This paper compares the forecasting performances of both univariate and multivariate models for realized volatilities series. We consider realized volatility measures of the returns of 13 major banks traded in the NYSE. Since our variables are characterized by the presence of long range dependence, we use several modelling approaches that are able to capture such feature. We look at the forecasting accuracy of the considered models to make inference on the underlying mechanism that has generated volatilities of the assets. Our main conclusion is that the contagion effect among the considered volatilities is small or, at least, not well captured by the considered multivariate models.
Cubadda, G., Hecq, A., Riccardo, A. (2019). Forecasting realized volatility measures with multivariate and univariate models. In S.G. Julien Chevallier (a cura di), Financial Mathematics, Volatility and Covariance Modelling, Volume 2 (pp. 286-307). Taylor & Francis [10.4324/9781315162737-12].
Forecasting realized volatility measures with multivariate and univariate models
Cubadda, Gianluca;
2019-07-01
Abstract
This paper compares the forecasting performances of both univariate and multivariate models for realized volatilities series. We consider realized volatility measures of the returns of 13 major banks traded in the NYSE. Since our variables are characterized by the presence of long range dependence, we use several modelling approaches that are able to capture such feature. We look at the forecasting accuracy of the considered models to make inference on the underlying mechanism that has generated volatilities of the assets. Our main conclusion is that the contagion effect among the considered volatilities is small or, at least, not well captured by the considered multivariate models.File | Dimensione | Formato | |
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