ISBN 13 : 978-1-107-66145-5

Contents :
Preface to the third edition

1 Introduction
1 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 AnintroductiontoEViews
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 134
4.2 The constant term
4.3 How are the parameters (the elements of the ? 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)=?2<∞
5.5 Assumption 3: cov(ui,uj)=0fori =j
5.6 Assumption 4: thextare 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? 308
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 376
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 423
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 446
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 506
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

Language : English

Print : 3ème

Place of publishing : CAMBRIDGE

Location : Nice Library

Material : Paper

Statement : Présent

Owner : Bibliothèque