<s> Applied Quantitative Methodsfor Trading and Investment
Applied Quantitative Methods for Trading and Investment. 2003 John Wiley & Sons, Ltd ISBN: 0-470-84885-5
Edited by C.L. Dunis, J. Laws and P. Naı̈m
Wiley Finance SeriesApplied Quantitative Methods for Trading and InvestmentChristian L. Dunis, Jason Laws and Patrick Naı̈mCountry Risk Assessment: A Guide to Global Investment StrategyMichel Henry Bouchet, Ephraim Clark and Bertrand GroslambertCredit Derivatives Pricing Models: Models, Pricing and ImplementationPhilipp J. SchönbucherHedge Funds: A resource for investorsSimone BorlaThe Simple Rules: Revisiting the art of ﬁnancial risk managementErik BanksOption TheoryPeter JamesRisk-adjusted Lending ConditionsWerner RosenbergerMeasuring Market RiskKevin DowdAn Introduction to Market Risk ManagementKevin DowdBehavioural FinanceJames MontierAsset Management: Equities DemystiﬁedShanta AcharyaAn Introduction to Capital Markets: Products, Strategies, ParticipantsAndrew M. ChisholmHedge Funds: Myths and LimitsFrancois-Serge LhabitantThe Manager’s Concise Guide to RiskJihad S. NaderSecurities Operations: A guide to trade and position managementMichael SimmonsModeling, Measuring and Hedging Operational RiskMarcelo CruzMonte Carlo Methods in FinancePeter JäckelBuilding and Using Dynamic Interest Rate ModelsKen Kortanek and Vladimir MedvedevStructured Equity Derivatives: The Deﬁnitive Guide to Exotic Options and Structured NotesHarry KatAdvanced Modelling in Finance Using Excel and VBAMary Jackson and Mike StauntonOperational Risk: Measurement and ModellingJack KingInterest Rate ModellingJessica James and Nick Webber
Applied Quantitative Methodsfor Trading and InvestmentEdited by
Christian L. DunisJason Lawsand
Patrick Naı̈m
Copyright  2003
John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester,West Sussex PO19 8SQ, EnglandTelephone (+44) 1243 779777
Email (for orders and customer service enquiries): cs-books@wiley.co.ukVisit our Home Page on www.wileyeurope.com or www.wiley.comAll Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system ortransmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning orotherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of alicence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP, UK,without the permission in writing of the Publisher. Requests to the Publisher should be addressed to thePermissions Department, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO198SQ, England, or emailed to permreq@wiley.co.uk, or faxed to (+44) 1243 770620.This publication is designed to provide accurate and authoritative information in regard to the subject mattercovered. It is sold on the understanding that the Publisher is not engaged in rendering professional services. Ifprofessional advice or other expert assistance is required, the services of a competent professional should besought.Other Wiley Editorial OfﬁcesJohn Wiley & Sons Inc., 111 River Street, Hoboken, NJ 07030, USAJossey-Bass, 989 Market Street, San Francisco, CA 94103-1741, USAWiley-VCH Verlag GmbH, Boschstr. 12, D-69469 Weinheim, GermanyJohn Wiley & Sons Australia Ltd, 33 Park Road, Milton, Queensland 4064, AustraliaJohn Wiley & Sons (Asia) Pte Ltd, 2 Clementi Loop #02-01, Jin Xing Distripark, Singapore 129809John Wiley & Sons Canada Ltd, 22 Worcester Road, Etobicoke, Ontario, Canada M9W 1L1Wiley also publishes its books in a variety of electronic formats. Some content that appearsin print may not be available in electronic books.Library of Congress Cataloging-in-Publication DataApplied quantitative methods for trading and investment / edited by Christian Dunis, JasonLaws, and Patrick Naı̈mp. cm. — (Wiley ﬁnance series)Includes bibliographical references and index.ISBN 0-470-84885-5 (cased : alk. paper)1. Finance—Mathematical models. 2. Investments—Mathematical models. 3.Speculation—Mathematical models. I. Dunis, Christian. II. Laws, Jason. III. Naı̈m,Patrick. IV. SeriesHG106.A67 2003332.6 01 5195—dc212003049721British Library Cataloguing in Publication DataA catalogue record for this book is available from the British LibraryISBN 0-470-84885-5Typeset in 10/12pt Times by Laserwords Private Limited, Chennai, IndiaPrinted and bound in Great Britain by TJ International, Padstow, CornwallThis book is printed on acid-free paper responsibly manufactured from sustainable forestryin which at least two trees are planted for each one used for paper production.
ContentsAbout the Contributors
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Preface
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1
Applications of Advanced Regression Analysis for Trading andInvestmentChristian L. Dunis and Mark Williams1.11.21.31.41.51.61.7
2
AbstractIntroductionLiterature reviewThe exchange rate and related ﬁnancial dataBenchmark models: theory and methodologyNeural network models: theory and methodologyForecasting accuracy and trading simulationConcluding remarksReferences
111341020313639
Using Cointegration to Hedge and Trade International EquitiesA. Neil Burgess
41
AbstractIntroductionTime series modelling and cointegrationImplicit hedging of unknown common risk factorsRelative value and statistical arbitrageIllustration of cointegration in a controlled simulationApplication to international equitiesDiscussion and conclusionsReferences
414142454750546668
2.12.22.32.42.52.62.7
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3 Modelling the Term Structure of Interest Rates: An Applicationof Gaussian Afﬁne Models to the German Yield CurveNuno Cassola and Jorge Barros Luı́s
71
AbstractIntroductionBackground issues on asset pricingDufﬁe–Kan afﬁne models of the term structureA forward rate test of the expectations theoryIdentiﬁcationEconometric methodology and applicationsEstimation resultsConclusionsReferences
71717778838487106126126
4 Forecasting and Trading Currency Volatility: An Application ofRecurrent Neural Regression and Model CombinationChristian L. Dunis and Xuehuan Huang
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AbstractIntroductionThe exchange rate and volatility dataThe GARCH (1,1) benchmark volatility forecastsThe neural network volatility forecastsModel combinations and forecasting accuracyForeign exchange volatility trading modelsConcluding remarks and further workAcknowledgementsAppendix AAppendix BAppendix CAppendix DAppendix EAppendix FAppendix GReferences
129129132135137142145149150150152155156157158159160
5 Implementing Neural Networks, Classiﬁcation Trees, and RuleInduction Classiﬁcation Techniques: An Application to CreditRiskGeorge T. Albanis
163
3.13.23.33.43.53.63.73.8
4.14.24.34.44.54.64.7
5.15.25.35.4
AbstractIntroductionData descriptionNeural networks for classiﬁcation in ExcelClassiﬁcation tree in Excel
163163165166172
Contents
5.55.6
6
AbstractIntroductionThe modelMaximum likelihood estimationAn application to foreign exchange ratesConclusionReferencesAppendix A: Gauss code for maximum likelihood for varianceswitching models
Quantitative Equity Investment Management with Time-VaryingFactor SensitivitiesYves Bentz7.17.27.37.47.57.6
8
See5 classiﬁerConclusionsReferences
Switching Regime Volatility: An Empirical EvaluationBruno B. Roche and Michael Rockinger6.16.26.36.46.5
7
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AbstractIntroductionFactor sensitivities deﬁnedOLS to estimate factor sensitivities: a simple, popular butinaccurate methodWLS to estimate factor sensitivities: a better but stillsub-optimal methodThe stochastic parameter regression model and the Kalmanﬁlter: the best way to estimate factor sensitivitiesConclusionReferences
178191191193193193194195197206206208
213213213215216222223236237
Stochastic Volatility Models: A Survey with Applications toOption Pricing and Value at RiskMonica Billio and Domenico Sartore
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AbstractIntroductionModels of changing volatilityStochastic volatility modelsEstimationExtensions of SV modelsMultivariate modelsEmpirical applicationsConcluding remarksAppendix A: Application of the pentanomial model
239239244246250261263265284284
8.18.28.38.48.58.68.78.8
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Appendix B: Application to Value at RiskReferences9 Portfolio Analysis Using ExcelJason Laws9.19.29.39.49.59.6
AbstractIntroductionThe simple Markovitz modelThe matrix approach to portfolio riskMatrix algebra in Excel when the number of assets increasesAlternative optimisation targetsConclusionBibliography
10 Applied Volatility and Correlation Modelling Using ExcelFrédérick Bourgoin10.110.210.310.410.5
AbstractIntroductionThe BasicsUnivariate modelsMultivariate modelsConclusionReferences
11 Optimal Allocation of Trend-Following Rules: An ApplicationCase of Theoretical ResultsPierre Lequeux
286286293293293294301303308310311313313313314315324331332
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Abstract11.1 Introduction11.2 Data11.3 Moving averages and their statistical properties11.4 Trading rule equivalence11.5 Expected transactions cost under assumption of random walk11.6 Theoretical correlation of linear forecasters11.7 Expected volatility of MA11.8 Expected return of linear forecasters11.9 An applied example11.10 Final remarksReferences
333333333335336338340341342344346347
12 Portfolio Management and Information from Over-the-CounterCurrency OptionsJorge Barros Luı́s
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Abstract12.1 Introduction
349349
Contents
12.212.312.412.512.612.7
13
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The valuation of currency options spreadsRND estimation using option spreadsMeasures of correlation and option pricesIndicators of credibility of an exchange rate bandEmpirical applicationsConclusionsReferences
353355359361365378379
Filling Analysis for Missing Data: An Application to WeatherRisk ManagementChristian L. Dunis and Vassilios Karalis
381
13.113.213.313.413.5
Index
AbstractIntroductionWeather data and weather derivativesAlternative ﬁlling methods for missing dataEmpirical resultsConcluding remarksAppendix AAppendix BReferences
381381383385393395396397398401
About the ContributorsGeorge T. Albanis is currently working at Hypovereinsbank – HVB Group. He obtainedhis PhD from City University Business School, London and holds a BSc in Economicsfrom the University of Piraeus, Greece and Master’s degrees in Business Finance andin Decision Modelling and Information Systems from Brunel University, London. Anexperienced programmer, his interests are applications of advanced nonlinear techniquesfor ﬁnancial prediction in ﬁxed income and credit derivatives markets, and quantiﬁcationof risk in ﬁnancial modelling.Yves Bentz is Vice President with Crédit Suisse First Boston, specialising in high frequency equity trading strategies and statistical arbitrage. He was previously a quantitativetrader with Morgan Stanley and with Beaghton Capital Management in London where hedeveloped automated equity and derivatives trading strategies. Yves holds a PhD fromthe University of London (London Business School). He has published several researchpapers on factor modelling and nonlinear modelling, in particular stochastic parametermodels and nonparametric statistics and their applications to investment management.Monica Billio is Associate Professor of Econometrics at Università Ca’ Foscari of Venice.She graduated in Economics at Università Ca’ Foscari di Venezia and holds a PhD degreein Applied Mathematics from the Université Paris IX Dauphine. Her ﬁelds of interest aresimulation-based methods and the econometrics of ﬁnance.Frédérick Bourgoin is an Associate Portfolio Manager in the Active Fixed Income Portfolio Management Team at Barclays Global Investors in London where he is involvedin the development of the active bond and currency strategies, as well as the risk management systems. Prior to joining BGI, he was a risk manager and quantitative analystat Portman Asset Management. Frédérick holds a Post-Graduate Degree in Finance fromESSEC Business School and an MSc in Econometrics and Mathematical Economics fromPanthéon-Sorbonne University in Paris.Neil Burgess is a Vice President in the Institutional Equity Division at Morgan Stanleywhere he works in the area of quantitative programme trading, leading and coordinatingnew developments in trading systems and strategies for equities and equity derivativesbetween Europe and the USA. He obtained his PhD from London University. He haspublished widely in the ﬁeld of emerging computational techniques and has acted as a
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programme committee member for international conferences: Forecasting Financial Markets, Computational Finance and Intelligent Data Engineering and Learning.Nuno Cassola holds a PhD in Economics from the University of Kent at Canterbury. Heworked as an Associate Professor at the Technical University of Lisbon from 1992 until1994. He then joined the Research Department of the Banco de Portugal in 1994 wherehe became Head of the Monetary and Financial Division in 1996. In 1999 he joined theEuropean Central Bank in Frankfurt where he is currently Principal Economist in theMonetary Policy Stance Division of the Monetary Policy Directorate.Christian L. Dunis is Girobank Professor of Banking and Finance at Liverpool BusinessSchool, and Director of its Centre for International Banking, Economics and Finance(CIBEF). He is also a consultant to asset management ﬁrms, a Visiting Professor of International Finance at Venice International University and an Ofﬁcial Reviewer attached tothe European Commission for the Evaluation of Applications to Finance of EmergingSoftware Technologies. He is an Editor of the European Journal of Finance and has published widely in the ﬁeld of ﬁnancial markets analysis and forecasting. He has organisedthe Forecasting Financial Markets Conference since 1994.Xuehuan Huang graduated from Liverpool Business School with an MSc in InternationalBanking and Finance and from China’s Shenzen University with a BA in Business Management. After working as an auditor with Ernst & Young, she is currently a ﬁnancialanalyst at Bayer DS European headquarters.Vassilios Karalis is an Associate Researcher at the Centre for International Banking,Economics and Finance of Liverpool Business School (CIBEF). Vassilios holds an MScin International Banking and Finance from Liverpool Business School and a BSc inMathematics with specialisation in probabilities, statistics and operational research fromthe University of Ioannina, Hellas.Jason Laws is a Lecturer in International Banking and Finance at Liverpool JohnMoores University. He is also the Course Director for the MSc in International Banking,Economics and Finance at Liverpool Business School. He has taught extensively in thearea of investment theory and derivative securities at all levels, both in the UK and inAsia. Jason is also an active member of CIBEF and has published in a number of academicjournals. His research interests are focused on volatility modelling and the implementationof trading strategies.Pierre Lequeux joined the Global Fixed Income division of ABN AMRO Asset Management London in 1999. As Head of Currency Management, he has responsibility forthe quantitative and fundamental currency investment process. He was previously Headof the Quantitative Research and Trading desk at Banque Nationale de Paris, Londonbranch, which he joined in 1987. Pierre is also an Associate Researcher at the Centre forInternational Banking, Economics and Finance of Liverpool Business School (CIBEF)and a member of the editorial board of Derivatives Use, Trading & Regulation.Jorge Barros Luı́s is Head of Credit Risk Modelling with Banco Português de Investimento. Previous positions include Economist at the European Central Bank and Banco dePortugal, Chief-Economist at Banif Investimento and Adviser to the Minister of Financeand to the Secretary of State for the Treasury of the Portuguese Government. Jorge holds
About the Contributors
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a PhD in Economics from the University of York and has published several papers onyield curve modelling and information extraction from option prices.Patrick Naı̈m is an engineer of the Ecole Centrale de Paris. He is the founder andchairman of Elseware, a company specialised in the application of nonlinear methods toﬁnancial management problems. He is currently working for some of the largest Frenchinstitutions and coordinating research projects in the ﬁeld at a European level.Bruno B. Roche is Head of Research in the Global Management Research group of amajor multinational company where he leads a specialist team whose role is to provideworld class expertise, methodologies, technologies and knowledge management in multiple areas which have a global critical impact (e.g. ﬁnancial markets, risk managementand advertising effectiveness). He is also a Researcher at the Solvay Business School atthe University of Brussels.Michael Rockinger is Professor of Finance at the HEC School of Business of the University of Lausanne. He has been scientiﬁc consultant at the French Central Bank for manyyears. He is also afﬁliated with CEPR and FAME. Previously, Michael taught Finance atall levels at HEC-Paris. His research interests are various, one of them is the modellingof asset prices. Michael earned his PhD in Economics at Harvard University. He is alsoa graduate in Mathematics from the Swiss Federal Institute of Technology (EPFL) andholder of a Master’s degree from the University of Lausanne.Domenico Sartore is Full Professor of Econometrics at Università Ca’ Foscari di Venezia.Previously he taught at the University of Milan and the University of Padua. At present,he is President of the economics and ﬁnance consultancy GRETA (Gruppi di RicercaEconomica Teorica ed Applicata) in Venice. His ﬁeld of interest is the econometrics ofﬁnance, where he has published many papers.Mark Williams is an Associate Researcher at the Centre for International Banking,Economics and Finance of Liverpool Business School (CIBEF). Mark holds an MScin International Banking and Finance from Liverpool Business School and a BSc inEconomics from Manchester Metropolitan University.
PrefaceApplied Quantitative Methods for Trading and Investment is intended as a quantitativeﬁnance textbook very much geared towards applied quantitative ﬁnancial analysis, withdetailed empirical examples, software applications, screen dumps, etc. Examples on theaccompanying CD-Rom detail the data, software and techniques used, so that contrary towhat frequently happens with most textbook examples, they clarify the analysis by beingreasonably easily reproducible by the reader.We expect this book to have a wide spectrum of uses and be adopted by ﬁnancialmarket practitioners and in universities. For the former readership, it will be of interestto quantitative researchers involved in investment and/or risk management, to fund managers and quantitative proprietary traders, and also to sophisticated private investors whowill learn how to use techniques generally employed by market professionals in largeinstitutions to manage their own money. For the latter, it will be relevant for studentson MSc, MBA and PhD programmes in Finance where a quantitative techniques unit ispart of the course, and to students in scientiﬁc disciplines wishing to work in the ﬁeld ofquantitative ﬁnance.Despite the large number of publications in the ﬁeld of computational ﬁnance in recentyears, most of these have been geared towards derivatives pricing and/or risk management.1 In the ﬁeld of ﬁnancial econometrics, most books have been subject speciﬁc,2 withvery few truly comprehensive publications.3 Even then, these books on ﬁnancial econometrics have been in reality mostly theoretical, with empirical applications essentiallyfocused on validating or invalidating economic and ﬁnancial theories through econometricand statistical methods.What distinguishes this book from others is that it focuses on a wide spectrum of methods for modelling ﬁnancial markets in the context of practical ﬁnancial applications. Ontop of “traditional” ﬁnancial econometrics, the methods used also include technical analysis systems and many nonparametric tools from the ﬁelds of data mining and artiﬁcialintelligence. Although we do not pretend to have covered all possible methodologies,1See, for instance, Wilmott, P. (1998), Derivatives: The Theory and Practice of Financial Engineering, JohnWiley, Chichester and Alexander, C. (2001), Market Models, John Wiley, Chichester.2See, for instance, Dunis, C., A. Timmermann and J. Moody (2001), Developments in Forecast Combinationand Portfolio Choice, John Wiley, Chichester.3See Campbell, J. Y., A. W. Lo and A. C. MacKinlay (1997), The Econometrics of Financial Markets, Princeton University Press, Princeton and Gouriéroux, C. and J. Jasiak (2002), Financial Econometrics, PrincetonUniversity Press, Princeton.
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we believe that the wide breadth of potential methods retained in this manual is highlydesirable and one of its strengths. At the same time, we have been careful to present eventhe most advanced techniques in a way that is accessible to most potential readers, making sure that those interested in the practical utilisation of such methods could skip themore theoretical developments without hindering comprehension, and concentrate on therelevant practical application: in this respect, the accompanying CD-Rom should provean invaluable asset.An applied book of this nature, with its extensive range of methodologies and applications covered, could only beneﬁt from being a collaborative effort of several people withthe appropriate experience in their ﬁeld. In order to retain the practitioner’s perspectivewhile ensuring the methodological soundness and, should we say, academic respectabilityof the selected applications at the same time, we have assembled a small team of quantitative market professionals, fund managers and proprietary traders, and academics who havetaught applied quantitative methods in ﬁnance at the postgraduate level in their respectiveinstitutions and also worked as scientiﬁc consultants to asset management ﬁrms.As mentioned above, the range of applications and techniques applied is quite large.The different applications cover foreign exchange trading models with three chapters,one using technical analysis, one advanced regression methods including nonparametricNeural Network Regression (NNR) models and one a volatility ﬁlter-based system relyingon Markov switching regimes; one chapter on equity statistical arbitrage and portfolioimmunisation based on cointegration; two chapters on stock portfolio optimisation, oneusing Kalman ﬁltering techniques in the presence of time-varying betas and the other usingmatrix algebra and Excel Solver to derive an optimal emerging stock market portfolio;one chapter on yield curve modelling through the use of afﬁne models; one chapter oncredit classiﬁcation with decision trees, rule induction and neural network classiﬁcationmodels; two chapters on volatility modelling and trading, one using Excel to compute bothunivariate and multivariate GARCH volatility and correlation in the stock market, the otherusing straddle strategies based on GARCH and Recurrent Network Regression (RNR) tobuild a forex volatility trading model; one chapter on Value at Risk (VaR) and optionpricing in the presence of stochastic volatility; one chapter on the information containedin derivatives prices through the use of risk-neutral density functions and, ﬁnally, onechapter on weather risk management when confronted with missing temperature data.The ﬁrst part of the book is concerned with applications relying upon advanced modelling techniques. The applications include currencies, equities, volatility, the term structure of interest rates and credit classiﬁcation. The second part of the book includesthree chapters where the applications on equities, VaR, option pricing and currency trading employ similar methodologies, namely Kalman ﬁlter and regime switching. In theﬁnal part of the book there are ﬁve chapters where a variety of ﬁnancial applicationsranging from technical trading to missing data analysis are predominantly implementedusing Excel.In the following we provide further details on each chapter included in the book.1. “Applications of Advanced Regression Analysis for Trading and Investment” byC. L. Dunis and M. Williams: this chapter examines the use of regression modelsin trading and investment with an application to EUR/USD exchange rate forecasting and trading models. In particular, NNR models are benchmarked against someother traditional regression-based and alternative forecasting techniques to ascertain
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their potential added value as a forecasting and quantitative trading tool. In additionto evaluating the various models out-of-sample from May 2000 to July 2001 usingtraditional forecasting accuracy measures, such as root-mean-squared errors, modelsare also assessed using ﬁnancial criteria, such as risk-adjusted measures of return.Transaction costs are also taken into account. Overall, it is concluded that regressionmodels, and in particular NNR models, do have the ability to forecast EUR/USDreturns for the period investigated, and add value as a forecasting and quantitativetrading tool.2. “Using Cointegration to Hedge and Trade International Equities” by A. N. Burgess:this chapter analyses how to hedge and trade a portfolio of international equities,applying the econometric concept of cointegration. The concepts are illustrated withrespect to a particular set of data, namely the 50 equities which constituted theSTOXX 50 index as of 4 July 2002. The daily closing prices of these equities areinvestigated over a period from 14 September 1998 to 3 July 2002 – the longestperiod over which continuous data is available across the whole set of stocks in thisparticular universe. Despite some spurious effects due to the non-synchronous closingtimes of the markets on which these equities trade, the data are deemed suitable forillustration purposes. Overall, depending on the particular task in hand, it is shownthat the techniques applied can be successfully used to identify potential hedges fora given equity position and/or to identify potential trades which might be taken froma statistical arbitrage perspective.3. “Modelling the Term Structure of Interest Rates: An Application of Gaussian AfﬁneModels to the German Yield Curve” by N. Cassola and J. B. Luı́s: this chapter showsthat a two-factor constant volatility model describes quite well the dynamics and theshape of the German yield curve between 1986 and 1998. The analysis supports theexpectations theory with constant term premiums and thus the term premium structurecan be calculated and short-term interest rate expectations derived from the adjustedforward rate curve. The analysis is carried out in Matlab and the authors include allof the ﬁles with which to reproduce the analysis. Their ﬁndings will be of interestto risk managers analysing the shape of the yield curve under different scenarios andalso to policy makers in assessing the impact of ﬁscal and monetary policy.4. “Forecasting and Trading Currency Volatility: An Application of Recurrent NeuralRegression and Model Combination” by C. L. Dunis and X. Huang: this chapterexamines the use of nonparametric Neural Network Regression (NNR) and RecurrentNeural Network (RNN) regression models for forecasting and trading currency volatility, with an application to the GBP/USD and USD/JPY exchange rates. The results ofthe NNR and RNN models are benchmarked against the simpler GARCH alternativeand implied volatility. Two simple model combinations are also analysed. Alternative FX volatility forecasting models are tested out-of-sample over the period April1999–May 2000, not only in terms of forecasting accuracy, but also in terms of trading efﬁciency: in order to do so, a realistic volatility trading strategy is implemented,using FX option straddles once mispriced options have been identiﬁed. Allowingfor transaction costs, most trading strategies retained produce positive returns. RNNmodels appear as the best single modelling approach, yet model combination whichhas the best overall performance in terms of forecasting accuracy fails to improvethe RNN-based volatility trading results.
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5. “Implementing Neural Networks, Classiﬁcation Trees, and Rule Induction Classiﬁcation Techniques: An Application to Credit Risk” by G. T. Albanis: this chaptershows how to implement several classiﬁcation tools for data mining applications inﬁnance. Two freely available softwares on classiﬁcation neural networks and decision trees, respectively, and one commercial software for constructing decision treesand rule induction classiﬁers are demonstrated, using two datasets that are availablein the public domain. The ﬁrst dataset is known as the Australian credit approvaldataset. The application consists of constructing a classiﬁcation rule for assessing thequality of credit card applicants. The second dataset is known as the German creditdataset. The aim in this application is to construct a classiﬁcation rule for assessing the credit quality of German borrowers. Beyond these examples, the methodsdemonstrated in this chapter can be applied to many other quantitative trading andinvestment problems, such as the determination of outperforming/underperformingstocks, bond rating, etc.6. “Switching Regime Volatility: An Empirical Evaluation” by B. B. Roche and M.Rockinger: this chapter describes in a pedagogical fashion, using daily observationsof the USD/DEM exchange rate from October 1995 to October 1998, how to estimatea univariate switching model for daily foreign exchange returns which are assumed tobe drawn in a Markovian way from alternative Gaussian distributions with differentmeans and variances. The application shows that the USD/DEM exchange rate canbe modelled as a mixture of normal distributions with changes in volatility, but notin mean, where regimes with high and low volatility alternate. The usefulness ofthis methodology is demonstrated in a real life application, i.e. through the proﬁtperformance comparison of simple hedging strategies.7. “Quantitative Equity Investment Management with Time-Varying Factor Sensitivities”by Y. Bentz: this chapter describes three methods used in modern equity investmentmanagement for estimating time-vary