Forecasting temporal dependence in second order moments of returns is a relevant problem in many contexts of financial econometrics. It is commonly accepted that financial volatilities move together over time across assets and markets. For this reason in the paper we propose an approach based on the analysis of independent temporal components to model the multivariate volatility. We have assumed that the underlying factors or sources of the model are AR-APARCH processes with errors interpreted by the Meixner distribution. An application with two sets of real data shows the use of the model in the analysis of parallel financial series.
Modelling multivariate volatility processes using temporal independent component analysis
PROVASI, CORRADO
2007
Abstract
Forecasting temporal dependence in second order moments of returns is a relevant problem in many contexts of financial econometrics. It is commonly accepted that financial volatilities move together over time across assets and markets. For this reason in the paper we propose an approach based on the analysis of independent temporal components to model the multivariate volatility. We have assumed that the underlying factors or sources of the model are AR-APARCH processes with errors interpreted by the Meixner distribution. An application with two sets of real data shows the use of the model in the analysis of parallel financial series.File in questo prodotto:
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