Econometric Analysis of Financial and Economic Time Series Part B / Edition 1

by Thomas B. B Fomby, Dek Terrell

Overview

The editors are pleased to offer the following papers to the reader in recognition and appreciation of the contributions to our literature made by Robert Engle and Sir Clive Granger, winners of the 2003 Nobel Prize in Economics. The basic themes of this part of Volume 20 of "Advances in Econometrics" are time varying betas of the capital asset pricing model, analysis of predictive densities of nonlinear models of stock returns, modelling multivariate dynamic correlations, flexible seasonal time series models, estimation of long-memory time series models, the application of the technique of boosting in volatility forecasting, the use of different time scales in GARCH modelling, out-of-sample evaluation of the Fed Model in stock price valuation, structural change as an alternative to long memory, the use of smooth transition auto-regressions in stochastic volatility modelling, the analysis of the balanced-ness of regressions analyzing Taylor-Type rules of the Fed Funds rate, a mixture-of-experts approach for the estimation of stochastic volatility, a modern assessment of Clives first published paper on Sunspot activity, and a new class of models of tail-dependence in time series subject to jumps. 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. It illustrates new concepts.

Product Details

ISBN-13:	9780762312733
Publisher:	Emerald Publishing Ltd
Publication date:	11/23/2006
Series:	Advances in Econometrics Series
Pages:	380
Product dimensions:	6.14(w) x 9.21(h) x 0.88(d)

Table of Contents

Introduction (T. B. Fomby, D. Terrell). Remarks (R. Engle, C. Granger). Realized beta: persistence and predictability (T. G. and ersen, T.Bollerslev, F. X. Diebold, J. Wu). Asymmetric predictive abilities of nonlinearmodels for stock returns: evidence from density forecast comparison (Y. Bao, T.-H. Lee). Flexible seasonal time seriesmodels (Z. Cai, R. Chen). Estimation of long-memory time seriesmodels : a survey of different likelihood-based methods (N. Hang Chan, W. Palma). Boosting-based frameworks infinancial modeling : application to symbolic volatility forecasting (V. V. Gavrishchaka). Overlaying time scales infinancial volatility data (E. Hillebrand). Evaluating the fed model of stock price valuation: an out-of-sample forecasting perspective (D. W. Jansen, Z. Wang). Structural change as an alternative to long memory in financial time series (T. Leung Lai, H. Xing). Time series mean level and stochastic volatility modeling by smooth transition autoregressions: a bayesian approach (H. Freitas Lopes, E. Salazar). Estimating Taylor-type rules: an unbalanced regression? (P. L. Siklos, Mark E. Wohar). Bayesian inference on mixture-of-experts for estimation of stochastic volatility (A. Villagran, G. Huerta). A modern time series assessment of A statistical model for sunspot activity by C.W.J. Granger (1957) (G.Yoon). Comment on Yoon Paper ( C.W.J. Granger, KB). A new class of tail-dependent time seriesmodels and its applications in financial time series (Z. Zhang).