Wang Peijie. Financial Econometrics

Second edition. — Routledge, 2009. — 336 p.Extended from the first edition of mainly time series modelling, the new edition also takes in discrete choice models, estimation of censored and truncated samples, as well as panel data analysis that has witnessed phenomenal expansion in application in finance and financial economics since the publication of the first edition of the book. Virtually all major topics on time series, cross-sectional and panel data analysis have been dealt with. Subjects covered include:
unit roots, cointegration and other comovements in time series.
time varying volatility models of the GARCH type and the stochastic volatility approach.
analysis of shock persistence and impulse responses.
Markov switching.
present value relations and data characteristics.
state space models and the Kalman filter.
frequency domain analysis of time series.
limited dependent variables and discrete choice models.
truncated and censored samples.
panel data analysis.
Refreshingly, every chapter has a section of two or more examples and a section of empirical literature, offering the reader the opportunity to practise right away the kind of research going on in the area. This approach helps the reader develop interest, confidence and momentum.
and learning contemporary econometric topics.
Graduate and advanced undergraduate students requiring a broad knowledge of techniques applied in the finance literature, as well as students of financial economics engaged and n empirical enquiry, should find this textbook to be invaluable.
Stochastic processes and financial data generating processes.
Introduction.
Stochastic processes and their properties.
The behaviour of financial variables and beyond.
Commonly applied statistical distributions and their relevance.
Normal distributions.
χ2-distributions.
t-distributions.
F-distributions.
Overview of estimation methods.
Basic OLS procedures.
Basic ML procedures.
Estimation when iid is violated.
General residual distributions in time series and cross-section modelling.
MM and GMM approaches.
Unit roots, cointegration and other comovements in time series.
Unit roots and testing for unit roots.
Cointegration.
Common trends and common cycles.
Examples and cases.
Empirical literature.
Time-varying volatility models: GARCH and stochastic volatility.
ARCH and GARCH and their variations.
Multivariate GARCH.
Stochastic volatility.
Examples and cases 7.
Empirical literature.
Shock persistence and impulse response analysis.
Univariate persistence measures.
Multivariate persistence measures.
mpulse response analysis and variance decomposition.
Non-orthogonal cross-effect impulse response analysis.
Examples and cases.
Empirical literature.
Modelling regime shifts: Markov switching models.
Markov chains.
Estimation.
Smoothing.
Time-varying transition probabilities.
Examples and cases.
Empirical literature.
Present value models and tests for rationality and market efficiency.
The basic present value model and its time series characteristics.
The VAR representation.
The present value model in logarithms with time-varying discount rates.
The VAR representation for the present value model in the log-linear form ariance decomposition.
Examples and cases.
Empirical literature.
State space models and the Kalman filter.
State space expression.
Kalman filter algorithms.
Time-varying coefficient models.
State space models of commonly used time series processes.
Examples and cases.
Empirical literature.
Frequency domain analysis of time series.
The Fourier transform and spectra.
Multivariate spectra, phases and coherence.
Frequency domain representations of commonly used time series processes.
Frequency domain analysis of the patterns of violation of white noise conditions.
Examples and cases.
Empirical literature.
Limited dependent variables and discrete choice models.
Probit and logit formulations.
Multinomial logit models and multinomial logistic regression 2.
Ordered probit and logit.
Marginal effects.
Examples and cases.
Empirical literature.
Limited dependent variables and truncated and censored samples.
Truncated and censored data analysis.
The Tobit model.
Generalisation of the Tobit model: Heckman and Cragg.
Examples and cases.
Empirical literature.
Panel data analysis.
Structure and organisation of panel data sets.
Fixed effects vs. random effects models.
Random parameter models.
Dynamic panel data analysis.
Examples and cases.
Empirical literature.
Research tools and sources of information.
Financial economics and econometrics literature on the Internet.
Econometric software packages for financial and economic data analysis.
Learned societies and professional associations.
Organisations and institutions.