Faculty Digital Archive Collection:
http://hdl.handle.net/2451/25932
2015-11-03T06:31:51ZVector Multiplicative Error Models: Representation and Inference
http://hdl.handle.net/2451/26944
Title: Vector Multiplicative Error Models: Representation and Inference
Authors: Cipollini, Fabrizio; Engle, Robert F.; Gallo, Giampiero M.
Abstract: The Multiplicative Error Model introduced by Engle (2002) for positive valued processes is specified as the product of a (conditionally autoregressive) scale factor and an innovation process with positive support. In this paper we propose a multivariate extension of such a model, by taking into consideration the possibility that the vector innovation process be contemporaneously correlated. The estimation procedure is hindered by the lack of probability density functions for multivariate positive valued random variables. We suggest the use of copula functions and of estimating equations to jointly estimate the parameters of the scale factors and of the correlations of the innovation processes. Empirical applications on volatility indicators are used to illustrate the gains over the equation by equation procedure.2006-10-01T00:00:00ZHigh Frequency Multiplicative Component GARCH
http://hdl.handle.net/2451/26942
Title: High Frequency Multiplicative Component GARCH
Authors: Engle, Robert F.; Sokalska, Magdalena E.; Chanda, Ananda
Abstract: This paper proposes a new way of modeling and forecasting intraday returns. We decompose the volatility of high frequency asset returns into components that may be easily interpreted and estimated. The conditional variance is expressed as a product of daily, diurnal and stochastic intraday volatility components. This model is applied to a comprehensive sample consisting of 10-minute returns on more than 2500 US equities. We apply a number of different specifications. Apart from building a new model, we obtain several interesting forecasting results. In particular, it turns out that forecasts obtained from the pooled cross section of companies seem to outperform the corresponding forecasts from company-by-company estimation.2005-08-02T00:00:00ZThe Underlying Dynamics of Credit Correlations
http://hdl.handle.net/2451/26940
Title: The Underlying Dynamics of Credit Correlations
Authors: Berd, Arthur; Engle, Robert; Voronov, Artem
Abstract: We propose a hybrid model of portfolio credit risk where the dynamics of the underlying latent variables is governed by a one factor GARCH process. The distinctive feature of such processes is that the long-term aggregate return distributions can substantially deviate from the asymptotic Gaussian limit for very long horizons. We introduce the notion of correlation spectrum as a convenient tool for comparing portfolio credit loss generating models and pricing synthetic CDO tranches. Analyzing alternative specifications of the underlying dynamics, we conclude that the asymmetric models with TARCH volatility specification are the preferred choice for generating significant and persistent credit correlation skews.2005-10-01T00:00:00ZThe Properties of Automatic Gets Modelling
http://hdl.handle.net/2451/26938
Title: The Properties of Automatic Gets Modelling
Authors: Hendry, David F.; Krolzig, Martin
Abstract: After reviewing the simulation performance of general-to-specific automatic regression model selection, as embodied in PcGets, we show how model selection can be non-distortionary: approximately unbiased ‘selection estimates’ are derived, with reported standard errors close to the sampling standard deviations of the estimated DGP parameters, and a near-unbiased goodness-of-fit measure. The handling of theory-based restrictions, non-stationarity, and problems posed by collinear data are considered. Finally, we consider how PcGets can handle three ‘intractable’ problems: more variables than observations in regression analysis; perfectly collinear regressors; and modelling simultaneous equations without a priori restrictions.2004-10-08T00:00:00Z