Introductory econometrics for finance:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge [u.a.]
Cambridge Univ. Press
2014
|
| Ausgabe: | 3. ed. |
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027193888&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke Literaturverz. S. [697] - 709 |
| Umfang: | XXIV, 716 S. graph. Darst. |
| ISBN: | 9781107034662 9781107661455 |
Internformat
MARC
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| 100 | 1 | |a Brooks, Chris |d 1971- |e Verfasser |0 (DE-588)1012858766 |4 aut | |
| 245 | 1 | 0 | |a Introductory econometrics for finance |c Chris Brooks |
| 250 | |a 3. ed. | ||
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 2014 | |
| 300 | |a XXIV, 716 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
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| 787 | 0 | 8 | |i Überarbeitung als |b Fourth edition |d 2019 |z 978-1-108-43682-3 |z 978-1-108-42253-6 |w (DE-604)BV045486555 |
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-027193888 | |
Datensatz im Suchindex
| _version_ | 1819303717157994496 |
|---|---|
| adam_text | Contents
List Of figures page
XÜ
List of tables
xv
List of boxes
som
List of
Screenshots xix
Preface
co
the thud edition
xxi
Acknowledgements
xxv
1
Introduction
1
1.1
What is econometrics?
2
1.2
Is financial econometrics different from economic
econometrics ?
2
1.3
Types of data
4
1.4
Returns in financial modelling
7
1.5
Steps involved in formulating an econometric model
11
1.6
Points to consider when reading articles in empirical
finance
12
1.7
A note on
В
ayesian versus classical statistics
13
1.8
An introduction to EViews
14
1.9
Further reading
24
1.10
Outline
oí
the remainder of this book
24
2
Mathematical and statistical foundations
28
2.1
Functions
28
2.2
Differential calculus
37
2.3
Matrices
41
2.4
Probability and probability distributions
56
2.5
Descriptive statistics
61
3
A brief overview of the classical linear regression model
75
3.1
What is a regression model?
75
3.2
Regression versus correlation
76
3.3
Simple regression v
. 76
3.4
Some further terminology.
, 84
3.5
Simple linear regression in EViews
-
estimation of an optimal
hedge ratio
86
vi
* Contents
3.6
The assumptions underlying the classical linear regression
model
90
3.7
Properties of
che OLS
estimator
91
3.8
Precision and standard errors
93
3.9
An introduction to statistical inference
98
3.10
A. special type of hypothesis test: the
í
-rano
111
3.11
/in. example of a simple /-test
oí
a theory
m
finance: can US
mutual funds beat the market?
113
3.12
Can UX unit trust managers beat the market?
115
3.13
The. over reaction hypothesis and the UK stock market
116
3.14
The exact significance level
120
3.15
Hypothesis testing in
Б
Views
-
example
1:
hedging revisited
121
3.16
Hypothesis testing
m F.
Views
-
example
2:
the CAP
M
1.23
Appendix: Mathematical derivations of CLRjM results
1/- /
4
Further development and analysis of the classical linear
regression model
134
4.1
Generalising the simple model to multiple linear regression.
134
4.2
The constant term
135
4.3
How are the parameters (the elements of the
β
vector) calculated
m
the generalised case?
137
4.4
lesting
multiple hypotheses: the
F-test
139
4.5
Sample BViews output for multiple hypothesis tests
144
4.6
Multiple regression in EVicws using an APT-styJe model
145
4.7
Data mining and the true size of the test
150
4.8
Goodness of fit statistics
151
4.9
Hedonic pricing models
156
4.10
Tests of non-nested hypotheses
159
4.11
Quantile regression
161
Appendix
4.1:
Mathematical derivations of CLRJM results
168
Appendix
4.2:
A. brief introduction to
tactor
models and principal
components analysis
170
5
Classical linear regression model assumptions and diagnostic tests
179
5.1
Introduction
. 179
5.2
Statistical distributions for diagnostic tests
180
5.3
Assumption
1:
E(ut)
= 0 181
5.4
Assumption.
2:
var(u;) ~
σ2
<
со
181
5.5
Assumption
3:
cov(u,, w,)
— 0
for
ι φ
j
188
5.6
Assumption
4:
the xt are
non-
stochastic
208
5.7
Assumption
5:
the disturbances are normally distributed
209
5.8
Muhicollinearity
217
5.9
Adopting the wrong functional form
220
5.10
Omission of an important variable
224
5.11
Inclusion of an irrelevant variable
225
Contents *
vu
5.12 Parameter
stability tests
226
5.13
.Measurement errors
235
5.14
A strategy for constructing econometric models and a discussion
oí
model-building philosophies
238
5.15
Determinants of sovereign credit ratings
240
6
Univariate time series modelling and forecasting
251
6.1
Introduction
251
6.2
Some notation and concepts
252
6.3
Moving average processes
256
6.4 Autoregressive
processes
259
6.5
The partia.1 autocorrelation function
266
6.6
ARMA
processes
268
6.7
Building
ARMA
models: the Box-Jenkins approach
273
6.8
Constructing
ARMA,
models in F, Views
276
6.9
Examples or time
senes
modelling in finance
281
6.10
Exponential smoothing
283
6.11
Forecasting
m
econometrics
285
6.12
Forecasting using AR.M.A models in EViews
296
6.13
Exponential smoothing models
m EViews
299
7
Multivariate models
305
7.1
Motivations
305
Simultaneous equations bias
307
So how can simultaneous equations models be
validi
y
estimated?
308
Can the original coefficients be retrieved
írom
the
π
s?
309
Simultaneous equations in finance
311
A definition of
exogenei
ty
312
Triangular systems
31.4
Estimation procedures for simultaneous equations systems
315
.Ал.
application of a simultaneous equations approach to modelling
bid -ask spreads and trading activity
318
Simultaneous equations modelling using EViews
323
Vector
autoregressive
models
326
Does the
VAR
include contemporaneous terms?
332
7.13
Block, significance and causality tests
333
7.1.4
VA.Rs with exogenous variables
335
7.15
Impulse responses and variance decompositions
336
7.16
VAR.
model example: the interaction between property returns
and the macro
e co
η ο
my
338
7.17
VAR
estimation
m
EViews
344
Modelling long-run relationships in finance
353
8.1
Stationarity and unit root testing
353
8.2
Testó
for unit roots in the presence of structural breaks
365
7.
2
—7
/
.3
7.
4
7.
.5
η
6
7.
7.
.8
7.
9
7.
10
7.
11
7.
12
viii
* Contents
В.
3
Testing for
u
ml roots
m
Б
Vi e
ws
369
8.4
Comtegranon
373
8.5
.Equilibrium correction or error correction, models
375
8.6
Testing for
comtegration m
regression: a residuals-based approach
376
8.7
Methods of parameter
estimation
in coiniegratcd systems
377
8.8
Lead -lag and long term relationships between spot and futures
markets
380
8.9
Testing
for and estimating cointegratmg systems using the
Johansen technique based on VARs
386
8.10
Purchasing power parity
390
8.11
Comtegranon
between
international
bond markets
391
8.12
Testing the expectations hypothesis of the term structure of
interest rates
398
8.13
Testing for comtegraLion and modelling cointegrated systems
using
E
Views
400
9
Modelling volatility and correlation
415
9.1
Motivations: an excursion into
non-
linearity land
415
9.2
Models for
volatili
ty
420
9.3
Historical volatility
420
9.4
Implied volatility models
421
9.5
Exponentially weighted
movi
rig average models
421
9.6
Autoregressive
volatility models
422
9.7
Au
to regressive conditionally beteroscedasne (ARCH) models
423
9.8
Generalised ARCH (GARCH) models
428
9.9
Estimation of ARCH/GARCH models
431
9.1.0
Extensions to the basic GARCH model
439
9.11
Asymmetric GARCH. models
440
9.12
The GJR model
440
9.13
The EG ARCH model
441
9.14
GJR and BGARCH
m EViews
441
9.1.5
Tests for asymmetries in volatility
443
9.16
GARCH
-ι η
-mean
445
9.17
Uses of GARCH-type models including volatility forecasting
446
9.18
Testing nori-lineax restrictions or testing hypotheses about
non-linear models
452
9.19
Volatility forecasting: some examples and results from the
literature
454
9.20
Stochastic volatility models revisited
461
9.21
Forecasting covanances and correlations
463
9.22
Covariance modelling and forecasting in finance: some
examples
464
9.23
Simple covariance models
466
9.24
Multivariate GARCH models
467
9.25
Direct correlation models
471
Contents *
їх
9.26
Extensions
co
the
basic multivanate GARCM
modei
472
9.27
A muInvariate
GÄRCH
model tor the
САРМ
with
11
me
-
v a ry
ing
c
ova
r i a ne e s
474
9.28
Ľsrj
mating a time-varying hedge ratio for: FTS.E stock index
returns
475
9.29
Mullivariate stochastic volatility models
478
9.30
Estimating
mul
livar
iate
GARCH models using EViews
480
Appendix:
Parameter
estimation using maximum likelihood
484
10
Switching models
490
10.1
Motivations
490
10.2
Seasonaliües i.n financia]
markets: introduction and literature
review
492
10.3
Modelling seasonaJity in financial data
493
10.4
Estimating simple piece/wise linear functions
500
10.5
Markov swi
tchun.g
models
502
10.6
A Markov switching model for the real exchange rate
503
10.7
A. Markov switching model for the gilt- equity yield ratio
506
10.8
Estimating Markov switching models in EViews
510
10.9
Threshold
autoregressive
models
513
10.10
Estimation of threshold
autoregressive
models
515
10.11
Specification tests in. the context of Markov switching and
threshold a u to repressive models: a cautionary note
516
10.12
A SETAR. model for the French franc German mark exchange
rate
517
10.13
Threshold models and the dynamics of the
FISE
100
index and
ι η
d e x
fu
tu
т.
es mar
ke ts
519
10.14
A note on regime switching models and forecasting accuracy
523
11
Panel data
526
11.1
Introduction
-
what are panel techniques and why are they used?
526
11.2
What panel techniques are available?
528
11.3
The fixed effects mode.!
529
11.4
Tune-fixed effects models
531
11.5
Investigating banking competition using a fixed effects model
532
11.6
The random effects model
536
11.7
Panel data application to credit stability of banks in Central and
Eastern Europe
537
11.8
Pan el data wi th
E V i e
ws
5 41
11.9
Panel unit root and cointegration tests
547
11.10
Further reading
557
12
Limited dependent variable models
559
12.1
Introduction and motivation
559
12.2
The.
lin
ear
ρ
ro b a b
Dity
model
560
Contents
12.3
The logit model
562
12.4
Using a logit to test the peeking order hypothesis
563
12.5
The
probit
model
565
12.6
Choosing between the logit and
probit
models
565
12.7
Estimation of limited dependent variable models
565
12.8
Goodness of fit measures for linear dependent
variable models
567
12.9
Multinomial linear dependent variables
568
12.10
The pecking order hypothesis revisited
-
the choice between
financing methods
571
12.11
Ordered response linear dependent variables models
574
12.12
Are unsolicited credit ratings biased downwards?
A.n.
ordered
probit
analysis
574
12.13
Censored and truncated dependent variables
579
12.14
Limited dependent variable models in
К
Views
583
Appendix: The maximum likelihood estimator for logit and
probit
models
589
13
Simulation methods
591
13.1
Motivations
591
13.2
Monte Carlo simulations
592
13.3
Variance reduction techniques
593
13.4
Bootstrapping
597
13.5
Random number generation
600
1.3.6
Disadvantages of the. simulation approach to econometric or
financial problem solving
601
13.7
An example of Monte Carlo simulation
ш
econometrics:
deriving a set of critical values for a Dickey--Fuller test.
603
13.8
An example of how to simulate the price of a financial
option
607
13.9
An example of bootstrapping to calculate capital, risk
requirements
613
14
Conducting empirical research or doing a project or
dissertation in finance
626
14.1
What is an empirical research project and what is it for?
626
14.2
Selecting the topic
627
14.3
Sponsored or independent research?
629
14.4
The research proposal
6.31
14.5
Working papers arid literature on the internet
631
14.6
Getting the data
633
1.4.7
Choice of computer software
634
14.8
Methodology
634
14.9
Event studies
634
14.10
Tests of the CAPM and the Fama-French Methodology
648
Contents
XI
14.1.1
How might the finished project .look?
14.12
Presentational issues
Appendix
1
Sources of data used in this book
Appendix2 Tables of statistical distributions
Glossary
References
Index
661
666
667
668
697
710
|
| any_adam_object | 1 |
| author | Brooks, Chris 1971- |
| author_GND | (DE-588)1012858766 |
| author_facet | Brooks, Chris 1971- |
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| author_sort | Brooks, Chris 1971- |
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| callnumber-search | HG173 |
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| callnumber-subject | HG - Finance |
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| ctrlnum | (OCoLC)876210239 (DE-599)BVBBV041747422 |
| dewey-full | 332.01/5195 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 332 - Financial economics |
| dewey-raw | 332.01/5195 |
| dewey-search | 332.01/5195 |
| dewey-sort | 3332.01 45195 |
| dewey-tens | 330 - Economics |
| discipline | Mathematik Wirtschaftswissenschaften |
| edition | 3. ed. |
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| genre | (DE-588)4123623-3 Lehrbuch gnd-content |
| genre_facet | Lehrbuch |
| id | DE-604.BV041747422 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T16:54:08Z |
| institution | BVB |
| isbn | 9781107034662 9781107661455 |
| language | English |
| lccn | 013049908 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027193888 |
| oclc_num | 876210239 |
| open_access_boolean | |
| owner | DE-703 DE-521 DE-355 DE-BY-UBR DE-1047 DE-2070s DE-739 DE-1043 DE-945 DE-634 DE-188 DE-11 DE-19 DE-BY-UBM DE-706 DE-20 DE-Aug4 DE-92 DE-859 |
| owner_facet | DE-703 DE-521 DE-355 DE-BY-UBR DE-1047 DE-2070s DE-739 DE-1043 DE-945 DE-634 DE-188 DE-11 DE-19 DE-BY-UBM DE-706 DE-20 DE-Aug4 DE-92 DE-859 |
| physical | XXIV, 716 S. graph. Darst. |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Cambridge Univ. Press |
| record_format | marc |
| spellingShingle | Brooks, Chris 1971- Introductory econometrics for finance Ökonometrie (DE-588)4132280-0 gnd Finanzmathematik (DE-588)4017195-4 gnd Statistik (DE-588)4056995-0 gnd Kreditmarkt (DE-588)4073788-3 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4017195-4 (DE-588)4056995-0 (DE-588)4073788-3 (DE-588)4123623-3 |
| title | Introductory econometrics for finance |
| title_auth | Introductory econometrics for finance |
| title_exact_search | 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 | Ökonometrie (DE-588)4132280-0 gnd Finanzmathematik (DE-588)4017195-4 gnd Statistik (DE-588)4056995-0 gnd Kreditmarkt (DE-588)4073788-3 gnd |
| topic_facet | Ökonometrie Finanzmathematik Statistik Kreditmarkt Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027193888&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT brookschris introductoryeconometricsforfinance |