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This page intentionally left blankIntroductory econometrics for FinanceSECOND EDITIONThis best-selling textbook addresses the need for an introduction toeconometrics specifically written for finance students. It includesexamples and case studies which finance students will recognise andrelate to. This new edition builds on the successful data- andproblem-driven approach of the first edition, giving students the skills toestimate and interpret models while developing an intuitive grasp ofunderlying theoretical features: Thoroughly revised and updated, including two new chapters onpanel data and limited dependent variable models Problem-solving approach assumes no prior knowledge ofeconometrics emphasising intuition rather than formulae, givingstudents the skills and confidence to estimate and interpret models Detailed examples and case studies from finance show students howtechniques are applied in real research Sample instructions and output from the popular computer packageEV

Introductory Econometrics for Finance SECOND EDITION This best-selling textbook addresses the need for an introduction to econometrics specifically written for finance students.

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1 This page intentionally left blankIntroductory econometrics for FinanceSECOND EDITIONThis best-selling textbook addresses the need for an introduction toeconometrics specifically written for finance students. It includesexamples and case studies which finance students will recognise andrelate to. This new edition builds on the successful data- andproblem-driven approach of the first edition, giving students the skills toestimate and interpret models while developing an intuitive grasp ofunderlying theoretical features: Thoroughly revised and updated, including two new chapters onpanel data and limited dependent variable models Problem-solving approach assumes no prior knowledge ofeconometrics emphasising intuition rather than formulae, givingstudents the skills and confidence to estimate and interpret models Detailed examples and case studies from finance show students howtechniques are applied in real research Sample instructions and output from the popular computer packageEViews enable students to implement models themselves andunderstand how to interpret results Gives advice on planning and executing a project in empirical finance .

2 Preparing students for using econometrics in practice Covers important modern topics such as time-series forecasting,volatility modelling, switching models and simulation methods Thoroughly class-tested in leading finance schoolsChris Brooks is Professor of finance at the ICMA Centre, University ofReading, UK, where he also obtained his PhD. He has published oversixty articles in leading academic and practitioner journals includingtheJournal of Business,theJournal of Banking and finance ,theJournal ofEmpirical finance ,theReview of Economics and Statisticsand theEconomicJournal. He is an associate editor of a number of journals including theInternational Journal of Forecasting.

3 He has also acted as consultant forvarious banks and professional bodies in the fields of finance , econometrics and real Econometricsfor FinanceSECOND EDITIONC hris BrooksThe ICMA Centre, University of ReadingCAMBRIDGE UNIVERSITY PRESSC ambridge, New York, Melbourne, Madrid, Cape Town, Singapore, S o PauloCambridge University PressThe Edinburgh Building, Cambridge CB2 8RU, UKFirst published in print formatISBN-13 978-0-521-87306-2 ISBN-13 978-0-521-69468-1 ISBN-13 978-0-511-39848-3 Chris Brooks 20082008 Information on this title: publication is in copyright. Subject to statutory exception and to the provision of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University University Press has no responsibility for the persistence or accuracy of urls for external or third-party internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will remain, accurate or in the United States of America by Cambridge University Press, New (EBL)

4 HardbackContentsList of figurespagexiiList of tablesxivList of boxesxviList of screenshotsxviiPreface to the second editionxixAcknowledgementsxxiv1 What is econometrics ? Is financial econometrics different from economic econometrics ? Types of Returns in financial Steps involved in formulating an econometric Points to consider when reading articles in empirical Econometric packages for modelling financial Outline of the remainder of this Further reading25 Appendix: Econometric software package suppliers262 A brief overview of the classical linear regression What is a regression model? Regression versus Simple Some further Simple linear regression in EViews -- estimation of an optimalhedge The assumptions underlying the classical linear regression Properties of the OLS Precision and standard An introduction to statistical A special type of hypothesis test: An example of the use of a simplet-test to test a theory in finance :can US mutual funds beat the market?

5 Can UK unit trust managers beat the market? The overreaction hypothesis and the UK stock The exact significance Hypothesis testing in EViews -- example 1: hedging Estimation and hypothesis testing in EViews -- example 2:the CAPM77 Appendix: Mathematical derivations of CLRM results813 Further development and analysis of the classical linearregression Generalising the simple model to multiple linear The constant How are the parameters (the elements of the vector) calculatedin the generalised case? Testing multiple hypotheses: Sample EViews output for multiple hypothesis Multiple regression in EViews using an APT-style Data mining and the true size of the Goodness of fit Hedonic pricing Tests of non-nested hypotheses115 Appendix : Mathematical derivations of CLRM results117 Appendix : A brief introduction to factor models and principalcomponents analysis1204 Classical linear regression model assumptions anddiagnostic Statistical distributions for diagnostic Assumption 1:E(ut)= Assumption 2:var(ut)= 2< Assumption 3:cov(ui,uj)=0 fori = Assumption 4: thextare Assumption 5.

6 The disturbances are normally Adopting the wrong functional Omission of an important Inclusion of an irrelevant Parameter stability A strategy for constructing econometric models and a discussionof model-building Determinants of sovereign credit ratings1945 Univariate time series modelling and Some notation and Moving average Autoregressive The partial autocorrelation ARMA Building ARMA models: the Box--Jenkins Constructing ARMA models in Examples of time series modelling in Exponential Forecasting in Forecasting using ARMA models in Estimating exponential smoothing models using EViews2586 Multivariate Simultaneous equations So how can simultaneous equations models be validly estimated?

7 Can the original coefficients be retrieved from the s? Simultaneous equations in A definition of Triangular Estimation procedures for simultaneous equations An application of a simultaneous equations approach tomodelling bid--ask spreads and trading Simultaneous equations modelling using Vector autoregressive Does the VAR include contemporaneous terms? Block significance and causality VARs with exogenous Impulse responses and variance VAR model example: the interaction between property returns andthe VAR estimation in EViews308viiiContents7 Modelling long-run relationships in Stationarity and unit root Testing for unit roots in Equilibrium correction or error correction Testing for cointegration in regression.

8 A residuals-based Methods of parameter estimation in cointegrated Lead--lag and long-term relationships between spot andfutures Testing for and estimating cointegrating systems using theJohansen technique based on Purchasing power Cointegration between international bond Testing the expectations hypothesis of the term structure ofinterest Testing for cointegration and modelling cointegrated systemsusing EViews3658 Modelling volatility and Motivations: an excursion into non-linearity Models for Historical Implied volatility Exponentially weighted moving average Autoregressive volatility Autoregressive conditionally heteroscedastic (ARCH) Generalised ARCH (GARCH) Estimation of ARCH/GARCH Extensions to the basic GARCH Asymmetric GARCH The GJR The EGARCH GJR and EGARCH in Tests for asymmetries in Uses of GARCH-type models including volatility Testing non-linear restrictions or testing hypotheses aboutnon-linear Volatility forecasting.

9 Some examples and results from Stochastic volatility models Forecasting covariances and Covariance modelling and forecasting in finance : some Historical covariance and Implied covariance Exponentially weighted moving average model for Multivariate GARCH A multivariate GARCH model for the CAPM with Estimating a time-varying hedge ratio for FTSE stock index Estimating multivariate GARCH models using EViews441 Appendix: Parameter estimation using maximum likelihood4449 Switching Seasonalities in financial markets: introduction andliterature Modelling seasonality in financial Estimating simple piecewise linear Markov switching A Markov switching model for the real exchange A Markov switching model for the gilt--equity yield Threshold autoregressive Estimation of threshold autoregressive Specification tests in the context of Markov switching andthreshold autoregressive models.

10 A cautionary A SETAR model for the French franc--German mark exchange Threshold models and the dynamics of the FTSE 100 index andindex futures A note on regime switching models and forecasting accuracy48410 Panel Introduction -- what are panel techniques and why are they used? What panel techniques are available? The fixed effects Time-fixed effects Investigating banking competition using a fixed effects The random effects Panel data application to credit stability of banks in Central andEastern Panel data with Further reading509xContents11 Limited dependent variable Introduction and The linear probability The logit Using a logit to test the pecking order The probit Choosing between the logit and probit Estimation of limited dependent variable Goodness of fit measures for linear dependent variable Multinomial linear dependent The pecking order hypothesis revisited -- the choice


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