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Forecasting Equicorrelation

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Author Info

  • Adam E Clements

    ()
    (QUT)

  • Christopher A Coleman-Fenn

    ()
    (QUT)

  • Daniel R Smith

    ()
    (QUT)

Abstract

We study the out-of-sample forecasting performance of several time-series models of equicorrelation, which is the average pairwise correlation between a number of assets. Building on the existing Dynamic Conditional Correlation and Linear Dynamic Equicorrelation models, we propose a model that uses proxies for equicorrelation based on high-frequency intraday data, and the level of equicorrelation implied by options prices. Using state-of-the-art statistical evaluation technology, we find that the use of both realized and implied equicorrelations outperform models that use daily data alone. However, the out-of-sample forecasting benefits of implied equicorrelation disappear when used in conjunction with the realized measures.

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File URL: http://www.ncer.edu.au/papers/documents/WP72Rcover.pdf
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Bibliographic Info

Paper provided by National Centre for Econometric Research in its series NCER Working Paper Series with number 72.

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Length: 38 pages
Date of creation: 01 Apr 2011
Date of revision: 29 Aug 2011
Handle: RePEc:qut:auncer:2011_3

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Web page: http://www.ncer.edu.au
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Related research

Keywords: Equicorrelation; Implied Correlation; Multivariate GARCH; DCC;

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References

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  1. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
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Cited by:
  1. Yuta Kurose & Yasuhiro Omori, 2013. "Dynamic Equicorrelation Stochastic Volatility," CIRJE F-Series CIRJE-F-907, CIRJE, Faculty of Economics, University of Tokyo.

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