The Econometric Modelling of Financial Time SeriesCambridge University Press, 2008 M03 20 - 468 páginas 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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Página 4
... Chapter 2 thus develops the theory of such models within the general context of (univariate) linear stochastic processes. An important aspect of specifying ARIMA models is to be able to determine correctly the order of integration of ...
... Chapter 2 thus develops the theory of such models within the general context of (univariate) linear stochastic processes. An important aspect of specifying ARIMA models is to be able to determine correctly the order of integration of ...
Página 6
... chapter 5, and lead naturally on to non-linear stochastic processes that are capable of modelling higher conditional moments, such as the autoregressive conditionally heteroskedastic (ARCH) model introduced by Engle (1982) and ...
... chapter 5, and lead naturally on to non-linear stochastic processes that are capable of modelling higher conditional moments, such as the autoregressive conditionally heteroskedastic (ARCH) model introduced by Engle (1982) and ...
Página 7
... chapters focus on multivariate techniques of time series analysis, including regression methods. Chapter 8 concentrates on analysing the relationships between a set of stationary – or, more precisely, non-integrated – financial time ...
... chapters focus on multivariate techniques of time series analysis, including regression methods. Chapter 8 concentrates on analysing the relationships between a set of stationary – or, more precisely, non-integrated – financial time ...
Página 8
... chapter also discusses recent research on nonlinear generalisations of cointegration and how structural breaks may be dealt with in cointegrating relationships. Having emphasised earlier in this chapter that the book is exclusively ...
... chapter also discusses recent research on nonlinear generalisations of cointegration and how structural breaks may be dealt with in cointegrating relationships. Having emphasised earlier in this chapter that the book is exclusively ...
Página 9
... chapter provides the basic theory of such models within the general framework of the analysis of linear stochastic processes. As already stated in chapter 1, our treatment is purposely non-rigorous. For detailed theoretical treatments ...
... chapter provides the basic theory of such models within the general framework of the analysis of linear stochastic processes. As already stated in chapter 1, our treatment is purposely non-rigorous. For detailed theoretical treatments ...
Otras ediciones - Ver todas
The Econometric Modelling of Financial Time Series Terence C. Mills,Raphael N. Markellos Sin vista previa disponible - 2008 |
The Econometric Modelling of Financial Time Series Terence C. Mills Sin vista previa disponible - 1995 |
The Econometric Modelling of Financial Time Series Terence C. Mills,Raphael N. Markellos Sin vista previa disponible - 2008 |
Términos y frases comunes
À Á allow alternative analysis approach approximation ARCH assumed assumption asymptotic autocorrelation average behaviour bilinear cent changes chapter cointegration component computed conditional consider consistent constant contain converges correction correlation critical values ð Þ defined dependence developed discussed distribution effect empirical equation error estimated evidence example expected extension Figure financial first forecast function future GARCH given Granger hypothesis implies important independent integrated interest known limiting linear mean noise non-linear normal Note null observations obtained parameters period positive possible present procedure properties proposed provides random walk ratio regression rejected relationship requires residuals respectively response restrictions returns sample shown shows significant simple squared standard stationary statistic stochastic suggest tail trend unit root values variables variance vector volatility written yields zero
Pasajes populares
Página 12 - A stochastic process is said to be strictly stationary if its properties are unaffected by a change of time origin, that is, if the joint probability distribution associated with m observations...
Referencias a este libro
Probability Theory and Statistical Inference: Econometric Modeling with ... Aris Spanos Vista previa limitada - 1999 |
Extreme Financial Risks: From Dependence to Risk Management Yannick Malevergne,Didier Sornette Sin vista previa disponible - 2006 |