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Neglecting structural breaks when estimating and valuing dynamic correlations for asset allocation

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  • Andreea G. Halunga
  • Christos S. Savva

Abstract

This paper assesses the econometric and economic value consequences of neglecting structural breaks in dynamic correlation models and in the context of asset allocation framework. It is shown that changes in the parameters of the conditional correlation process can lead to biased estimates of persistence. Monte Carlo simulations reveal that short-run persistence is downward biased while long-run persistence is severely upward biased, leading to spurious high persistence of shocks to conditional correlation. An application to stock returns supports these results and concludes that neglecting such structural shifts could lead to misleading decisions on portfolio diversification, hedging, and risk management.

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  • Andreea G. Halunga & Christos S. Savva, 2019. "Neglecting structural breaks when estimating and valuing dynamic correlations for asset allocation," Econometric Reviews, Taylor & Francis Journals, vol. 38(6), pages 660-678, July.
    • Handle: RePEc:taf:emetrv:v:38:y:2019:i:6:p:660-678
    DOI: 10.1080/07474938.2017.1411431
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    Cited by:

    1. Azra Zaimovic & Adna Omanovic & Almira Arnaut-Berilo, 2021. "How Many Stocks Are Sufficient for Equity Portfolio Diversification? A Review of the Literature," JRFM, MDPI, vol. 14(11), pages 1-30, November.

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