A General Multivariate Threshold GARCH Model With Dynamic Conditional Correlations
AbstractWe propose a new multivariate GARCH model with Dynamic Conditional Correlations that extends previous models by admitting multivariate thresholds in conditional volatilities and correlations. The model estimation is feasible in large dimensions and the positive deniteness of the conditional covariance matrix is easily ensured by the structure of the model. Thresholds in conditional volatilities and correlations are estimated from the data, together with all other model parameters. We study the performance of our model in three distinct applications to US stock and bond market data. Even if the conditional volatility functions of stock returns exhibit pronounced GARCH and threshold features, their conditional correlation dynamics depends on a very simple threshold structure with no local GARCH features. We obtain a similar result for the conditional correlations between government and corporate bond returns. On the contrary, we Â¯nd both threshold and GARCH structures in the conditional correlations between stock and government bond returns. In all applications, our model improves signiÂ¯cantly the in-sample and out-of-sample forecasting power for future conditional correlations with respect to other relevant multivariate GARCH models.
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Bibliographic InfoArticle provided by American Statistical Association in its journal Journal of Business and Economic Statistics.
Volume (Year): 29 (2011)
Issue (Month): 1 ()
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Other versions of this item:
- Francesco Audrino & Fabio Trojani, 2007. "A general multivariate threshold GARCH model with dynamic conditional correlations," University of St. Gallen Department of Economics working paper series 2007 2007-25, Department of Economics, University of St. Gallen.
- Fabio Trojani & Francesco Audrino, 2005. "A general multivariate threshold GARCH model with dynamic conditional correlations," University of St. Gallen Department of Economics working paper series 2005 2005-04, Department of Economics, University of St. Gallen.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
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- Jeroen V.K. Rombouts & Lars Stentoft & Francesco Violante, 2012.
"The Value of Multivariate Model Sophistication: An Application to pricing Dow Jones Industrial Average options,"
CREATES Research Papers
2012-04, School of Economics and Management, University of Aarhus.
- ROMBOUTS, Jeroen V. K. & STENTOFT, Lars & VIOLANTE, Francesco, 2012. "The value of multivariate model sophistication: an application to pricing Dow Jones Industrial Average options," CORE Discussion Papers 2012003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Jeroen Rombouts & Lars Peter Stentoft & Francesco Violente, 2012. "The Value of Multivariate Model Sophistication: An Application to pricing Dow Jones Industrial Average Options," CIRANO Working Papers 2012s-05, CIRANO.
- Audrino, Francesco, 2011. "Forecasting correlations during the late-2000s financial crisis: short-run component, long-run component, and structural breaks," Economics Working Paper Series 1112, University of St. Gallen, School of Economics and Political Science.
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