Nonparametric tail copula estimation

An application to stock and volatility index returns

Yuri Salazar, Wing Lon Ng*

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this study, we measure asymmetric negative tail dependence and discuss their statistical properties. In a simulation study, we show the reliability of nonparametric estimators of tail copula to measure not only the common positive lower and upper tail dependence, but also the negative lower-upper and upper-lower tail dependence. The use of this new framework is illustrated in an application to financial data. We detect the existence of asymmetric negative tail dependence between stock and volatility indices. Many common parametric copula models used in finance fail to capture this characteristic.

Original languageEnglish
Pages (from-to)613-635
Number of pages23
JournalCommunications in Statistics: Simulation and Computation
Volume42
Issue number3
DOIs
Publication statusPublished - 1 Jan 2013

Keywords

  • Copula
  • Extreme value theory
  • Nonparametric estimation
  • Stock
  • Tail dependence
  • Volatility indices

Fingerprint Dive into the research topics of 'Nonparametric tail copula estimation: An application to stock and volatility index returns'. Together they form a unique fingerprint.

Cite this