Holdings: Introductory econometrics for finance

Introductory econometrics for finance:

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Bibliographic Details
Main Author:	Brooks, Chris 1971- (Author)
Format:	Book
Language:	English
Published:	Cambridge Cambridge Univ. Press 2008
Edition:	2. ed.
Subjects:	Finanzmarkt Multivariate Analyse Regression Simulation Theorie Zeitreihenanalyse Ökonometrie Ökonometrisches Modell Econometrics Finance > Econometric models Finanzmathematik Statistik Kreditmarkt Lehrbuch
Online Access:	Inhaltsverzeichnis
Item Description:	Hier auch später ersch., unveränd. Nachdr.; Literaturverz. S. 629 - 640
Physical Description:	XXIII, 648 S. Ill., graph. Darst.
ISBN:	9780521694681 052169468X 9780521873062

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MARC


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adam_text	Contents List of figures page xii List of tables xiv List of boxes xvi List of Screenshots xvii Preface to the second edition xix Acknowledgements xxiv 1 Introduction 1 1.1 What is econometrics? 1 1.2 Is financial econometrics different from economic econometrics? 2 1.3 Types of data 3 1.4 Returns in financial modelling 7 1.5 Steps involved in formulating an econometric model 9 1.6 Points to consider when reading articles in empirical finance 10 1.7 Econometric packages for modelling financial data 11 1.8 Outline of the remainder of this book 22 1.9 Further reading 25 Appendix: Econometric software package suppliers 26 2 A brief overview of the classical linear regression model 27 2.1 What is a regression model? 27 2.2 Regression versus correlation 28 2.3 Simple regression 28 2.4 Some further terminology 37 2.5 Simple linear regression in EViews - estimation of an optimal hedge ratio 40 2.6 The assumptions underlying the classical linear regression model 43 2.7 Properties of the OLS estimator 44 2.8 Precision and standard errors 46 2.9 An introduction to statistical inference 51 2.10 A special type of hypothesis test: the t-ratio 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? 2.12 Can UK unit trust managers beat the market? 2.13 The overreaction hypothesis and the UK stock market 2.14 The exact significance level 2.15 Hypothesis testing in EViews - example 1: hedging revisited 2.16 Estimation and hypothesis testing in EViews - example 2: the CAPM Appendix: Mathematical derivations of CLRM results 3 Further development and analysis of the classical linear regression model 3.1 Generalising the simple model to multiple linear regression 3.2 The constant term 3.3 How are the parameters (the elements of the β vector) calculated in the generalised case? 3.4 Testing multiple hypotheses: the F-test 3.5 Sample EViews output for multiple hypothesis tests 3.6 Multiple regression in EViews using an APT-style model 3.7 Data mining and the true size of the test 3.8 Goodness of fit statistics 3.9 Hedonic pricing models 3.10 Tests of non-nested hypotheses Appendix 3.1: Mathematical derivations of CLRM results Appendix 3.2: A brief introduction to factor models and principal components analysis 4 Classical linear regression model assumptions and diagnostic tests 4.1 Introduction 4.2 Statistical distributions for diagnostic tests 4.3 Assumption 1: E{ut) = 0 4.4 Assumption 2: var(«t) = σ2 < oo 4.5 Assumption 3: covO,·, u¡) = 0 for і φ j 4.6 Assumption 4: the xt are non-stochastic 4.7 Assumption 5: the disturbances are normally distributed 4.8 Multicollinearity 4.9 Adopting the wrong functional form 4.10 Omission of an important variable 4.11 Inclusion of an irrelevant variable 4.12 Parameter stability tests 180 4.13 A strategy for constructing econometric models and a discussion of model-building philosophies 191 4.14 Determinants of sovereign credit ratings 194 5 Univariate time series modelling and forecasting 206 5.1 Introduction 206 5.2 Some notation and concepts 207 5.3 Moving average processes 211 5.4 Autoregressive processes 215 5.5 The partial autocorrelation function 222 5.6 ARMA processes 223 5.7 Building ARMA models: the Box-Jenkins approach 230 5.8 Constructing ARMA models in EViews 234 5.9 Examples of time series modelling in finance 239 5.10 Exponential smoothing 241 5.11 Forecasting in econometrics 243 5.12 Forecasting using ARMA models in EViews 256 5.13 Estimating exponential smoothing models using EViews 258 6 IVI ulti variate models 265 6.1 Motivations 265 6.2 Simultaneous equations bias 268 6.3 So how can simultaneous equations models be validly estimated? 269 6.4 Can the original coefficients be retrieved from the жѕ7 269 6.5 Simultaneous equations in finance 272 6.6 A definition of exogeneity 273 6.7 Triangular systems 275 6.8 Estimation procedures for simultaneous equations systems 276 6.9 An application of a simultaneous equations approach to modelling bid-ask spreads and trading activity 279 6.10 Simultaneous equations modelling using EViews 285 6.11 Vector autoregressive models 290 6.12 Does the VAR include contemporaneous terms? 295 6.13 Block significance and causality tests 297 6.14 VARs with exogenous variables 298 6.15 Impulse responses and variance decompositions 298 6.16 VAR model example: the interaction between property returns and the macroeconomy 302 6.17 VAR estimation in EViews 308 7 Modelling long-run relationships in finance 318 7.1 Stationarity and unit root testing 318 7.2 Testing for unit roots in EViews 331 7.3 Cointegration 335 7.4 Equilibrium correction or error correction models 337 7.5 Testing for cointegration in regression: a residuals-based approach 339 7.6 Methods of parameter estimation in cointegrated systems 341 7.7 Lead-lag and long-term relationships between spot and futures markets 343 7.8 Testing for and estimating cointegrating systems using the Johansen technique based on VARs 350 7.9 Purchasing power parity 355 7.10 Cointegration between international bond markets 357 7.11 Testing the expectations hypothesis of the term structure of interest rates 362 7.12 Testing for cointegration and modelling cointegrated systems using EViews 365 Modelling volatility and correlation 379 Motivations: an excursion into non-linearity land 379 Models for volatility 383 Historical volatility 383 Implied volatility models 384 Exponentially weighted moving average models 384 Autoregressive volatility models 385 Autoregressive conditionally heteroscedastic (ARCH) models 386 Generalised ARCH (GARCH) models 392 Estimation of ARCH/GARCH models 394 Extensions to the basic GARCH model 404 Asymmetric GARCH models 404 The GJR model 405 The EGARCH model 406 GJR and EGARCH in EViews 406 Tests for asymmetries in volatility 408 GARCH-in-mean 409 Uses of GARCH-type models including volatility forecasting 411 Testing non-linear restrictions or testing hypotheses about non-linear models 417 Volatility forecasting: some examples and results from the literature 420 Stochastic volatility models revisited 427 8.21 Forecasting covariances and correlations 428 8.22 Covariance modelling and forecasting in finance: some examples 429 8.23 Historical covariance and correlation 431 8.24 Implied covariance models 431 8.25 Exponentially weighted moving average model for covariances 432 8.26 Multivariate GARCH models 432 8.27 A multivariate GARCH model for the С АРМ with time-varying covariances 436 8.28 Estimating a time-varying hedge ratio for FTSE stock index returns 437 8.29 Estimating multivariate GARCH models using EViews 441 Appendix: Parameter estimation using maximum likelihood 444 9 Switching models 451 9.1 Motivations 451 9.2 Seasonalities in financial markets: introduction and literature review 454 9.3 Modelling seasonality in financial data 455 9.4 Estimating simple piecewise linear functions 462 9.5 Markov switching models 464 9.6 A Markov switching model for the real exchange rate 466 9.7 A Markov switching model for the gilt-equity yield ratio 469 9.8 Threshold autoregressive models 473 9.9 Estimation of threshold autoregressive models 474 9.10 Specification tests in the context of Markov switching and threshold autoregressive models: a cautionary note 476 9.11 A SETAR model for the French franc-German mark exchange rate 477 9.12 Threshold models and the dynamics of the FTSE 100 index and index futures markets 480 9.13 A note on regime switching models and forecasting accuracy 484 10 Panel data 487 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 model 490 10.4 Time-fixed effects models 493 10.5 Investigating banking competition using a fixed effects model 494 10.6 The random effects model 498 10.7 Panel data application to credit stability of banks in Central and Eastern Europe 499 10.8 Panel data with EViews 502 10.9 Further reading 509 11 Limited dependent variable models 511 11.1 Introduction and motivation 511 11.2 The linear probability model 512 11.3 The logit model 514 11.4 Using a logit to test the pecking order hypothesis 515 11.5 The probit model 517 11.6 Choosing between the logit and probit models 518 11.7 Estimation of limited dependent variable models 518 11.8 Goodness of fit measures for linear dependent variable models 519 11.9 Multinomial linear dependent variables 521 11.10 The pecking order hypothesis revisited - the choice between financing methods 525 Ordered response linear dependent variables models 527 Are unsolicited credit ratings biased downwards? An ordered probit analysis 528 11.13 Censored and truncated dependent variables 533 11.14 Limited dependent variable models in EViews 537 Appendix: The maximum likelihood estimator for logit and probit models 544 12 Simulation methods 546 12.1 Motivations 546 12.2 Monte Carlo simulations 547 12.3 Variance reduction techniques 549 12.4 Bootstrapping 553 12.5 Random number generation 557 12.6 Disadvantages of the simulation approach to econometric or financial problem solving 558 12.7 An example of Monte Carlo simulation in econometrics: deriving a set of critical values for a Dickey-Fuller test 559 12.8 An example of how to simulate the price of a financial option 565 12.9 An example of bootstrapping to calculate capital risk requirements 571 13 Conducting empirical research or doing a project or dissertation in finance 585 13.1 What is an empirical research project and what is it for? 585 13.2 Selecting the topic 586 13.3 Sponsored or independent research? 590 13.4 The research proposal 590 13.5 Working papers and literature on the internet 591 13.6 Getting the data 591 13.7 Choice of computer software 13.8 How might the finished project look? 13.9 Presentational issues 14 Recent and future developments in the modelling of financial time series 14.1 Summary of the book 14.2 What was not covered in the book 14.3 Financial econometrics: the future? 14.4 The final word 593 593 597 598 598 598 602 606 Appendix 1 A review of some fundamental mathematical and statistical concepts Al Introduction A2 Characteristics of probability distributions A3 Properties of logarithms A4 Differential calculus A5 Matrices A6 The eigenvalues of a matrix Appendix 2 Tables of statistical distributions Appendix 3 Sources of data used in this book 607 607 607 608 609 611 614 616 628 References Index 629 641
adam_txt	Contents List of figures page xii List of tables xiv List of boxes xvi List of Screenshots xvii Preface to the second edition xix Acknowledgements xxiv 1 Introduction 1 1.1 What is econometrics? 1 1.2 Is financial econometrics different from 'economic econometrics? 2 1.3 Types of data 3 1.4 Returns in financial modelling 7 1.5 Steps involved in formulating an econometric model 9 1.6 Points to consider when reading articles in empirical finance 10 1.7 Econometric packages for modelling financial data 11 1.8 Outline of the remainder of this book 22 1.9 Further reading 25 Appendix: Econometric software package suppliers 26 2 A brief overview of the classical linear regression model 27 2.1 What is a regression model? 27 2.2 Regression versus correlation 28 2.3 Simple regression 28 2.4 Some further terminology 37 2.5 Simple linear regression in EViews - estimation of an optimal hedge ratio 40 2.6 The assumptions underlying the classical linear regression model 43 2.7 Properties of the OLS estimator 44 2.8 Precision and standard errors 46 2.9 An introduction to statistical inference 51 2.10 A special type of hypothesis test: the t-ratio 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? 2.12 Can UK unit trust managers beat the market? 2.13 The overreaction hypothesis and the UK stock market 2.14 The exact significance level 2.15 Hypothesis testing in EViews - example 1: hedging revisited 2.16 Estimation and hypothesis testing in EViews - example 2: the CAPM Appendix: Mathematical derivations of CLRM results 3 Further development and analysis of the classical linear regression model 3.1 Generalising the simple model to multiple linear regression 3.2 The constant term 3.3 How are the parameters (the elements of the β vector) calculated in the generalised case? 3.4 Testing multiple hypotheses: the F-test 3.5 Sample EViews output for multiple hypothesis tests 3.6 Multiple regression in EViews using an APT-style model 3.7 Data mining and the true size of the test 3.8 Goodness of fit statistics 3.9 Hedonic pricing models 3.10 Tests of non-nested hypotheses Appendix 3.1: Mathematical derivations of CLRM results Appendix 3.2: A brief introduction to factor models and principal components analysis 4 Classical linear regression model assumptions and diagnostic tests 4.1 Introduction 4.2 Statistical distributions for diagnostic tests 4.3 Assumption 1: E{ut) = 0 4.4 Assumption 2: var(«t) = σ2 < oo 4.5 Assumption 3: covO,·, u¡) = 0 for і φ j 4.6 Assumption 4: the xt are non-stochastic 4.7 Assumption 5: the disturbances are normally distributed 4.8 Multicollinearity 4.9 Adopting the wrong functional form 4.10 Omission of an important variable 4.11 Inclusion of an irrelevant variable 4.12 Parameter stability tests 180 4.13 A strategy for constructing econometric models and a discussion of model-building philosophies 191 4.14 Determinants of sovereign credit ratings 194 5 Univariate time series modelling and forecasting 206 5.1 Introduction 206 5.2 Some notation and concepts 207 5.3 Moving average processes 211 5.4 Autoregressive processes 215 5.5 The partial autocorrelation function 222 5.6 ARMA processes 223 5.7 Building ARMA models: the Box-Jenkins approach 230 5.8 Constructing ARMA models in EViews 234 5.9 Examples of time series modelling in finance 239 5.10 Exponential smoothing 241 5.11 Forecasting in econometrics 243 5.12 Forecasting using ARMA models in EViews 256 5.13 Estimating exponential smoothing models using EViews 258 6 IVI ulti variate models 265 6.1 Motivations 265 6.2 Simultaneous equations bias 268 6.3 So how can simultaneous equations models be validly estimated? 269 6.4 Can the original coefficients be retrieved from the жѕ7 269 6.5 Simultaneous equations in finance 272 6.6 A definition of exogeneity 273 6.7 Triangular systems 275 6.8 Estimation procedures for simultaneous equations systems 276 6.9 An application of a simultaneous equations approach to modelling bid-ask spreads and trading activity 279 6.10 Simultaneous equations modelling using EViews 285 6.11 Vector autoregressive models 290 6.12 Does the VAR include contemporaneous terms? 295 6.13 Block significance and causality tests 297 6.14 VARs with exogenous variables 298 6.15 Impulse responses and variance decompositions 298 6.16 VAR model example: the interaction between property returns and the macroeconomy 302 6.17 VAR estimation in EViews 308 7 Modelling long-run relationships in finance 318 7.1 Stationarity and unit root testing 318 7.2 Testing for unit roots in EViews 331 7.3 Cointegration 335 7.4 Equilibrium correction or error correction models 337 7.5 Testing for cointegration in regression: a residuals-based approach 339 7.6 Methods of parameter estimation in cointegrated systems 341 7.7 Lead-lag and long-term relationships between spot and futures markets 343 7.8 Testing for and estimating cointegrating systems using the Johansen technique based on VARs 350 7.9 Purchasing power parity 355 7.10 Cointegration between international bond markets 357 7.11 Testing the expectations hypothesis of the term structure of interest rates 362 7.12 Testing for cointegration and modelling cointegrated systems using EViews 365 Modelling volatility and correlation 379 Motivations: an excursion into non-linearity land 379 Models for volatility 383 Historical volatility 383 Implied volatility models 384 Exponentially weighted moving average models 384 Autoregressive volatility models 385 Autoregressive conditionally heteroscedastic (ARCH) models 386 Generalised ARCH (GARCH) models 392 Estimation of ARCH/GARCH models 394 Extensions to the basic GARCH model 404 Asymmetric GARCH models 404 The GJR model 405 The EGARCH model 406 GJR and EGARCH in EViews 406 Tests for asymmetries in volatility 408 GARCH-in-mean 409 Uses of GARCH-type models including volatility forecasting 411 Testing non-linear restrictions or testing hypotheses about non-linear models 417 Volatility forecasting: some examples and results from the literature 420 Stochastic volatility models revisited 427 8.21 Forecasting covariances and correlations 428 8.22 Covariance modelling and forecasting in finance: some examples 429 8.23 Historical covariance and correlation 431 8.24 Implied covariance models 431 8.25 Exponentially weighted moving average model for covariances 432 8.26 Multivariate GARCH models 432 8.27 A multivariate GARCH model for the С АРМ with time-varying covariances 436 8.28 Estimating a time-varying hedge ratio for FTSE stock index returns 437 8.29 Estimating multivariate GARCH models using EViews 441 Appendix: Parameter estimation using maximum likelihood 444 9 Switching models 451 9.1 Motivations 451 9.2 Seasonalities in financial markets: introduction and literature review 454 9.3 Modelling seasonality in financial data 455 9.4 Estimating simple piecewise linear functions 462 9.5 Markov switching models 464 9.6 A Markov switching model for the real exchange rate 466 9.7 A Markov switching model for the gilt-equity yield ratio 469 9.8 Threshold autoregressive models 473 9.9 Estimation of threshold autoregressive models 474 9.10 Specification tests in the context of Markov switching and threshold autoregressive models: a cautionary note 476 9.11 A SETAR model for the French franc-German mark exchange rate 477 9.12 Threshold models and the dynamics of the FTSE 100 index and index futures markets 480 9.13 A note on regime switching models and forecasting accuracy 484 10 Panel data 487 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 model 490 10.4 Time-fixed effects models 493 10.5 Investigating banking competition using a fixed effects model 494 10.6 The random effects model 498 10.7 Panel data application to credit stability of banks in Central and Eastern Europe 499 10.8 Panel data with EViews 502 10.9 Further reading 509 11 Limited dependent variable models 511 11.1 Introduction and motivation 511 11.2 The linear probability model 512 11.3 The logit model 514 11.4 Using a logit to test the pecking order hypothesis 515 11.5 The probit model 517 11.6 Choosing between the logit and probit models 518 11.7 Estimation of limited dependent variable models 518 11.8 Goodness of fit measures for linear dependent variable models 519 11.9 Multinomial linear dependent variables 521 11.10 The pecking order hypothesis revisited - the choice between financing methods 525 Ordered response linear dependent variables models 527 Are unsolicited credit ratings biased downwards? An ordered probit analysis 528 11.13 Censored and truncated dependent variables 533 11.14 Limited dependent variable models in EViews 537 Appendix: The maximum likelihood estimator for logit and probit models 544 12 Simulation methods 546 12.1 Motivations 546 12.2 Monte Carlo simulations 547 12.3 Variance reduction techniques 549 12.4 Bootstrapping 553 12.5 Random number generation 557 12.6 Disadvantages of the simulation approach to econometric or financial problem solving 558 12.7 An example of Monte Carlo simulation in econometrics: deriving a set of critical values for a Dickey-Fuller test 559 12.8 An example of how to simulate the price of a financial option 565 12.9 An example of bootstrapping to calculate capital risk requirements 571 13 Conducting empirical research or doing a project or dissertation in finance 585 13.1 What is an empirical research project and what is it for? 585 13.2 Selecting the topic 586 13.3 Sponsored or independent research? 590 13.4 The research proposal 590 13.5 Working papers and literature on the internet 591 13.6 Getting the data 591 13.7 Choice of computer software 13.8 How might the finished project look? 13.9 Presentational issues 14 Recent and future developments in the modelling of financial time series 14.1 Summary of the book 14.2 What was not covered in the book 14.3 Financial econometrics: the future? 14.4 The final word 593 593 597 598 598 598 602 606 Appendix 1 A review of some fundamental mathematical and statistical concepts Al Introduction A2 Characteristics of probability distributions A3 Properties of logarithms A4 Differential calculus A5 Matrices A6 The eigenvalues of a matrix Appendix 2 Tables of statistical distributions Appendix 3 Sources of data used in this book 607 607 607 608 609 611 614 616 628 References Index 629 641
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id	DE-604.BV023092575
illustrated	Illustrated
index_date	2024-07-02T19:41:37Z
indexdate	2024-07-09T21:10:50Z
institution	BVB
isbn	9780521694681 052169468X 9780521873062
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-016295424
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physical	XXIII, 648 S. Ill., graph. Darst.
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publisher	Cambridge Univ. Press
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spelling	Brooks, Chris 1971- Verfasser (DE-588)1012858766 aut Introductory econometrics for finance Chris Brooks 2. ed. Cambridge Cambridge Univ. Press 2008 XXIII, 648 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Hier auch später ersch., unveränd. Nachdr.; Literaturverz. S. 629 - 640 Finanzmarkt stw Multivariate Analyse stw Regression stw Simulation stw Theorie stw Zeitreihenanalyse stw Ökonometrie stw Ökonometrisches Modell stw Ökonometrisches Modell Econometrics Finance Econometric models Finanzmathematik (DE-588)4017195-4 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Kreditmarkt (DE-588)4073788-3 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Ökonometrie (DE-588)4132280-0 s Finanzmathematik (DE-588)4017195-4 s Statistik (DE-588)4056995-0 s DE-604 Kreditmarkt (DE-588)4073788-3 s 1\p DE-604 Überarbeitung als 3. ed. 2014 978-1-107-03466-2 978-1-107-66145-5 (DE-604)BV041747422 Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016295424&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk
spellingShingle	Brooks, Chris 1971- Introductory econometrics for finance Finanzmarkt stw Multivariate Analyse stw Regression stw Simulation stw Theorie stw Zeitreihenanalyse stw Ökonometrie stw Ökonometrisches Modell stw Ökonometrisches Modell Econometrics Finance Econometric models Finanzmathematik (DE-588)4017195-4 gnd Statistik (DE-588)4056995-0 gnd Kreditmarkt (DE-588)4073788-3 gnd Ökonometrie (DE-588)4132280-0 gnd
subject_GND	(DE-588)4017195-4 (DE-588)4056995-0 (DE-588)4073788-3 (DE-588)4132280-0 (DE-588)4123623-3
title	Introductory econometrics for finance
title_auth	Introductory econometrics for finance
title_exact_search	Introductory econometrics for finance
title_exact_search_txtP	Introductory econometrics for finance
title_full	Introductory econometrics for finance Chris Brooks
title_fullStr	Introductory econometrics for finance Chris Brooks
title_full_unstemmed	Introductory econometrics for finance Chris Brooks
title_short	Introductory econometrics for finance
title_sort	introductory econometrics for finance
topic	Finanzmarkt stw Multivariate Analyse stw Regression stw Simulation stw Theorie stw Zeitreihenanalyse stw Ökonometrie stw Ökonometrisches Modell stw Ökonometrisches Modell Econometrics Finance Econometric models Finanzmathematik (DE-588)4017195-4 gnd Statistik (DE-588)4056995-0 gnd Kreditmarkt (DE-588)4073788-3 gnd Ökonometrie (DE-588)4132280-0 gnd
topic_facet	Finanzmarkt Multivariate Analyse Regression Simulation Theorie Zeitreihenanalyse Ökonometrie Ökonometrisches Modell Econometrics Finance Econometric models Finanzmathematik Statistik Kreditmarkt Lehrbuch
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016295424&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT brookschris introductoryeconometricsforfinance

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