On the robustness of the principal volatility components

In this paper, we analyse the recent principal volatility components analysis procedure. The procedure overcomes several difficulties in modelling and forecasting the conditional covariance matrix in large dimensions arising from the curse of dimensionality. We show that outliers have a devastating...

Full description

Saved in:
Bibliographic Details
Published in:Journal of empirical finance 2019-06, Vol.52, p.201-219
Main Authors: Trucíos, Carlos, Hotta, Luiz K., Valls Pereira, Pedro L.
Format: Article
Language:English
Subjects:
Conditional covariance matrix
Constant volatility
Curse of dimensionality
Jumps
Outliers
Principal components
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, we analyse the recent principal volatility components analysis procedure. The procedure overcomes several difficulties in modelling and forecasting the conditional covariance matrix in large dimensions arising from the curse of dimensionality. We show that outliers have a devastating effect on the construction of the principal volatility components and on the forecast of the conditional covariance matrix and consequently in economic and financial applications based on this forecast. We propose a robust procedure and analyse its finite sample properties by means of Monte Carlo experiments and also illustrate it using empirical data. The robust procedure outperforms the classical method in simulated and empirical data. •Outliers affect drastically the principal volatility components procedure.•A robust procedure with good finite sample properties is proposed.•The new procedure is useful in small, moderate and high dimensional data.
ISSN:0927-5398
1879-1727
DOI:10.1016/j.jempfin.2019.03.006