Nonparametric estimation of the tail-dependence coefficient
A common measure of tail dependence is the so-called tail-dependence coefficient. We present a nonparametric estimator of the tail-dependence coefficient and prove its strong consistency and asymptotic normality in the case of known marginal distribution functions. The finite-sample behavior as well...
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| Format: | article |
| Language: | eng |
| Published: |
2013
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| Online Access: | http://hdl.handle.net/1822/27448 |
| Country: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/27448 |
| Summary: | A common measure of tail dependence is the so-called tail-dependence coefficient. We present a nonparametric estimator of the tail-dependence coefficient and prove its strong consistency and asymptotic normality in the case of known marginal distribution functions. The finite-sample behavior as well as robustness will be assessed through simulation. Although it has a good performance, it is sensitive to the extreme value dependence assumption. We shall see that a block maxima procedure might improve the estimation. This will be illustrated through simulation. An application to financial data shall be presented at the end. |
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