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 5
... return on a stock from t to tþ1 is defined as the sum of the dividend yield and the capital gain – i.e. as where Dt is the dividend paid during period t. Let us suppose that the expected return is constant, Etrtþ1ð Þ1⁄4r, where Etð Þ is ...
... return on a stock from t to tþ1 is defined as the sum of the dividend yield and the capital gain – i.e. as where Dt is the dividend paid during period t. Let us suppose that the expected return is constant, Etrtþ1ð Þ1⁄4r, where Etð Þ is ...
Página 7
... returns. The most noticeable future of such distributions is their leptokurtic property: they have fat tails and high peakedness compared to a normal distribution. Although ARCH processes can model such features, much attention in the ...
... returns. The most noticeable future of such distributions is their leptokurtic property: they have fat tails and high peakedness compared to a normal distribution. Although ARCH processes can model such features, much attention in the ...
Página 29
... their respective standard errors, the approach to ARMA model building proposed by Box and Jenkins (1976) is essentially to Table 2.1 ACF of real S&P 500 returns and accompanying Univariate linear stochastic models: basic concepts.
... their respective standard errors, the approach to ARMA model building proposed by Box and Jenkins (1976) is essentially to Table 2.1 ACF of real S&P 500 returns and accompanying Univariate linear stochastic models: basic concepts.
Página 30
... returns and accompanying statistics Note: Figures in [..] give Pð 2k >QðkÞÞ. match the behaviour of the SACF and ... returns on the S&P 500 a fair game? An important and often analysed financial series is the real return on the annual ...
... returns and accompanying statistics Note: Figures in [..] give Pð 2k >QðkÞÞ. match the behaviour of the SACF and ... returns on the S&P 500 a fair game? An important and often analysed financial series is the real return on the annual ...
Página 31
... returns are white noise. Real returns on the S&P 500 would therefore appear to be consistent with the fair game model in which the expected return is constant, being 3.59 per cent per annum. Example 2.2 Modelling the UK interest rate ...
... returns are white noise. Real returns on the S&P 500 would therefore appear to be consistent with the fair game model in which the expected return is constant, being 3.59 per cent per annum. Example 2.2 Modelling the UK interest rate ...
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 |