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This article develops multivariate factor models for forecasting volatility in Australian stocks. We suggest estimation procedures for approximate factor models that are robust to jumps when the cross-sectional dimension is not very large, and also work with volatility measures that have been constructed so that they contain no jump components. Out-of-sample forecast analysis shows that multivariate factor models of volatility outperform univariate models, but there is little difference between simple and sophisticated factor models.
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Anderson et al. (2006) studied this question.
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