The Econometric Modelling of Financial Time Series

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Cambridge University Press, Mar 20, 2008 - Business & Economics - 468 pages
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Terence Mills' best-selling graduate textbook provides detailed coverage of research techniques and findings relating to the empirical analysis of financial markets. In its previous editions it has become required reading for many graduate courses on the econometrics of financial modelling. This third edition, co-authored with Raphael Markellos, contains a wealth of material reflecting the developments of the last decade. Particular attention is paid to the wide range of nonlinear models that are used to analyse financial data observed at high frequencies and to the long memory characteristics found in financial time series. The central material on unit root processes and the modelling of trends and structural breaks has been substantially expanded into a chapter of its own. There is also an extended discussion of the treatment of volatility, accompanied by a new chapter on nonlinearity and its testing.
 

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Contents

22 Stochastic difference equations
12
20
19
24 Linear stochastic processes
28
X
42
27 ARIMA modelling
48
Table 26 SACF and SPACF of the first difference of
52
28 Seasonal ARIMA modelling
53
3
65
73 Determining the tail shape of a returns distribution
254
75 Testing for covariance stationarity
261
76 Modelling the central part of returns distributions
264
77 Dataanalytic modelling of skewness and kurtosis
266
79 Summary and further extensions
271
8
274
82 ARCHinmean regression models
287
83 Misspecification testing
293

33 Trend stationarity versus difference stationarity
85
34 Other approaches to testing for unit roots
89
35 Testing for more than one unit root
96
36 Segmented trends structural breaks and smooth transitions
98
37 Stochastic unit root processes
105
4
111
penalises acceleration in the trend so the minimisation problem becomes
121
Ek i
126
5
151
53 Measures of volatility
157
54 Stochastic volatility
166
mean zero variance one and nuisance parameters c Precise details
187
56 Some models related to ARCH
199
57 The forecasting performance of alternative volatility models
204
6
206
61 Bilinear and related models
207
Markov chains
216
63 Nonparametric and neural network models
223
7
247
Chow
301
029
302
84 Robust estimation
304
85 The multivariate linear regression model
307
86 Vector autoregressions
309
where vj yjt vjt and where Ev is correspondingly
313
87 Variance decompositions innovation accounting and
316
9
329
25
336
92 Cointegrated processes
338
Now if 7r 0 then since xt 71
340
12
342
93 Testing for cointegration in regression
346
94 Estimating cointegrating regressions
352
96 Causality testing in VECMs
373
97 Impulse response asymptotics in nonstationary VARs
375
98 Testing for a single longrun relationship
377
99 Common trends and cycles
383
10
388

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About the author (2008)

Terence C. Mills is Professor of Applied Statistics and Econometrics, Loughborough University. He is the co-editor of the Palgrave Handbook of Econometrics and has over 170 publications.

Raphael N. Markellos is Professor of Quantitative Finance at Athens University of Economics and Business, and Visiting Research Fellow at the Centre for International Financial and Economic Research (CIFER), Loughborough University.

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