Dynamic factor long memory volatility

Richard D. F. Harris, Anh T. H. Nguyen

    Research output: Contribution to journalArticle (Academic Journal)peer-review

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    Abstract

    In this paper, we develop a long memory orthogonal factor (LMOF) multivariate volatility model for forecasting the covariance matrix of financial asset returns. We evaluate the LMOF model using the volatility timing framework of Fleming et al. [J. Finance, 2001, 56, 329–352] and compare its performance with that of both a static investment strategy based on the unconditional covariance matrix and a range of dynamic investment strategies based on existing short memory and long memory multivariate conditional volatility models. We show that investors should be willing to pay to switch from the static strategy to a dynamic volatility timing strategy and that, among the dynamic strategies, the LMOF model consistently produces forecasts of the covariance matrix that are economically more useful than those produced by the other multivariate conditional volatility models, both short memory and long memory. Moreover, we show that combining long memory volatility with the factor structure yields better results than employing either long memory volatility or the factor structure alone. The factor structure also significantly reduces transaction costs, thus increasing the feasibility of dynamic volatility timing strategies in practice. Our results are robust to estimation error in expected returns, the choice of risk aversion coefficient, the estimation window length and sub-period analysis.
    Original languageEnglish
    Pages (from-to)1205-1221
    Number of pages17
    JournalQuantitative Finance
    Volume17
    Issue number8
    Early online date1 Feb 2017
    DOIs
    Publication statusE-pub ahead of print - 1 Feb 2017

    Keywords

    • finance
    • conditional variance-covariance matrix
    • long memory
    • factor models
    • volatility timing

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