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 3
... mean and whose values are independent of each other. The price change, 1Pt 1⁄4P t ÀP tÀ1, is thus simply atand hence is independent of past price changes. Note that, by successive backward substitution in (1.1), we can write the current ...
... mean and whose values are independent of each other. The price change, 1Pt 1⁄4P t ÀP tÀ1, is thus simply atand hence is independent of past price changes. Note that, by successive backward substitution in (1.1), we can write the current ...
Página 7
... mean and multivariate ARCH models, vector autoregressions, Granger causality, variance decompositions and impulse response analysis. These topics are illustrated with a variety of examples drawnfromthefinanceliterature ...
... mean and multivariate ARCH models, vector autoregressions, Granger causality, variance decompositions and impulse response analysis. These topics are illustrated with a variety of examples drawnfromthefinanceliterature ...
Página 11
... mean and variance of xt must be constant – i.e. Ex1ð Þ1⁄4E x2ð Þ1⁄4ÁÁÁ1⁄4E xTð Þ1⁄4E xtð Þ1⁄4 and Vx1ð Þ1⁄4V x2ð Þ1⁄4ÁÁÁ1⁄4V xTð Þ1⁄4V xtð Þ1⁄4 2 x If m1⁄42, strict stationarity implies that all bivariate distributions do not depend on ...
... mean and variance of xt must be constant – i.e. Ex1ð Þ1⁄4E x2ð Þ1⁄4ÁÁÁ1⁄4E xTð Þ1⁄4E xtð Þ1⁄4 and Vx1ð Þ1⁄4V x2ð Þ1⁄4ÁÁÁ1⁄4V xTð Þ1⁄4V xtð Þ1⁄4 2 x If m1⁄42, strict stationarity implies that all bivariate distributions do not depend on ...
Página 12
... mean that any linearly deterministic components have been subtracted from (xt À ). Such a component is one that can be perfectly predicted from past values of itself, and examples commonly found are a (constant) mean, as is implied by ...
... mean that any linearly deterministic components have been subtracted from (xt À ). Such a component is one that can be perfectly predicted from past values of itself, and examples commonly found are a (constant) mean, as is implied by ...
Página 28
... mean of the process, , is zero. Non-zero means are easily accommodated by replacing xt with xt À in (2.10), so that in the general case of an ARMA(p,q) process we have BðÞxt À ð Þ1⁄4 BðÞat Noting that BðÞ 1⁄4 1 À 1 À ... À p À Á 1⁄4 1ðÞ ...
... mean of the process, , is zero. Non-zero means are easily accommodated by replacing xt with xt À in (2.10), so that in the general case of an ARMA(p,q) process we have BðÞxt À ð Þ1⁄4 BðÞat Noting that BðÞ 1⁄4 1 À 1 À ... À p À Á 1⁄4 1ðÞ ...
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 |