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 6
... stochastic process – yt, say – follows a random walk is more restrictive than the requirement that yt follows a martingale. The martingale rules out any dependence of the conditional expectation of 1ytþ1 on the information available at ...
... stochastic process – yt, say – follows a random walk is more restrictive than the requirement that yt follows a martingale. The martingale rules out any dependence of the conditional expectation of 1ytþ1 on the information available at ...
Página 9
... stochastic process, specifically as being a member of the class of ARIMA models popularised by Box and Jenkins (1976). This chapter provides the basic theory of such models within the general framework of the analysis of linear stochastic ...
... stochastic process, specifically as being a member of the class of ARIMA models popularised by Box and Jenkins (1976). This chapter provides the basic theory of such models within the general framework of the analysis of linear stochastic ...
Página 10
... stochastic process. As we shall see, however, such an assumption is unlikely to be appropriate for many financial series. If normality cannot be assumed but the process is taken to be linear, in the sense that the current value of the ...
... stochastic process. As we shall see, however, such an assumption is unlikely to be appropriate for many financial series. If normality cannot be assumed but the process is taken to be linear, in the sense that the current value of the ...
Página 11
... stochastic process is said to be strictly stationary if its properties are unaffected by a change of time origin. In other words, the joint probability distribution at any set of times t1 ,t 2 ,...t m must be the same as the joint ...
... stochastic process is said to be strictly stationary if its properties are unaffected by a change of time origin. In other words, the joint probability distribution at any set of times t1 ,t 2 ,...t m must be the same as the joint ...
Página 12
... process mean 1⁄4E(xt) and variance 2x 1⁄4 0 1⁄4 VxtðÞ, the stationary stochastic process describing the evolution of xt. It therefore indicates, by measuring the extent to which one value of the process is correlated with previous ...
... process mean 1⁄4E(xt) and variance 2x 1⁄4 0 1⁄4 VxtðÞ, the stationary stochastic process describing the evolution of xt. It therefore indicates, by measuring the extent to which one value of the process is correlated with previous ...
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 |