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Nonparametric recursive estimation of the copula

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  • Camirand Lemyre, Felix
  • Decrouez, Geoffrey

Abstract

This paper introduces two nonparametric recursive estimators of the copula. These estimators employ a recursive estimation of the quantile achieved using a stochastic approximation algorithm. Their asymptotic properties and numerical performance are investigated in the context of i.i.d. data.

Suggested Citation

  • Camirand Lemyre, Felix & Decrouez, Geoffrey, 2021. "Nonparametric recursive estimation of the copula," Statistics & Probability Letters, Elsevier, vol. 168(C).
    • Handle: RePEc:eee:stapro:v:168:y:2021:i:c:s0167715220302327
    DOI: 10.1016/j.spl.2020.108929
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    References listed on IDEAS

    as
    1. Amiri, Aboubacar & Thiam, Baba, 2014. "A smoothing stochastic algorithm for quantile estimation," Statistics & Probability Letters, Elsevier, vol. 93(C), pages 116-125.
    2. Harvey, Andrew, 2010. "Tracking a changing copula," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 485-500, June.
    3. Segers, Johan, 2012. "Asymptotics of empirical copula processes under non-restrictive smoothness assumptions," LIDAM Reprints ISBA 2012009, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
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