The Econometric Modelling of Financial Time Series

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Cambridge University Press, Aug 26, 1999 - Business & Economics - 372 pages
Substantially revised and updated second edition of Terry Mills' best-selling graduate textbook The Econometric Modelling of Financial Time Series. The book provides detailed coverage of the variety of models that are currently being used in the empirical analysis of financial markets. Covering bond, equity and foreign exchange markets, it is aimed at scholars and practitioners wishing to acquire an understanding of the latest research techniques and findings, and also graduate students wishing to research into financial markets. This second edition includes a great deal of new material, and also provides a more in-depth treatment of two crucial, and related, areas: the theory of integrated processes and cointegration. The new material discusses the distributional properties of asset returns and more recent and novel techniques of analysing and interpreting vector autoregressions that contain integrated and possibly cointegrated variables. Data appendix available online at www.lboro.ac.uk/departments/ec/cup.
 

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Contents

Introduction
1
Univariate linear stochastic models basic concepts
8
22 Stochastic difference equations
11
23 ARMA processes
13
24 Linear stochastic processes
28
26 Nonstationary processes and AKIMA models
37
27 ARIMA modelling
48
28 Forecasting using ARIMA models
53
58 Distributional properties of absolute returns
200
59 Summary and further extensions
203
Regression techniques for nonintegrated financial time series
205
62 ARCHinmean regression models
218
63 Misspecification testing
221
64 Robust estimation
233
65 The multivariate linear regression model
235
66 Vector autoregressions
238

Univariate linear stochastic models further topics
61
31 Determining the order of integration of a time series
62
unobserved component models and signal extraction
99
33 Measures of persistence and trend reversion
107
34 Fractional integration and long memory processes
114
Univariate nonlinear stochastic models
122
42 Testing the random walk hypothesis
124
43 Stochastic volatility
126
44 ARCH processes
131
45 Other nonlinear univariate models
153
46 Testing for nonlinearity
171
Modelling return distributions
177
52 Two models for returns distributions
178
53 Determining the tail shape of a returns distribution
184
54 Empirical evidence on tail indices
188
55 Testing for covariance stationarity
193
56 Modelling the central part of returns distributions
196
57 Data analytic modelling of skewness and kurtosis
198
67 Variance decompositions innovation accounting and structural VARs
245
68 Vector ARMA models
248
Regression techniques for integrated financial time series
253
72 Cointegrated processes
260
73 Testing for cointegration in regression
268
74 Estimating cointegrating regressions
273
75 VARs with integrated variables
279
76 Causality testing in VECMs
297
77 Fully modified VAR estimation
299
78 Impulse response asymptotic in nonstationary VARs
302
Further topics in the analysis of integrated financial time series
306
82 Common trends and cycles
310
83 Estimating permanent and transitory components of a VECM
315
84 Present value models excess volatility and cointegration
318
85 Generalisations and extensions of cointegration and error correction models
334
Data appendix
340
References
342
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Page 361 - Testing for a moving average unit root in autoregressive integrated moving average models, Journal of the American Statistical Association, 88, 596-601.
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