Buch, Englisch, 406 Seiten, HC gerader Rücken kaschiert, Format (B × H): 161 mm x 240 mm, Gewicht: 777 g
Reihe: Advances in Econometrics
Buch, Englisch, 406 Seiten, HC gerader Rücken kaschiert, Format (B × H): 161 mm x 240 mm, Gewicht: 777 g
Reihe: Advances in Econometrics
ISBN: 978-0-7623-1274-0
Verlag: Jai Press Inc.
- This Series: Aids in the diffusion of new econometric techniques
- Emphasis is placed on expositional clarity and ease of assimilation for readers who are unfamiliar with a given topic of a volume
- Illustrates new concepts
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Introduction (D. Terrell, T. Fomby). Remarks (R. Engle, C. Granger)
Part I: Multivariate volatility models.
A flexible dynamic correlation model (D. Baur). A multivariate skew-garch model (G. De Luca, M. Genton, N. Loperfido). Semi-parametric modelling of correlation dynamics (C. Hafner, Dick Van Dijk, P. H. Franses). A multivariate heavy-tailed distribution for arch/garch residuals (D. Politis). A portmanteau test for multivariate garch when the conditional mean is ECM: Theory and empirical applications (C. Y. sin).
Part II: highfrequency volatility models.
Sampling frequency and window length trade-offs in data-driven volatility estimation: appraising the accuracy of asymptotic approximations (E. Andreou, E. Ghysels). Model-based measurement of actual volatility in highfrequency data (B. Jungacker, s. J. Koopman). Noise reduced realized volatility: a kalman filter approach (J. Owens, D. Steigerwald).
Part III: Univariate volatility models.
Modeling the asymmetry of stock movements using price ranges (R. Chou). On a simple two-stage closed-form estimator for a stochastic volatility in a general linear regression (J.-M. Dufour, P. Valéry). The students t dynamic linear regression: Re-examining volatility modelling (M. Heracleous, a. spanos). arch models for multi-period forecast uncertainty a reality check using a panel of density forecasts (K. Lahiri, F. Liu). Necessary and sufficient restrictions for existence of a unique fourth moment of a univariate garch (P,Q) Process (P. Zadrozny).