. Xu, Chinese University, Hong Kong
v
FOREWORD
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.
Apostolos-Paul N. Refenes
Chair, COMPUTATIONAL FINANCE 1997
vii
CONTENTS
PART 1: MARKET DYNAMICS AND RISK
Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management
F. X Diebold, T. Schuermann, J. D. Stroughair 3
Stability Analysis and Forecasting implications
J. del Hoyo, J. G. Llorente 13
Time-Varying Risk Premia
M Steiner, S. Schneider 25
A Data Matrix to Investigate Independence, Over Reaction and/or Shock Persistence in Financial
Data
R. Dacco, S.E. Satchell 49
Forecasting High Frequency Exchange Rates Using Cross-Bicorrelations
C. Brooks, M Hinich 61
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 73
PART 2: TRADING AND ARBITRAGE STRATEGIES
Controlling Nonstationarity in Statistical Arbitrage Using a Portfolio of Cointegration Models
A. N. Burgess 89
Nonparametric Tests for Nonlinear Cointegration
J. Breitung 109
Comments on "A Nonparametric test for nonlinear cointegration"
H. White 125
Reinforcement Learning for Trading Systems and Portfolios: Immediate vs Future Rewards
J.E. Moody, M Saffell, Y. Liao, L. Wu 129
An Evolutionary Bootstrap Method for Selecting Dynamic Trading Strategies
B. LeBaron 141
Discussion of "An Evolutionary Bootstrap Method for Selecting Dynamic Trading Strategies"
A. S. Weigend 161
Multi-Task Learning in a Neural Vector Error Correction Approach for Exchange Rate
Forecasting
F. A. Rauscher 165
Selecting Relative-Value Stocks with Nonlinear Cointegration
C. Kollias, K. Metaxas 181
ix
x
PART 3: VOLATILITY MODELING AND OPTION PRICING
Option Pricing with Neural Networks and a Homogeneity Hint
R. Garcia, R. Genr;ay 195
Bootstrapping GARCH(l,I) Models
G. Maercker 207
Using Illiquid Option Prices to Recover Probability Distributions
F. Gonzalez Miranda, A. N. Burgess 219
Modeling Financial Time Series Using State Space Models
J. Timmer, A. S. Weigend 233
Forecasting Properties of Neural Network Generated Volatility Estimates
P. Ahmed, S. Swidler 247
Interest Rates Structure Dynamics: A Non-Parametric Approach
M Cottrell, E. De Bodt, P. Gregoire 259
State Space ARCH: Forecasting Volatility with a Stochastic Coefficient Model
A. Veiga, M C. Medeiros, C. Fernandes 267
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 277
Time-Varying Factor Sensitivities in Equity Investment Management
Y. Bentz, J. T. Connor 291
Discovering Structure in Finance Using Independent Component Analysis
A.D. Back, A. S. Weigend 309
Fitting No Arbitrage Term Structure Models Using a Regularisation Term
N. Towers, J. T. Connor 323
Quantification of Sector Allocation at the German Stock Market
E. Steurer 333
PART 5: CORPORATE DISTRESS MODELS
Predicting Corporate Financial Distress Using Quantitative and Qualitative Data: A Comparison
of Standard and Collapsible Neural Networks
Q. Booker, R. E. Dorsey, J. D. Johnson 355
xi
Credit Assessment Using Evolutionary MLP Networks
E.F.F. Mendes, A. Carvalho, A.B. Matias 365
Exploring Corporate Bankruptcy with Two-Level Self-Organizing Map
K. Kiviluoto, P. Bergius 373
The Ex-Ante Classification of Takeover Targets Using Neural Networks
D. Fairclough, J. Hunter 381
PART 6: ADVANCES ON METHODOLOGY -SHORT NOTES
Forecasting Non-Stationary Financial Data with OUR-Filters and Composed Threshold Models
M Wildi 391
Portfolio Optimisation with Cap Weight Restrictions
N. F. Wagner 403
Are Neural Network and Econometric Forecasts Good for Trading? Stochastic Variance Models
as a Filter Rule
R. Bramante, R. Colombo, G. Gabbi 417
Incorporating Prior Knowledge about Financial Markets through Neural Multitask Learning
K. Bartlmae, S. Gutjahr, G. Nakhaeizadeh 425
Predicting Time-Series with a Committee of Independent Experts Based on Fuzzy Rules
M Rast 433
Multiscale Analysis of Time Series Based on A Neuro-Fuzzy-Chaos Methodology Applied to
Financial Data
N. K. Kasabov, R. Kozma 439
On the Market Timing Ability of Neural Networks: An Empirical Study Testing the Forecasting
Performance
T. H. Hann, J. Hofmeister 451
Currency Forecasting Using Recurrent RBF Networks Optimized by Genetic Algorithms
A. Adamopoulos et af. 461
Exchange Rate Trading Using a Fast Retraining Procedure for Generalised RBF Networks
D. R. Dersch, B.G. Flower, S. J. Pickard 471