A vector heterogeneous autoregressive index model for realized volatility measures

Citation data:

International Journal of Forecasting, ISSN: 0169-2070, Vol: 33, Issue: 2, Page: 337-344

Publication Year:
2017
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Repository URL:
http://hdl.handle.net/2108/175470; http://www.sciencedirect.com/science/article/pii/S0169207016301005
DOI:
10.1016/j.ijforecast.2016.09.002
Author(s):
Gianluca Cubadda; Barbara Guardabascio; Alain Hecq
Publisher(s):
Elsevier BV; NL
Tags:
Business, Management and Accounting; Combinations of realized volatilities; Common volatility; Forecasting; HAR models; Index models; Settore SECS-S/03 - Statistica Economica; Combinations of realized volatilities; Common volatility; Forecasting; HAR models; Index models
article description
This paper introduces a new model for detecting the presence of commonalities in a set of realized volatility measures. In particular, we propose a multivariate generalization of the heterogeneous autoregressive model (HAR) that is endowed with a common index structure. The vector heterogeneous autoregressive index model has the property of generating a common index that preserves the same temporal cascade structure as in the HAR model, a feature that is not shared by other aggregation methods (e.g., principal components). The parameters of this model can be estimated easily by a proper switching algorithm that increases the Gaussian likelihood at each step. We illustrate our approach using an empirical analysis that aims to combine several realized volatility measures of the same equity index for three different markets.