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 9
... variables Xtfg1À1 defined on an appropriate probability space. For ourpurposesitwillusuallybesufficienttorestrict theindex setT1⁄4(À1,1) of the parent stochastic process to be the same as that of the realisation, i.e. T1⁄4(1,T), and ...
... variables Xtfg1À1 defined on an appropriate probability space. For ourpurposesitwillusuallybesufficienttorestrict theindex setT1⁄4(À1,1) of the parent stochastic process to be the same as that of the realisation, i.e. T1⁄4(1,T), and ...
Página 12
... variables. By 'purely non-deterministic' we 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 ...
... variables. By 'purely non-deterministic' we 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 ...
Página 23
... variables is often due to both variables being correlated with a third. In the present context, a large portion of the correlation between xt and xt-k may be due to the correlation this pair have with the intervening lags xt-\, xt_2 ...
... variables is often due to both variables being correlated with a third. In the present context, a large portion of the correlation between xt and xt-k may be due to the correlation this pair have with the intervening lags xt-\, xt_2 ...
Página 31
... 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 SPACF of the UK spread 1955. Univariate linear stochastic models: basic concepts 31.
... 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 SPACF of the UK spread 1955. Univariate linear stochastic models: basic concepts 31.
Página 38
... variable g(xt) should be constant. Expanding g(xt) as a first- order Taylor series around fit yields g(*t) = g(l*t) + (xt ~ Vt)g'{Vt) where g'{^t) is the first derivative of g(xt) evaluated at \it. The variance of giUt) can then be ...
... variable g(xt) should be constant. Expanding g(xt) as a first- order Taylor series around fit yields g(*t) = g(l*t) + (xt ~ Vt)g'{Vt) where g'{^t) is the first derivative of g(xt) evaluated at \it. The variance of giUt) can then be ...
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 |