Functional large deviations for multivariate regularly varying random walks

By Henrik Hult, Filip Lindskog, Thomas Mikosch, Gennady Samorodnitsky. 2005

109 citations recorded by the source corpus.

We extend classical results by A. V. Nagaev [Izv Akad. Nauk UzSSR Ser Fiz.-Mat. Nauk 6 (1969) 17-22, Theory Probab. Appl. 14 (1969) 51-64, 193-208] on large deviations for sums of i.i.d. regularly varying random variables to partial sum processes of i.i.d. regularly varying vectors. The results are stated in terms of a heavy-tailed large deviation principle on the space of cAdlAg functions. We illustrate how these results can be applied to functionals of the partial sum process, including ruin probabilities for multivariate random walks and long strange se-ments. These results make precise the idea of heavy-tailed large deviation heuristics: in an asymptotic sense, only the largest step contributes to the extremal behavior of a multivariate random walk.

Subfields: Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/dbc5d7d80ef9103b23eb6d4caa1f74048080b5df

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

  1. Richard A. Davis
  2. Thomas Mikosch
  3. Yuwei Zhao
  4. Kam Chuen Yuen
  5. Jingcheng Liu
  6. Yiqing Chen
  7. Kaiyong Wang
  8. Kai Wang Ng

In Management Science and Operations Research

  1. Thomas Mikosch
  2. Kam Chuen Yuen
  3. Yiqing Chen
  4. Kaiyong Wang
  5. Richard A. Davis
  6. Kai Wang Ng
  7. Jingcheng Liu
  8. Yang Yang

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