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 10
... finite stretches of the realisation approach their population counterparts as the length of the realisation becomes infinite. For more on ergodicity, see, for example, Granger and Newbold (1986, chap. 1) or Hamilton (1994, chap. 3.2) ...
... finite stretches of the realisation approach their population counterparts as the length of the realisation becomes infinite. For more on ergodicity, see, for example, Granger and Newbold (1986, chap. 1) or Hamilton (1994, chap. 3.2) ...
Página 11
... finite second moments) thus implies weak stationarity, the converse does not hold, for it is possible for a process to be weakly stationary but not strictly stationary; this would be the case if higher moments, such as Ex3t ÀÁ , were ...
... finite second moments) thus implies weak stationarity, the converse does not hold, for it is possible for a process to be weakly stationary but not strictly stationary; this would be the case if higher moments, such as Ex3t ÀÁ , were ...
Página 13
... finite. Wold's theorem is fundamental, in that it allows us to represent any arbitrary linear process as a stochastic difference equation with infinite lags. In practical terms this representation may not seem very useful, since it ...
... finite. Wold's theorem is fundamental, in that it allows us to represent any arbitrary linear process as a stochastic difference equation with infinite lags. In practical terms this representation may not seem very useful, since it ...
Página 17
... finite number of ^-weights, all MA models are stationary. In order to obtain a converging autoregressive representation, however, the restriction \6\<l must be imposed. This restriction is known as the invertibility condition, and ...
... finite number of ^-weights, all MA models are stationary. In order to obtain a converging autoregressive representation, however, the restriction \6\<l must be imposed. This restriction is known as the invertibility condition, and ...
Página 28
... finite variance, and hence the sequence has been termed white noise – i.e. at $WN(0, 2). If these innovations are also independent (in which case we denote them as being iid), then the sequence is termed strict white noise, denoted a ...
... finite variance, and hence the sequence has been termed white noise – i.e. at $WN(0, 2). If these innovations are also independent (in which case we denote them as being iid), then the sequence is termed strict white noise, denoted a ...
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 |