A Vector Heterogeneous Autoregressive Index model for realized volatility measures

Gianluca Cubadda, Barbara Guardabascio and Alain Hecq

No 33, Research Memorandum from Maastricht University, Graduate School of Business and Economics (GSBE)

Abstract: This paper introduces a new modelling 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 to generate 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 easily estimated by a proper switching algorithm that increases the Gaussian likelihood at each step. We illustrate our approach with an empirical analysis aiming at combining several realized volatility measures of the same equity index for three different markets.

Date: 2015-01-01
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: A vector heterogeneous autoregressive index model for realized volatility measures (2017)
Working Paper: A Vector Heterogeneous Autoregressive Index Model for Realized Volatily Measures (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:unm:umagsb:2015033

DOI: 10.26481/umagsb.2015033

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