Analysis of elliptical copula correlation factor model with Kendall's tau

By Marten H. Wegkamp, Yue Zhao. 2013

44 citations recorded by the source corpus.

We study the adaptive estimation of copula correlation matrix $\Sigma$ for the semi-parametric elliptical copula model. In this context, the correlations are connected to Kendall's tau through a sine function transformation. Hence, a natural estimate for $\Sigma$ is the plug-in estimator $\hat{\Sigma}$ with Kendall's tau statistic. We first obtain a sharp bound on the operator norm of $\hat{\Sigma}-\Sigma$. Then we study a factor model of $\Sigma$, for which we propose a refined estimator $\widetilde{\Sigma}$ by fitting a low-rank matrix plus a diagonal matrix to $\hat{\Sigma}$ using least squares with a nuclear norm penalty on the low-rank matrix. The bound on the operator norm of $\hat{\Sigma}-\Sigma$ serves to scale the penalty term, and we obtain finite sample oracle inequalities for $\widetilde{\Sigma}$. We also consider an elementary factor copula model of $\Sigma$, for which we propose closed-form estimators. All of our estimation procedures are entirely data-driven.

Subfields: Finance, Statistics and Probability

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In Finance

  1. Jan Peters
  2. Peter Buhlmann
  3. Bernhard Schölkopf
  4. Zhao Ren
  5. Niklas Pfister
  6. Marcelo C. Medeiros
  7. Timo Teräsvirta

In Statistics and Probability

  1. Jan Peters
  2. Bernhard Schölkopf
  3. Peter Buhlmann
  4. Niklas Pfister
  5. Zhao Ren
  6. Dominik Janzing
  7. Chao Gao
  8. David Lopez-Paz

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