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 2
... trend decompositionbyWarrenPersons,wasthatmarket pricevariationsconsistedofthree primary movements: daily, medium-term and long-term (see Samuelson, 1987). Although criticism of Dow theory and technical analysis has been a favourite ...
... trend decompositionbyWarrenPersons,wasthatmarket pricevariationsconsistedofthree primary movements: daily, medium-term and long-term (see Samuelson, 1987). Although criticism of Dow theory and technical analysis has been a favourite ...
Página 4
... trends and structural breaks. To do this formally requires the application of unit root tests and a vast range of related procedures. Tests for unit roots and alternative trend specifications are the focus of chapter 3. We should avoid ...
... trends and structural breaks. To do this formally requires the application of unit root tests and a vast range of related procedures. Tests for unit roots and alternative trend specifications are the focus of chapter 3. We should avoid ...
Página 7
... trends. The techniques introduced in this chapter are illustrated with extended examples analysing the market model and the interactions of the UK financial markets. Finally, chapter 10 considers modelling issues explicit to finance ...
... trends. The techniques introduced in this chapter are illustrated with extended examples analysing the market model and the interactions of the UK financial markets. Finally, chapter 10 considers modelling issues explicit to finance ...
Página 15
... trends. With <0 (d), however, adjacent values have a negative correlation and the generated series displays violent, rapid oscillations. –1.0 1.0 –0.5 0.0 0.5 1 2 3 4 5 6 7 8 9 10 11 12 k k (a) f = 0.5 Figure 2.1 ACFs and simulations of ...
... trends. With <0 (d), however, adjacent values have a negative correlation and the generated series displays violent, rapid oscillations. –1.0 1.0 –0.5 0.0 0.5 1 2 3 4 5 6 7 8 9 10 11 12 k k (a) f = 0.5 Figure 2.1 ACFs and simulations of ...
Página 39
... trend in the mean is said tobedeterministic. Trends of this typecan be removed by a simple transformation. Consider the linear trend obtained by setting d1⁄41, where, for simplicity, the error component is assumed to be a white-noise ...
... trend in the mean is said tobedeterministic. Trends of this typecan be removed by a simple transformation. Consider the linear trend obtained by setting d1⁄41, where, for simplicity, the error component is assumed to be a white-noise ...
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 |