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 5
... obtained unpredictability as a direct implication of market efficiency, or, more broadly speaking, of the condition whereby market prices fully, correctly and instantaneously reflect all the available information. An evolving discussion ...
... obtained unpredictability as a direct implication of market efficiency, or, more broadly speaking, of the condition whereby market prices fully, correctly and instantaneously reflect all the available information. An evolving discussion ...
Página 8
... obtained by recursively solving (1.3) forwards and assuming that 1 þ r ð ÞÀnEtPtþnð Þconverges to zero as n ! 1. Present value models of the type (1.4) are analysed comprehensively in chapter 10, with the theme of whether stock markets ...
... obtained by recursively solving (1.3) forwards and assuming that 1 þ r ð ÞÀnEtPtþnð Þconverges to zero as n ! 1. Present value models of the type (1.4) are analysed comprehensively in chapter 10, with the theme of whether stock markets ...
Página 16
... , x0 = 0 Figure 2.1 (continued) 2.3.2 Moving average processes Now consider the model obtained by. 10 20 30 40 50 60 70 80 90 100 Figure 2.4 Simulations of various AR(2) processes. 16 The Econometric Modelling of Financial Time Series.
... , x0 = 0 Figure 2.1 (continued) 2.3.2 Moving average processes Now consider the model obtained by. 10 20 30 40 50 60 70 80 90 100 Figure 2.4 Simulations of various AR(2) processes. 16 The Econometric Modelling of Financial Time Series.
Página 17
... obtained by choosing^j = — 6 and^= 0, j> 2, in (2.1): xt = at — 6at-\ or Xt = (l - 0B)at (2.5) This is known as the ... obtain a converging autoregressive representation, however, the restriction \6\<l must be imposed. This restriction ...
... obtained by choosing^j = — 6 and^= 0, j> 2, in (2.1): xt = at — 6at-\ or Xt = (l - 0B)at (2.5) This is known as the ... obtain a converging autoregressive representation, however, the restriction \6\<l must be imposed. This restriction ...
Página 18
... obtained by equating coefficients in (B)(B)1⁄41 (see Mills, 1990, chap. 5, for examples of how to do this). The stationarity conditions required for convergence of the -weights are that the roots of the characteristic equation Bð Þ1⁄4 1 ...
... obtained by equating coefficients in (B)(B)1⁄41 (see Mills, 1990, chap. 5, for examples of how to do this). The stationarity conditions required for convergence of the -weights are that the roots of the characteristic equation Bð Þ1⁄4 1 ...
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 |