By Terrell D. (ed.), Fomby T. (ed.)
The editors are happy to supply the subsequent papers to the reader in popularity and appreciation of the contributions to our literature made by way of Robert Engle and Sir Clive Granger, winners of the 2003 Nobel Prize in Economics. the elemental subject matters of this a part of quantity 20 of Advances in Econometrics are time various betas of the capital asset pricing version, research of predictive densities of nonlinear types of inventory returns, modelling multivariate dynamic correlations, versatile seasonal time sequence versions, estimation of long-memory time sequence versions, the applying of the means of boosting in volatility forecasting, using various time scales in GARCH modelling, out-of-sample review of the 'Fed version' in inventory rate valuation, structural swap as a substitute to lengthy reminiscence, using gentle transition auto-regressions in stochastic volatility modelling, the research of the ''balanced-ness'' of regressions reading Taylor-Type principles of the Fed cash cost, a mixture-of-experts method for the estimation of stochastic volatility, a latest review of Clive's first released paper on Sunspot job, and a brand new type of types of tail-dependence in time sequence topic to jumps.
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Extra resources for Econometric Analysis of Financial and Economic Time Series Part A, Volume 20 (Advances in Econometrics)
Journal of Banking and Finance, 25, 1805–1827. Ng, A. (2000). Volatility spillover effects from Japan and the US to the Pacific-Basin. Journal of International Money and Finance, 19, 207–233. , & Schwert, G. W. (1990). Alternative models of conditional stock volatility. Journal of Econometrics, 45, 267–290. , & Susmel, R. (1998). Volatility and cross correlation across major stock markets. Journal of Empirical Finance, 5, 397–416. Tsay, R. S. (2002). ). Wiley Series in Probability and Statistics.
G. 8 This problem is potentially more severe in the full BEKK model. The asymmetric extension of the CCM (see Eq. (10)) introduced by Kroner and Ng (1998) has the variance equations of the diagonal BEKK model and the covariance equation as given in the original model. Again, this model could be used as a benchmark to analyze how variance estimates change when correlations are modelled time varying. This question is further examined in the simulation study in Section 3. Cappiello et al. (2002) develop an asymmetric version of the DCC model of Engle (2002).
The constant correlation process is an exception since the values are considerably higher compared to Table 1. This can be explained with the fact that the addition of an asymmetric term introduces a timevarying component into the correlation process. Furthermore, the performance increased for all correlation processes with a larger sample size of T ¼ 2,000. I conclude from the simulation results that correlation estimates are the closest to the true values in the FDC model for time-varying correlations and constant correlations among the time-varying correlation models.