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 4
... alternative trend specifications are the focus of chapter 3. We should avoid giving the impression that the only financial time series of interest are stock prices. There are financial markets other than those for stocks, most notably ...
... alternative trend specifications are the focus of chapter 3. We should avoid giving the impression that the only financial time series of interest are stock prices. There are financial markets other than those for stocks, most notably ...
Página 7
... alternative approaches to testing for the presence of a long-run relationship, and the analysis of both common cycles and trends. The techniques introduced in this chapter are illustrated with extended examples analysing the market ...
... alternative approaches to testing for the presence of a long-run relationship, and the analysis of both common cycles and trends. The techniques introduced in this chapter are illustrated with extended examples analysing the market ...
Página 33
... alternative approach to assessing model adequacy is to overfit. For example, we might consider fitting an AR(3) process or, perhaps, an ARMA (2,1) to the series. These yield the following pair of models (methods of estimating MA ...
... alternative approach to assessing model adequacy is to overfit. For example, we might consider fitting an AR(3) process or, perhaps, an ARMA (2,1) to the series. These yield the following pair of models (methods of estimating MA ...
Página 35
... alternative criteria, are there reasons for preferring one to another? If the true orders (p0 ,q0) are contained in the set p;qð Þ, p 2 "p; q 2 "q, then – for all criteria – p1 !p 0 and q1 !q 0, almost surely, as T!1. BIC is strongly ...
... alternative criteria, are there reasons for preferring one to another? If the true orders (p0 ,q0) are contained in the set p;qð Þ, p 2 "p; q 2 "q, then – for all criteria – p1 !p 0 and q1 !q 0, almost surely, as T!1. BIC is strongly ...
Página 40
... alternative way of generating a non-stationary mean level is to consider ARMA models whose autoregressive parameters do not satisfy stationarity conditions. For example, consider the AR(1) process xt 1⁄4 xtÀ1 þ at ð2:15Þ where >1. If ...
... alternative way of generating a non-stationary mean level is to consider ARMA models whose autoregressive parameters do not satisfy stationarity conditions. For example, consider the AR(1) process xt 1⁄4 xtÀ1 þ at ð2:15Þ where >1. If ...
Otras ediciones - Ver todas
The Econometric Modelling of Financial Time Series Terence C. Mills,Raphael N. Markellos Sin vista previa disponible - 2008 |
The Econometric Modelling of Financial Time Series Terence C. Mills Sin vista previa disponible - 1995 |
The Econometric Modelling of Financial Time Series Terence C. Mills,Raphael N. Markellos Sin vista previa disponible - 2008 |
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 |