INTRODUCTORY ECONOMETRICS FOR FINANCE:SECOND EDITION【2025|PDF|Epub|mobi|kindle电子书版本百度云盘下载】

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1 Introduction1

1.1 What is econometrics？1

1.2 Is financial econometrics different from ‘economic econometrics？2

1.3 Types of data3

1.4 Returns in financial modelling7

1.5 Steps involved in formulating an econometric model9

1.6 Points to consider when reading articles in empirical finance10

1.7 Econometric packages for modelling financial data11

1.8 Outline of the remainder of this book22

1.9 Further reading25

Appendix：Econometric software package suppliers26

2 A brief overview of the classical linear regression model27

2.1 What is a regression model？27

2.2 Regression versus correlation28

2.3 Simple regression28

2.4 Some further terminology37

2.5 Simple linear regression in EViews-estimation of an optimal hedge ratio40

2.6 The assumptions underlying the classical linear regression model43

2.7 Properties of the OLS estimator44

2.8 Precision and standard errors46

2.9 An introduction to statistical inference51

2.10 A special type of hypothesis test：the t-ratio65

2.11 An example of the use of a simple t-test to test a theory in finance：can US mutual funds beat the market？67

2.12 Can UK unit trust managers beat the market？69

2.13 The overreaction hypothesis and the UK stock market71

2.14 The exact significance level74

2.15 Hypothesis testing in EViews-example 1：hedging revisited75

2.16 Estimation and hypothesis testing in EViews-example 2：the CAPM77

Appendix：Mathematical derivations of CLRM results81

3 Further development and analysis of the classical linear regression model88

3.1 Generalising the simple model to multiple linear regression88

3.2 The constant term89

3.3 How are the parameters（the elements of the β vector）calculated in the generalised case？91

3.4 Testing multiple hypotheses：the F-test93

3.5 Sample EViews output for multiple hypothesis tests99

3.6 Multiple regression in EViews using an APT-style model99

3.7 Data mining and the true size of the test105

3.8 Goodness of fit statistics106

3.9 Hedonic pricing models112

3.10 Tests of non-nested hypotheses115

Appendix 3.1：Mathematical derivations of CLRM results117

Appendix 3.2：A brief introduction to factor models and principal components analysis120

4 Classical linear regression model assumptions and diagnostic tests129

4.1 Introduction129

4.2 Statistical distributions for diagnostic tests130

4.3 Assumption 1：E（ut）＝0131

4.4 Assumption 2：var（ut）＝σ2＜∞132

4.5 Assumption 3：cov（ui，uj）＝0 for i≠j139

4.6 Assumption 4：the xt are non-stochastic160

4.7 Assumption 5：the disturbances are normally distributed161

4.8 Multicollinearity170

4.9 Adopting the wrong functional form174

4.10 Omission of an important variable178

4.11 Inclusion of an irrelevant variable179

4.12 Parameter stability tests180

4.13 A strategy for constructing econometric models and a discussion of model-building philosophies191

4.14 Determinants of sovereign credit ratings194

5 Univariate time series modelling and forecasting206

5.1 Introduction206

5.2 Some notation and concepts207

5.3 Moving average processes211

5.4 Autoregressive processes215

5.5 The partial autocorrelation function222

5.6 ARMA processes223

5.7 Building ARMA models：the Box-Jenlkins approach230

5.8 Constructing ARMA models in EViews234

5.9 Examples of time series modelling in finance239

5.10 Exponential smoothing241

5.11 Forecasting in econometrics243

5.12 Forecasting using ARMA models in EViews256

5.13 Estimating exponential smoothing models using EViews258

6 Multivariate models265

6.1 Motivations265

6.2 Simultaneous equations bias268

6.3 So how can simultaneous equations models be validly estimated？269

6.4 Can the original coefficients be retrieved from the πs？269

6.5 Simultaneous equations in finance272

6.6 A definition of exogeneity273

6.7 Triangular systems275

6.8 Estimation procedures for simultaneous equations systems276

6.9 An application of a simultaneous equations approach to modelling bid-ask spreads and trading activity279

6.10 Simultaneous equations modelling using EViews285

6.11 Vector autoregressive models290

6.12 Does the VAR include contemporaneous terms？295

6.13 Block significance and causality tests297

6.14 VARs with exogenous variables298

6.15 Impulse responses and variance decompositions298

6.16 VAR model example：the interaction between property returns and the macroeconomy302

6.17 VAR estimation in EViews308

7 Modelling long-run relationships in finance318

7.1 Stationarity and unit root testing318

7.2 Testing for unit roots in EViews331

7.3 Cointegration335

7.4 Equilibrium correction or error correction models337

7.5 Testing for cointegration in regression：a residuals-based approach339

7.6 Methods of parameter estimation in cointegrated systems341

7.7 Lead-lag and long-term relationships between spot and futures markets343

7.8 Testing for and estimating cointegrating systems using the Johansen technique based on VARs350

7.9 Purchasing power parity355

7.10 Cointegration between international bond markets357

7.11 Testing the expectations hypothesis of the term structure of interest rates362

7.12 Testing for cointegration and modelling cointegrated systems using EViews365

8 Modelling volatility and correlation379

8.1 Motivations：an excursion into non-linearity land379

8.2 Models for volatility383

8.3 Historical volatility383

8.4 Implied volatility models384

8.5 Exponentially weighted moving average models384

8.6 Autoregressive volatility models385

8.7 Autoregressive conditionally heteroscedastic（ARCH）models386

8.8 Generalised ARCH（GARCH）models392

8.9 Estimation of ARCH/GARCH models394

8.10 Extensions to the basic GARCH model404

8.11 Asymmetric GARCH models404

8.12 The GJR model405

8.13 The EGARCH model406

8.14 GJR and EGARCH in EViews406

8.15 Tests for asymmetries in volatility408

8.16 GARCH-in-mean409

8.17 Uses of GARCH-type models including volatility forecasting411

8.18 Testing non-linear restrictions or testing hypotheses about non-linear models417

8.19 Volatility forecasting：some examples and results from the literature420

8.20 Stochastic volatility models revisited427

8.21 Forecasting covariances and correlations428

8.22 Covariance modelling and forecasting in finance：some examples429

8.23 Historical covariance and correlation431

8.24 Implied covariance models431

8.25 Exponentially weighted moving average model for covariances432

8.26 Multivariate GARCH models432

8.27 A multivariate GARCH model for the CAPM with time-varying covariances436

8.28 Estimating a time-varying hedge ratio for FTSE stock index returns437

8.29 Estimating multivariate GARCH models using EViews441

Appendix：Parameter estimation using maximum likelihood444

9 Switching models451

9.1 Motivations451

9.2 Seasonalities in financial markets：introduction and literature review454

9.3 Modelling seasonality in financial data455

9.4 Estimating simple piecewise linear functions462

9.5 Marlkov switching models464

9.6 A Markov switching model for the real exchange rate466

9.7 A Marlkov switching model for the gilt-equity yield ratio469

9.8 Threshold autoregressive models473

9.9 Estimation of threshold autoregressive models474

9.10 Specification tests in the context of Markov switching and threshold autoregressive models：a cautionary note476

9.11 A SETAR model for the French franc-German mark exchange rate477

9.12 Threshold models and the dynamics of the FTSE 100 index and index futures markets480

9.13 A note on regime switching models and forecasting accuracy484

10 Panel data487

10.1 Introduction-what are panel techniques and why are they used？487

10.2 What panel techniques are available？489

10.3 The fixed effects model490

10.4 Time-fixed effects models493

10.5 Investigating banking competition using a fixed effects model494

10.6 The random effects model498

10.7 Panel data application to credit stability of banks in Central and Eastern Europe499

10.8 Panel data with EViews502

10.9 Further reading509

11 Limited dependent variable models511

11.1 Introduction and motivation511

11.2 The linear probability model512

11.3 The logit model514

11.4 Using a logit to test the pecking order hypothesis515

11.5 The probit model517

11.6 Choosing between the logit and probit models518

11.7 Estimation of limited dependent variable models518

11.8 Goodness of fit measures for linear dependent variable models519

11.9 Multinomial linear dependent variables521

11.10 The pecking order hypothesis revisited-the choice between financing methods525

11.11 Ordered response linear dependent variables models527

11.12 Are unsolicited credit ratings biased downwards？An ordered probit analysis528

11.13 Censored and truncated dependent variables533

11.14 Limited dependent variable models in EViews537

Appendix：The maximum likelihood estimator for logit and probit models544

12 Simulation methods546

12.1 Motivations546

12.2 Monte Carlo simulations547

12.3 Variance reduction techniques549

12.4 Bootstrapping553

12.5 Random number generation557

12.6 Disadvantages of the simulation approach to econometric or financial problem solving558

12.7 An example of Monte Carlo simulation in econometrics：deriving a set of critical values for a Dickey-Fuller test559

12.8 An example of how to simulate the price of a financial option565

12.9 An example of bootstrapping to calculate capital risk requirements571

13 Conducting empirical research or doing a project or dissertation in finance585

13.1 What is an empirical research project and what is it for？585

13.2 Selecting the topic586

13.3 Sponsored or independent research？590

13.4 The research proposal590

13.5 Working papers and literature on the internet591

13.6 Getting the data591

13.7 Choice of computer software593

13.8 How might the finished project look？593

13.9 Presentational issues597

14 Recent and future developments in the modelling of financial time series598

14.1 Summary of the book598

14.2 What was not covered in the book598

14.3 Financial econometrics：the future？602

14.4 The final word606

Appendix 1 A review of some fundamental mathematical and statistical concepts607

A1 Introduction607

A2 Characteristics of probability distributions607

A3 Properties of logarithms608

A4 Differential calculus609

A5 Matrices611

A6 The eigenvalues of a matrix614

Appendix 2 Tables of statistical distributions616

Appendix 3 Sources of data used in this book628

References629

Index641