An Out-of-sample Analysis of Mean-Variance Portfolios with Orthogonal GARCH Factors
In this paper a comparative study is conducted to evaluate the out-of-sample performance of mean-variance portfolios when three different variance models are considered. We use the common framework of orthogonal factors to specify the conditional covariance matrix structure. A key advantage of th...
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| Format: | Article |
| Language: | English |
| Published: |
Econometric Research Association
2012-04-01
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| Series: | International Econometric Review |
| Subjects: | |
| Online Access: | http://www.era.org.tr/makaleler/14120057.pdf |
| Summary: | In this paper a comparative study is conducted to evaluate the out-of-sample performance
of mean-variance portfolios when three different variance models are considered. We use
the common framework of orthogonal factors to specify the conditional covariance matrix
structure. A key advantage of this approach is that the estimated factors can be modeled
as univariate GARCH processes so that we can consider models for which multivariate
extensions are not available. We, therefore, compared the Integrated GARCH (IGARCH)
with the Exponential GARCH (EGARCH) and Fractionally Integrated Exponential
GARCH (FIEGARCH) factor models on the basis of statistical diagnostics, and found the
EGARCH model superior when fitted with heavy tailed distributions. We also evaluated
out-of sample portfolio performances in terms of efficient frontiers, prediction intervals
and turnover, and concluded that the EGARCH and FIEGARCH models provide
comparable outcomes which are overall superior to the IGARCH performance. Looking
jointly at statistical and economic criterions we conclude that fitting a FIEGARCH model
with heavy tailed distributions can generally improve out-of-sample portfolio
performances. |
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| ISSN: | 1308-8793 1308-8815 |