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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Resultados 1-5 de 52
Página 4
... autocorrelation properties of price changes: see, for example, Fama (1965). A more general perspective is to view (1.1) as a particular model within the class of autoregressive integrated moving average (ARIMA) models popularised by Box ...
... autocorrelation properties of price changes: see, for example, Fama (1965). A more general perspective is to view (1.1) as a particular model within the class of autoregressive integrated moving average (ARIMA) models popularised by Box ...
Página 6
... autocorrelated. Such a specification would be consistent with a martingale, but not with the more restrictive random walk. Martingale processes are discussed in chapter 5, and lead naturally on to non-linear stochastic processes that ...
... autocorrelated. Such a specification would be consistent with a martingale, but not with the more restrictive random walk. Martingale processes are discussed in chapter 5, and lead naturally on to non-linear stochastic processes that ...
Página 11
... autocorrelations as k 1⁄4 Covxt;xtÀkð Þ1⁄4E xt À ð ÞxtÀk À ð Þ 1⁄2 and Covxt;xtÀkðÞ 1⁄4 k k 1⁄4 1 Vxtð ÞÁV xtÀkðÞ 1⁄2 2 0 respectively, both of which depend only on the lag k. Since these conditions apply just to the first- and second ...
... autocorrelations as k 1⁄4 Covxt;xtÀkð Þ1⁄4E xt À ð ÞxtÀk À ð Þ 1⁄2 and Covxt;xtÀkðÞ 1⁄4 k k 1⁄4 1 Vxtð ÞÁV xtÀkðÞ 1⁄2 2 0 respectively, both of which depend only on the lag k. Since these conditions apply just to the first- and second ...
Página 12
Terence C. Mills, Raphael N. Markellos. The autocorrelations considered as a function of k are referred to as the autocorrelation function (ACF). Note that, since k 1⁄4 Covxt;xtÀkð Þ1⁄4CovxtÀk;xtð Þ1⁄4Covxt;xtþkð Þ1⁄4 Àk it follows that ...
Terence C. Mills, Raphael N. Markellos. The autocorrelations considered as a function of k are referred to as the autocorrelation function (ACF). Note that, since k 1⁄4 Covxt;xtÀkð Þ1⁄4CovxtÀk;xtð Þ1⁄4Covxt;xtþkð Þ1⁄4 Àk it follows that ...
Página 13
... autocorrelation in xt. From this equation it follows that E(xt) = \x by using the result that E(at_ \at_-) = 0 for i^j. Now 7* = E(xt - n)(xt-k - n) = E(at + Viflt-i H r- ^flt-it + • • -)(flt-*: + ^iat-k-i H ) = (T2(l • + V'lV'lt+l + ^2 ...
... autocorrelation in xt. From this equation it follows that E(xt) = \x by using the result that E(at_ \at_-) = 0 for i^j. Now 7* = E(xt - n)(xt-k - n) = E(at + Viflt-i H r- ^flt-it + • • -)(flt-*: + ^iat-k-i H ) = (T2(l • + V'lV'lt+l + ^2 ...
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 |