FORECASTING THE CONDITIONAL COVARIANCE MATRIX OF A PORTFOLIO UNDER LONG-RUN TEMPORAL DEPENDENCE

Antonio Rubia Serrano and Trino Ñíguez Grau
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Antonio Rubia Serrano: Universidad de Alicante

Working Papers. Serie AD from Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie)

Abstract: Long-range persistence in volatility is widely modelled and forecasted in terms of the so-called fractional integrated models. These models are mostly applied in the univariate framework, since the extension to the multivariate context of assets portfolios, while relevant, is not straightforward. We discuss and apply a procedure which is able to forecast the multivariate volatility of a portfolio including assets with long-memory. The main advantage of this model is that it is feasible enough to be applied on large-scale portfolios, solving the problem of dealing with extremely complex likelihood functions which typically arises in this context. An application of this procedure to a portfolio of five daily exchange rate series shows that the out-of-sample forecasts for the multivariate volatility are improved under several loss-functions when the long-range dependence property of the portfolio assets is explicitly accounted for.

Keywords: Exchange Rates; Fractional Integration; Long Memory; MGARCH models; PCA (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 F31 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2003-10
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http://www.ivie.es/downloads/docs/wpasad/wpasad-2003-34.pdf Fisrt version / Primera version, 2003 (application/pdf)

Related works:
Journal Article: Forecasting the conditional covariance matrix of a portfolio under long-run temporal dependence (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:ivi:wpasad:2003-34

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