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. |
Dentro del libro
Resultados 1-5 de 55
Página 12
... 4E a2t À Á 1⁄4 2<1 and Covat ;atÀk ð Þ1⁄4E atatÀkð Þ1⁄40; for all k 1⁄4 0 We will refer to such a sequence as a white-noise 12 The Econometric Modelling of Financial Time Series 2.2 Stochastic difference equations ...
... 4E a2t À Á 1⁄4 2<1 and Covat ;atÀk ð Þ1⁄4E atatÀkð Þ1⁄40; for all k 1⁄4 0 We will refer to such a sequence as a white-noise 12 The Econometric Modelling of Financial Time Series 2.2 Stochastic difference equations ...
Página 13
... white-noise process, often denoting it as at ~ WN(0, a2). The coefficients (possibly infinite in number) in the linear filter are known as ip-weights. We can easily show that the model (2.1) leads to autocorrelation in xt. From this ...
... white-noise process, often denoting it as at ~ WN(0, a2). The coefficients (possibly infinite in number) in the linear filter are known as ip-weights. We can easily show that the model (2.1) leads to autocorrelation in xt. From this ...
Página 15
... white noise, any term in atatÀkÀi has zero expectation if kþi>0. Thus, (2.4) simplifies to k 1⁄4 kÀ1; for all k>0 and, consequently, k 1⁄4 k0. An AR(1) process therefore has an ACF given by k 1⁄4 k. Thus, if >0, the ACF decays ...
... white noise, any term in atatÀkÀi has zero expectation if kþi>0. Thus, (2.4) simplifies to k 1⁄4 kÀ1; for all k>0 and, consequently, k 1⁄4 k0. An AR(1) process therefore has an ACF given by k 1⁄4 k. Thus, if >0, the ACF decays ...
Página 28
... white noise is said to be a linear process. It is possible, however, for a linear filter of a white noise process to result in a non-linear stationary process. The distinctions between white and strict white noise and between linear and ...
... white noise is said to be a linear process. It is possible, however, for a linear filter of a white noise process to result in a non-linear stationary process. The distinctions between white and strict white noise and between linear and ...
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 |