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Improving the forecasting of dynamic conditional correlation: a volatility dependent approach

Otranto, Edoardo (2009) Improving the forecasting of dynamic conditional correlation: a volatility dependent approach. Cagliari, CRENoS-CUEC. p. 18 (Working Papers CRENoS, 2009, 17). ISBN 978-88-84-67-541-5. Working Paper.

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Abstract

Forecasting volatility in a multivariate framework has received many contributions in the recent literature, but problems in estimation are still frequently encountered when dealing with a large set of time series. The Dynamic Conditional Correlation (DCC) modeling is probably the most used approach; it has the advantage of separating the estimation of the volatility of each time series (with great flexibility, using single univariate models) and the correlation part (with the strong constraint imposing the same dynamics to all the correlations). We propose a modification to the DCC model, providing different dynamics for each correlation, simply hypothesizing a dependence on the volatility structure of each time series. This new model implies adding only two parameters with respect to the original DCC model. Its performance is evaluated in terms of out-of-sample forecasts with respect to the DCC models and other multivariate GARCH models. The results on four data sets seem to favor the new model.

Item Type:Working Paper
ID Code:5333
Status:Published
Uncontrolled Keywords:Dynamic conditional correlation, GARCH distance, multivariate GARCH, out-of-sample forecasts
Subjects:Area 13 - Scienze economiche e statistiche > SECS-S/01 Statistica
Divisions:002 Altri enti e centri di ricerca del Nord Sardegna > CRENoS-Centro Ricerche Economiche Nord Sud, Università di Cagliari e Università di Sassari
001 Università di Sassari > 01 Dipartimenti > Economia, impresa, regolamentazione
Publisher:CRENoS-CUEC
Copyright Holders:© CUEC 2009
ISBN:978-88-84-67-541-5
Publisher Policy:Depositato per gentile concessione del CRENoS e di CUEC
Deposited On:23 Feb 2011 10:29

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