The Econometric Modelling of Financial Time Series
Cambridge University Press, Mar 20, 2008 - Business & Economics - 468 pages
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 and first published in 2008, contains a wealth of new 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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22 Stochastic difference equations
24 Linear stochastic processes
27 ARIMA modelling
Table 26 SACF and SPACF of the first difference of
28 Seasonal ARIMA modelling
73 Determining the tail shape of a returns distribution
75 Testing for covariance stationarity
76 Modelling the central part of returns distributions
77 Dataanalytic modelling of skewness and kurtosis
79 Summary and further extensions
82 ARCHinmean regression models
83 Misspeciﬁcation testing
33 Trend stationarity versus difference stationarity
34 Other approaches to testing for unit roots
35 Testing for more than one unit root
36 Segmented trends structural breaks and smooth transitions
37 Stochastic unit root processes
penalises acceleration in the trend so the minimisation problem becomes
53 Measures of volatility
54 Stochastic volatility
mean zero variance one and nuisance parameters c Precise details
56 Some models related to ARCH
57 The forecasting performance of alternative volatility models
61 Bilinear and related models
63 Nonparametric and neural network models
84 Robust estimation
85 The multivariate linear regression model
86 Vector autoregressions
where vj yjt vjt and where Ev is correspondingly
87 Variance decompositions innovation accounting and
92 Cointegrated processes
Now if 7r 0 then since xt 71
93 Testing for cointegration in regression
94 Estimating cointegrating regressions
96 Causality testing in VECMs
97 Impulse response asymptotics in nonstationary VARs
98 Testing for a single longrun relationship
99 Common trends and cycles
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