Clustering of time series via non-parametric tail dependence estimation
✍ Scribed by Durante, Fabrizio; Pappadà, Roberta; Torelli, Nicola
- Book ID
- 125345576
- Publisher
- Springer-Verlag
- Year
- 2014
- Tongue
- English
- Weight
- 379 KB
- Volume
- 56
- Category
- Article
- ISSN
- 0932-5026
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**Abstract.** Dependencies between extreme events (extremal dependencies) are attracting an increasing attention in modern risk management. In practice, the concept of tail dependence represents the current standard to describe the amount of extremal dependence. In theory, multi‐variate extreme‐valu
Suppose our data {Xn} come from the model Xt = ∞ j = 0 cjZt-j, where {Zn} are i.i.d. with a symmetric distribution function which lies in the domain of normal attraction of a stable law with index ∈ (1; 2). Further we assume that cj = j d-1 L(j), where parameter d ∈ (0; 1 -1= ) and L is a normalized