 Long memory conditional volatility and asset allocationRichard Harris and Anh Nguyen International Journal of Forecasting, 2013, vol. 29, issue 2, 258-273 Abstract: In this paper, we evaluate the economic benefits that arise from allowing for long memory when forecasting the covariance matrix of returns over both short and long horizons, using the asset allocation framework of Engle and Colacito (2006) In particular, we compare the statistical and economic performances of four multivariate long memory volatility models (the long memory EWMA, long memory EWMA–DCC, FIGARCH-DCC and component GARCH-DCC models) with those of two short memory models (the short memory EWMA and GARCH-DCC models). We report two main findings. First, for longer horizon forecasts, long memory models generally produce forecasts of the covariance matrix that are statistically more accurate and informative, and economically more useful than those produced by short memory models. Second, the two parsimonious long memory EWMA models outperform the other models–both short and long memory–across most forecast horizons. These results apply to both low and high dimensional covariance matrices and both low and high correlation assets, and are robust to the choice of the estimation window. Keywords: Conditional variance-covariance matrix; Long memory; Asset allocation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:29:y:2013:i:2:p:258-273 DOI: 10.1016/j.ijforecast.2012.09.003 Access Statistics for this article International Journal of Forecasting is currently edited by R. J. Hyndman More articles in International Journal of Forecasting from Elsevier |
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