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Estimating volatility on overlapping returns when returns are autocorrelated

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  • Roy Kluitman
  • Philip Hans Franses

Abstract

Overlapping financial returns are sometimes used to increase the efficiency and power of statistical tests and for Value-at-Risk analysis. This is particularly useful when there are not many observations, such as daily returns for emerging markets. Sometimes, returns show autocorrelation. In this paper, unbiased variance estimators are derived for overlapping returns when the returns are generated by AR(1) or MA(1) processes. A limited Monte Carlo experiment reveals that alternative estimators can suffer from substantial bias. The relevance of using proper estimators is emphasized by considering daily returns for six emerging markets.

Suggested Citation

  • Roy Kluitman & Philip Hans Franses, 2002. "Estimating volatility on overlapping returns when returns are autocorrelated," Applied Mathematical Finance, Taylor & Francis Journals, vol. 9(3), pages 179-188.
    • Handle: RePEc:taf:apmtfi:v:9:y:2002:i:3:p:179-188
    DOI: 10.1080/13504860210162029
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    References listed on IDEAS

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    1. Pauline Bod & David Blitz & Philip Hans Franses & Roy Kluitman, 2002. "An unbiased variance estimator for overlapping returns," Applied Financial Economics, Taylor & Francis Journals, vol. 12(3), pages 155-158.
    2. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
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    Cited by:

    1. Stephen Taylor & Ming Fang, 2018. "Unbiased weighted variance and skewness estimators for overlapping returns," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 154(1), pages 1-8, December.

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