4,923 research outputs found
"Ranking Multivariate GARCH Models by Problem Dimension"
No full textIn the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. The two most widely known and used are the Scalar BEKK model of Engle and Kroner (1995) and Ding and Engle (2001), and the DCC model of Engle (2002). Some recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. In this paper, we provide an empirical comparison of a set of MGARCH models, namely BEKK, DCC, Corrected DCC (cDCC) of Aeilli (2008), CCC of Bollerslev (1990), Exponentially Weighted Moving Average, and covariance shrinking of Ledoit and Wolf (2004), using the historical data of 89 US equities. Our methods follow some of the approach described in Patton and Sheppard (2009), and contribute to the literature in several directions. First, we consider a wide range of models, including the recent cDCC model and covariance shrinking. Second, we use a range of tests and approaches for direct and indirect model comparison, including the Weighted Likelihood Ratio test of Amisano and Giacomini (2007). Third, we examine how the model rankings are influenced by the cross-sectional dimension of the problem.
Ranking Multivariate GARCH Models by Problem Dimension
No full textIn the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. The two most widely known and used are the Scalar BEKK model of Engle and Kroner (1995) and Ding and Engle (2001), and the DCC model of Engle (2002). Some recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. In this paper, we provide an empirical comparison of a set of MGARCH models, namely BEKK, DCC, Corrected DCC (cDCC) of Aeilli (2008), CCC of Bollerslev (1990), Exponentially Weighted Moving Average, and covariance shrinking of Ledoit and Wolf (2004), using the historical data of 89 US equities. Our methods follow some of the approach described in Patton and Sheppard (2009), and contribute to the literature in several directions. First, we consider a wide range of models, including the recent cDCC model and covariance shrinking. Second, we use a range of tests and approaches for direct and indirect model comparison, including the Weighted Likelihood Ratio test of Amisano and Giacomini (2007). Third, we examine how the model rankings are influenced by the cross-sectional dimension of the problem.
Paradise Lost and Found? The Econometric Contributions of Clive W.J. Granger and Robert F. Engle
No full textThis paper provides a non-technical and illustrated introduction to the econometric contributions of the 2003 Nobel Prize winners, Robert Engle and Clive Granger, with special emphasis on their implications for heterodox economists.ARCH, GARCH, cointegration, error correction model, general-to-speci...c
Ranking Multivariate GARCH Models by Problem Dimension
No full textIn the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. The two most widely known and used are the Scalar BEKK model of Engle and Kroner (1995) and Ding and Engle (2001), and the DCC model of Engle (2002). Some recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. In this paper, we provide an empirical comparison of a set of MGARCH models, namely BEKK, DCC, Corrected DCC (cDCC) of Aeilli (2008), CCC of Bollerslev (1990), Exponentially Weighted Moving Average, and covariance shrinking of Ledoit and Wolf (2004), using the historical data of 89 US equities. Our methods follow some of the approach described in Patton and Sheppard (2009), and contribute to the literature in several directions. First, we consider a wide range of models, including the recent cDCC model and covariance shrinking. Second, we use a range of tests and approaches for direct and indirect model comparison, including the Weighted Likelihood Ratio test of Amisano and Giacomini (2007). Third, we examine how the model rankings are influenced by the cross-sectional dimension of the problem.Covariance forecasting; model confidence set; model ranking; MGARCH; model comparison
Clinical and genetic data of two (classic) CFEOM1 families and one atypical CFEOM family.
No full textClinical and genetic data of two (classic) CFEOM1 families and one atypical CFEOM family.
No full textClinical and genetic data of two (classic) CFEOM1 families and one atypical CFEOM family.
No full textTutorial para Pruebas de Cointegración de Engle y Granger en EasyReg
No full textEste documento, de carácter pedagógico, presenta la prueba de cointegración de Engle y Granger y muestra paso a paso como efectuar dicha prueba empleando el paquete estadístico EasyReg International. Este documento está diseñado para estudiantes de un curso introductorio al análisis de series de tiempo. Por su simplicidad, puede ser útil para economistas que estén trabajando con series de tiempo y quieran empezar el estudio del concepto de cointegración. Se supone un conocimiento previo de los conceptos básicos de series de tiempo.EasyReg, Pruebas de cointegración, Prueba de de Engle y Granger.
Cointegrazione dinamica tra serie storiche economiche
No full textIl presente lavoro si propone di analizzare la relazione di cointegrazione tra serie storiche integrate nel caso in cui sia rilassata l’ipotesi, implicita nell’impostazione tradizionale, che il coefficiente di cointegrazione sia costante. Dati due processi cointegrati, la variabilit`a del coefficiente di cointegrazione permette di considerare comportamenti non lineari nella risposta di un processo a variazioni dell’altro, causate da shocks destabilizzanti la relazione di equilibrio di lungo periodo. In tale contesto `e di primaria importanza la ricostruzione della dinamica del coefficiente di cointegrazione che in questo lavoro viene condotta utilizzando una tecnica grafica nota come recurrence plots in grado di evidenziare la presenza di forze-guida deboli all’interno di sistemi dinamici, deterministici o stocastici. Tale impostazione che prevede come caso particolare la cointegrazione lineare (Engle e Granger, 1987) viene applicata a serie simulate
- …
