Introductory Econometrics for Finance 3rd Edition

Contents
List of figures
List of tables
List of boxes
List of screenshots
Preface to the third edition
Acknowledgements
1 Introduction
	1.1 What is econometrics?
	1.2 Is financial econometrics different from ‘economic econometrics’?
	1.3 Types of data
	1.4 Returns in financial modelling
	1.5 Steps involved in formulating an econometric model
	1.6 Points to consider when reading articles in empirical finance
	1.7 A note on Bayesian versus classical statistics
	1.8 An introduction to EViews
	1.9 Further reading
	1.10 Outline of the remainder of this book
2 Mathematical and statistical foundations
	2.1 Functions
	2.2 Differential calculus
	2.3 Matrices
	2.4 Probability and probability distributions
	2.5 Descriptive statistics
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 Simple linear regression in EViews – estimation of an optimal hedge ratio
	3.6 The assumptions underlying the classical linear regression model
	3.7 Properties of the OLS estimator
	3.8 Precision and standard errors
	3.9 An introduction to statistical inference
	3.10 A special type of hypothesis test: the t-ratio
	3.11 An example of a simple t-test of a theory in finance: can US mutual funds beat the market?
	3.12 Can UK unit trust managers beat the market?
	3.13 The overreaction hypothesis and the UK stock market
	3.14 The exact significance level
	3.15 Hypothesis testing in EViews – example 1: hedging revisited
	3.16 Hypothesis testing in EViews – example 2: the CAPM
	Appendix: Mathematical derivations of CLRM results
4 Further development and analysis of the classical linear regression model
	4.1 Generalising the simple model to multiple linear regression
	4.2 The constant term
	4.3 How are the parameters (the elements of the β vector) calculated in the generalised case?
	4.4 Testing multiple hypotheses: the F-test
	4.5 Sample EViews output for multiple hypothesis tests
	4.6 Multiple regression in EViews using an APT-style model
	4.7 Data mining and the true size of the test
	4.8 Goodness of fit statistics
	4.9 Hedonic pricing models
	4.10 Tests of non-nested hypotheses
	4.11 Quantile regression
	Appendix 4.1: Mathematical derivations of CLRM results
	Appendix 4.2: A brief introduction to factor models and principal components analysis
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 and a discussion of model-building philosophies
	5.15 Determinants of sovereign credit ratings
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 Constructing ARMA models in EViews
	6.9 Examples of time series modelling in finance
	6.10 Exponential smoothing
	6.11 Forecasting in econometrics
	6.12 Forecasting using ARMA models in EViews
	6.13 Exponential smoothing models in EViews
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 πs?
	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 to modelling bid–ask spreads and trading activity
	7.10 Simultaneous equations modelling using EViews
	7.11 Vector autoregressive models
	7.12 Does the VAR include contemporaneous terms?
	7.13 Block significance and causality tests
	7.14 VARs with exogenous variables
	7.15 Impulse responses and variance decompositions
	7.16 VAR model example: the interaction between property returns and the macroeconomy
	7.17 VAR estimation in EViews
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 Testing for unit roots in EViews
	8.4 Cointegration
	8.5 Equilibrium correction or error correction models
	8.6 Testing for cointegration in regression: a residuals-based approach
	8.7 Methods of parameter estimation in cointegrated systems
	8.8 Lead–lag and long-term relationships between spot and futures markets
	8.9 Testing for and estimating cointegrating systems using the Johansen technique based on VARs
	8.10 Purchasing power parity
	8.11 Cointegration between international bond markets
	8.12 Testing the expectations hypothesis of the term structure of interest rates
	8.13 Testing for cointegration and modelling cointegrated systems using EViews
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 GJR and EGARCH in EViews
	9.15 Tests for asymmetries in volatility
	9.16 GARCH-in-mean
	9.17 Uses of GARCH-type models including volatility forecasting
	9.18 Testing non-linear restrictions or testing hypotheses about non-linear models
	9.19 Volatility forecasting: some examples and results from the literature
	9.20 Stochastic volatility models revisited
	9.21 Forecasting covariances and correlations
	9.22 Covariance modelling and forecasting in finance: some examples
	9.23 Simple covariance models
	9.24 Multivariate GARCH models
	9.25 Direct correlation models
	9.26 Extensions to the basic multivariate GARCH model
	9.27 A multivariate GARCH model for the CAPM with time-varying covariances
	9.28 Estimating a time-varying hedge ratio for FTSE stock index returns
	9.29 Multivariate stochastic volatility models
	9.30 Estimating multivariate GARCH models using EViews
	Appendix: Parameter estimation using maximum likelihood
10 Switching models
	10.1 Motivations
	10.2 Seasonalities in financial markets: introduction and literature review
	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 Estimating Markov switching models in EViews
	10.9 Threshold autoregressive models
	10.10 Estimation of threshold autoregressive models
	10.11 Specification tests in the context of Markov switching and threshold autoregressive models: a cautionary note
	10.12 A SETAR model for the French franc–German mark exchange rate
	10.13 Threshold models and the dynamics of the FTSE 100 index and index futures markets
	10.14 A note on regime switching models and forecasting accuracy
11 Panel data
	11.1 Introduction – what are panel techniques and why are they used?
	11.2 What panel techniques are available?
	11.3 The fixed effects model
	11.4 Time-fixed effects models
	11.5 Investigating banking competition using a fixed effects model
	11.6 The random effects model
	11.7 Panel data application to credit stability of banks in Central and Eastern Europe
	11.8 Panel data with EViews
	11.9 Panel unit root and cointegration tests
	11.10 Further reading
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 – the choice between financing methods
	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
	12.14 Limited dependent variable models in EViews
	Appendix: The maximum likelihood estimator for logit and probit models
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 to econometric or financial problem solving
	13.7 An example of Monte Carlo simulation in econometrics: deriving a set of critical values for a Dickey–Fuller test
	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
14 Conducting empirical research or doing a project or dissertation in finance
	14.1 What is an empirical research project and what is it for?
	14.2 Selecting the topic
	14.3 Sponsored or independent research?
	14.4 The research proposal
	14.5 Working papers and literature on the internet
	14.6 Getting the data
	14.7 Choice of computer software
	14.8 Methodology
	14.9 Event studies
	14.10 Tests of the CAPM and the Fama–French Methodology
	14.11 How might the finished project look?
	14.12 Presentational issues
	Appendix 1 Sources of data used in this book
	Appendix 2 Tables of statistical distributions
Glossary
References
Index