  This article proposes a space-efficient approximation to empirical tail
dependence coefficients of an indefinite bivariate stream of data. The
approximation, which has stream-length invariant error bounds, utilises recent
work on the development of a summary for bivariate empirical copula functions.
The work in this paper accurately approximates a bivariate empirical copula in
the tails of each marginal distribution, therefore modelling the tail
dependence between the two variables observed in the data stream. Copulas
evaluated at these marginal tails can be used to estimate the tail dependence
coefficients. Modifications to the space-efficient bivariate copula
approximation, presented in this paper, allow the error of approximations to
the tail dependence coefficients to remain stream-length invariant. Theoretical
and numerical evidence of this, including a case-study using the Los Alamos
National Laboratory netflow data-set, is provided within this article.
