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
... stochastic processes. An important aspect of specifying ARIMA models is to be able to determine correctly the order of integration of the series being analysed and, associated with this, the appropriate way of modelling trends and ...
... stochastic processes. An important aspect of specifying ARIMA models is to be able to determine correctly the order of integration of the series being analysed and, associated with this, the appropriate way of modelling trends and ...
Página 6
... stochastic processes that are capable of modelling higher conditional moments, such as the autoregressive conditionally heteroskedastic (ARCH) model introduced by Engle (1982) and stochastic variance models. Related to these models is ...
... stochastic processes that are capable of modelling higher conditional moments, such as the autoregressive conditionally heteroskedastic (ARCH) model introduced by Engle (1982) and stochastic variance models. Related to these models is ...
Página 8
... Non-standard' computations were made using algorithms written by the authors in GAUSS and MatLab. ð1:4Þ 2 Univariate linear stochastic models: basic concepts Chapter 1 has. 8 The Econometric Modelling of Financial Time Series.
... Non-standard' computations were made using algorithms written by the authors in GAUSS and MatLab. ð1:4Þ 2 Univariate linear stochastic models: basic concepts Chapter 1 has. 8 The Econometric Modelling of Financial Time Series.
Página 9
... stochastic process, specifically as being a member of the class of ARIMA models popularised by Box and Jenkins (1976). This chapter provides the basic theory of such models within the general framework of the analysis of linear stochastic ...
... stochastic process, specifically as being a member of the class of ARIMA models popularised by Box and Jenkins (1976). This chapter provides the basic theory of such models within the general framework of the analysis of linear stochastic ...
Página 10
... stochastic process. As we shall see, however, such an assumption is unlikely to be appropriate for many financial series. If normality cannot be assumed but the process is taken to be linear, in the sense that the current value of the ...
... stochastic process. As we shall see, however, such an assumption is unlikely to be appropriate for many financial series. If normality cannot be assumed but the process is taken to be linear, in the sense that the current value of the ...
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 |