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 30
... cent. This is confirmed by the SACF, and a comparison of each of the r k with their corresponding standard errors, computed using equation (2.10), shows that none is individually significantly different from zero, thus suggesting that ...
... cent. This is confirmed by the SACF, and a comparison of each of the r k with their corresponding standard errors, computed using equation (2.10), shows that none is individually significantly different from zero, thus suggesting that ...
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... cent per annum. Example 2.2 Modelling the UK interest rate spread As we shall see in chapter 10, the 'spread', the difference between long-term and short-term interest rates, is an important variable in testing the Table 2.2 SACF and ...
... cent per annum. Example 2.2 Modelling the UK interest rate spread As we shall see in chapter 10, the 'spread', the difference between long-term and short-term interest rates, is an important variable in testing the Table 2.2 SACF and ...
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... mean return implied by the ARMA(2,2) model is 1.21 per cent, the same as that obtained directly from the MA(2) Table 2.4 Model selection criteria for nominal returns model, while 35 Univariate linear stochastic models: basic concepts.
... mean return implied by the ARMA(2,2) model is 1.21 per cent, the same as that obtained directly from the MA(2) Table 2.4 Model selection criteria for nominal returns model, while 35 Univariate linear stochastic models: basic concepts.
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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
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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 |