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The only econometrics textbook that requires no prior knowledge of the subject, aimed specifically at students of finance, accountancy, or banking. It includes a broad range of techniques, detailed case studies, and explanations of how to implement the techniques and understand the results from the most popular software packages. A complete resource for finance students, this textbook presents the most common empirical approaches in finance in a comprehensive and well-illustrated manner that shows how econometrics is used in practice, and includes detailed case studies to explain how the techniques are used in relevant financial contexts. Maintaining the accessible prose and clear examples of previous editions, the new edition of this best-selling textbook provides support for the main industry-standard software packages, expands the coverage of introductory mathematical and statistical techniques into two chapters for students without prior econometrics knowledge, and includes a new chapter on advanced methods. Learning outcomes, key concepts and end-of-chapter review questions (with full solutions online) highlight the main chapter takeaways and allow students to self-assess their understanding. Online resources include extensive teacher and student support materials, including EViews, Stata, R, and Python software guides Half Title page Title page Copyright page Contents in Brief Detailed Contents List of Figures List of Tables List of Boxes List of Screenshots Preface to the Fourth Edition Acknowledgements Outline of the Remainder of this Book Chapter 1 Introduction and Mathematical Foundations 1.1 What is Econometrics? 1.2 Is Financial Econometrics Different? 1.3 Steps Involved in Formulating an Econometric Model 1.4 Points to Consider When Reading Articles 1.5 Functions 1.6 Differential Calculus 1.7 Matrices Chapter 2 Statistical Foundations and Dealing with Data 2.1 Probability and Probability Distributions 2.2 A Note on Bayesian versus Classical Statistics 2.3 Descriptive Statistics 2.4 Types of Data and Data Aggregation 2.5 Arithmetic and Geometric Series 2.6 Future Values and Present Values 2.7 Returns in Financial Modelling 2.8 Portfolio Theory Using Matrix Algebra Chapter 3 A Brief Overview of the Classical Linear Regression Model 3.1 What is a Regression Model? 3.2 Regression versus Correlation 3.3 Simple Regression 3.4 Some Further Terminology 3.5 The Assumptions Underlying the Model 3.6 Properties of the OLS Estimator 3.7 Precision and Standard Errors 3.8 An Introduction to Statistical Inference 3.9 A Special Type of Hypothesis Test 3.10 An Example of a Simple t-test of a Theory 3.11 Can UK Unit Trust Managers Beat the Market? 3.12 The Overreaction Hypothesis 3.13 The Exact Significance Level Appendix 3.1 Mathematical Derivations of CLRM Results Chapter 4 Further Development and Analysis of the Classical Linear Regression Model 4.1 Generalising the Simple Model 4.2 The Constant Term 4.3 How are the Parameters Calculated? 4.4 Testing Multiple Hypotheses: The F-test 4.5 Data Mining and the True Size of the Test 4.6 Qualitative Variables 4.7 Goodness of Fit Statistics 4.8 Hedonic Pricing Models 4.9 Tests of Non-Nested Hypotheses 4.10 Quantile Regression Appendix 4.1 Mathematical Derivations of CLRM Results Appendix 4.2 A Brief Introduction to Factor Models and Principal Components Analysis Chapter 5 Classical Linear Regression Model Assumptions and Diagnostic Tests 5.1 Introduction 5.2 Statistical Distributions for Diagnostic Tests 5.3 Assumption (1): E(ut) = 0 5.4 Assumption (2): var(ut) = σ2 < ∞ 5.5 Assumption (3): cov(ui, uj) = 0 for i = j 5.6 Assumption (4): The xt are Non-Stochastic 5.7 Assumption (5): The Disturbances are Normally Distributed 5.8 Multicollinearity 5.9 Adopting the Wrong Functional Form 5.10 Omission of an Important Variable 5.11 Inclusion of an Irrelevant Variable 5.12 Parameter Stability Tests 5.13 Measurement Errors 5.14 A Strategy for Constructing Econometric Models 5.15 Determinants of Sovereign Credit Ratings Chapter 6 Univariate Time-Series Modelling and Forecasting 6.1 Introduction 6.2 Some Notation and Concepts 6.3 Moving Average Processes 6.4 Autoregressive Processes 6.5 The Partial Autocorrelation Function 6.6 ARMA Processes 6.7 Building ARMA Models: The Box–Jenkins Approach 6.8 Examples of Time-Series Modelling in Finance 6.9 Exponential Smoothing 6.10 Forecasting in Econometrics Chapter 7 Multivariate Models 7.1 Motivations 7.2 Simultaneous Equations Bias 7.3 So how can Simultaneous Equations Models be Validly Estimated? 7.4 Can the Original Coefficients be Retrieved from the π s? 7.5 Simultaneous Equations in Finance 7.6 A Definition of Exogeneity 7.7 Triangular Systems 7.8 Estimation Procedures for Simultaneous Equations Systems 7.9 An Application of a Simultaneous Equations Approach 7.10 Vector Autoregressive Models 7.11 Does the VAR Include Contemporaneous Terms? 7.12 Block Significance and Causality Tests 7.13 VARs with Exogenous Variables 7.14 Impulse Responses and Variance Decompositions 7.15 VAR Model Example: The Interaction Between Property Returns and the Macroeconomy 7.16 A Couple of Final Points on VARs Chapter 8 Modelling Long-Run Relationships in Finance 8.1 Stationarity and Unit Root Testing 8.2 Tests for Unit Roots in the Presence of Structural Breaks 8.3 Cointegration 8.4 Equilibrium Correction or Error Correction Models 8.5 Testing for Cointegration in Regression: A Residuals-Based Approach 8.6 Methods of Parameter Estimation in Cointegrated Systems 8.7 Lead–Lag and Long-Term Relationships Between Spot and Futures Markets 8.8 Testing for and Estimating Cointegration in Systems 8.9 Purchasing Power Parity 8.10 Cointegration Between International Bond Markets 8.11 Testing the Expectations Hypothesis of the Term Structure of Interest Rates Chapter 9 Modelling Volatility and Correlation 9.1 Motivations: An Excursion into Non-Linearity Land 9.2 Models for Volatility 9.3 Historical Volatility 9.4 Implied Volatility Models 9.5 Exponentially Weighted Moving Average Models 9.6 Autoregressive Volatility Models 9.7 Autoregressive Conditionally Heteroscedastic (ARCH) Models 9.8 Generalised ARCH (GARCH) Models 9.9 Estimation of ARCH/GARCH Models 9.10 Extensions to the Basic GARCH Model 9.11 Asymmetric GARCH Models 9.12 The GJR model 9.13 The EGARCH Model 9.14 Tests for Asymmetries in Volatility 9.15 GARCH-in-Mean 9.16 Uses of GARCH-Type Models 9.17 Testing Non-Linear Restrictions 9.18 Volatility Forecasting: Some Examples and Results 9.19 Stochastic Volatility Models Revisited 9.20 Forecasting Covariances and Correlations 9.21 Covariance Modelling and Forecasting in Finance 9.22 Simple Covariance Models 9.23 Multivariate GARCH Models 9.24 Direct Correlation Models 9.25 Extensions to the Basic Multivariate GARCH Model 9.26 A Multivariate GARCH Model for the CAPM 9.27 Estimating a Time-Varying Hedge Ratio 9.28 Multivariate Stochastic Volatility Models Appendix 9.1 Parameter Estimation Using Maximum Likelihood Chapter 10 Switching and State Space Models 10.1 Motivations 10.2 Seasonalities in Financial Markets 10.3 Modelling Seasonality in Financial Data 10.4 Estimating Simple Piecewise Linear Functions 10.5 Markov Switching Models 10.6 A Markov Switching Model for the Real Exchange Rate 10.7 A Markov Switching Model for the Gilt–Equity Yield Ratio 10.8 Threshold Autoregressive Models 10.9 Estimation of Threshold Autoregressive Models 10.10 Specification Tests 10.11 A SETAR Model for the French franc–German mark Exchange Rate 10.12 Threshold Models for FTSE Spot and Futures 10.13 Regime Switching Models and Forecasting 10.14 State Space Models and the Kalman Filter Chapter 11 Panel Data 11.1 Introduction: What Are Panel Techniques? 11.2 What Panel Techniques Are Available? 11.3 The Fixed Effects Model 11.4 Time-Fixed Effects Models 11.5 Investigating Banking Competition 11.6 The Random Effects Model 11.7 Panel Data Application to Credit Stability of Banks 11.8 Panel Unit Root and Cointegration Tests 11.9 Further Feading Chapter 12 Limited Dependent Variable Models 12.1 Introduction and Motivation 12.2 The Linear Probability Model 12.3 The Logit Model 12.4 Using a Logit to Test the Pecking Order Hypothesis 12.5 The Probit Model 12.6 Choosing Between the Logit and Probit Models 12.7 Estimation of Limited Dependent Variable Models 12.8 Goodness of Fit Measures for Linear Dependent Variable Models 12.9 Multinomial Linear Dependent Variables 12.10 The Pecking Order Hypothesis Revisited 12.11 Ordered Response Linear Dependent Variables Models 12.12 Are Unsolicited Credit Ratings Biased Downwards? An Ordered Probit Analysis 12.13 Censored and Truncated Dependent Variables Appendix 12.1 The Maximum Likelihood Estimator for Logit and Probit Models Chapter 13 Simulation Methods 13.1 Motivations 13.2 Monte Carlo Simulations 13.3 Variance Reduction Techniques 13.4 Bootstrapping 13.5 Random Number Generation 13.6 Disadvantages of the Simulation Approach 13.7 An Example of Monte Carlo Simulation 13.8 An Example of how to Simulate the Price of a Financial Option 13.9 An Example of Bootstrapping to Calculate Capital Risk Requirements Chapter 14 Additional Econometric Techniques for Financial Research 14.1 Event Studies 14.2 Tests of the CAPM and the Fama–French Methodology 14.3 Extreme Value Theory 14.4 The Generalised Method of Moments Chapter 15 Conducting Empirical Research or Doing a Project or Dissertation in Finance 15.1 What is an Empirical Research Project? 15.2 Selecting the Topic 15.3 Sponsored or Independent Research? 15.4 The Research Proposal 15.5 Working Papers and Literature on the Internet 15.6 Getting the Data 15.7 Choice of Computer Software 15.8 Methodology 15.9 How Might the Finished Project Look? 15.10 Presentational Issues Appendix 1 Sources of Data Used in This Book and the Accompanying Software Manuals Appendix 2 Tables of Statistical Distributions Glossary References Index "Description Contents Resources Courses About the AuthorsA complete resource for finance students, this textbook presents the most common empirical approaches in finance in a comprehensive and well-illustrated manner that shows how econometrics is used in practice, and includes detailed case studies to explain how the techniques are used in relevant financial contexts. Maintaining the accessible prose and clear examples of previous editions, the new edition of this best-selling textbook provides support for the main industry-standard software packages, expands the coverage of introductory mathematical and statistical techniques into two chapters for students without prior econometrics knowledge, and includes a new chapter on advanced methods. Learning outcomes, key concepts and end-of-chapter review questions (with full solutions online) highlight the main chapter takeaways and allow students to self-assess their understanding. Online resources include extensive teacher and student support materials, including EViews, Stata, R, and Python software guides" -- Provided by Publisher « Description Contents Resources Courses About the AuthorsA complete resource for finance students, this textbook presents the most common empirical approaches in finance in a comprehensive and well-illustrated manner that shows how econometrics is used in practice, and includes detailed case studies to explain how the techniques are used in relevant financial contexts. Maintaining the accessible prose and clear examples of previous editions, the new edition of this best-selling textbook provides support for the main industry-standard software packages, expands the coverage of introductory mathematical and statistical techniques into two chapters for students without prior econometrics knowledge, and includes a new chapter on advanced methods. Learning outcomes, key concepts and end-of-chapter review questions (with full solutions online) highlight the main chapter takeaways and allow students to self-assess their understanding. Online resources include extensive teacher and student support materials, including EViews, Stata, R, and Python software guides »-- Résumé de l'éditeur This best-selling textbook addresses the need for an introduction to econometrics specifically written for finance students. Key features:Thoroughly revised and updated, including two new chapters on panel data and limited dependent variable modelsProblem-solving approach assumes no prior knowledge of econometrics emphasising intuition rather than formulae, giving students the skills and confidence to estimate and interpret models Detailed examples and case studies from finance show students how techniques are applied in real researchample instructions and output from the popular computer package EViews enable students to implement models themselves and understand how to interpret resultsGives advice on planning and executing a project in empirical finance, preparing students for using econometrics in practiceCovers important modern topics such as time-series forecasting, volatility modelling, switching models and simulation methodsThoroughly class-tested in leading finance schools