Introductory Econometrics for Finance

✍ Scribed by Chris Brooks

• Publisher: Cambridge University Press
• Year: 2019
• Tongue: English
• Leaves: 891
• Edition: 4
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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