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.
|Number of pages||23|
|Journal||Communications in Statistics: Simulation and Computation|
|Publication status||Published - 1 Jan 2013|
- Extreme value theory
- Nonparametric estimation
- Tail dependence
- Volatility indices