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A Vector Heterogeneous Autoregressive Index Model for Realized Volatily Measures

Gianluca Cubadda, Barbara Guardabascio () and Alain Hecq

No 391, CEIS Research Paper from Tor Vergata University, CEIS

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 di?erent markets.

Keywords: Common volatility; HAR models; index models; combinations of realized volatil¬ities; forecasting (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2016-07-23, Revised 2016-07-23
New Economics Papers: this item is included in nep-ecm and nep-ets
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Related works:
Journal Article: A vector heterogeneous autoregressive index model for realized volatility measures (2017) Downloads
Working Paper: A Vector Heterogeneous Autoregressive Index model for realized volatility measures (2015) Downloads
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