Modelling, estimation and visualization of multivariate dependence for high-frequency data

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

8 Scopus citations

Abstract

Dependence modelling and estimation is a key issue in the assessment of financial risk. It is common knowledge meanwhile that the multivariate normal model with linear correlation as its natural dependence measure is by no means an ideal model. We suggest a large class of models and a dependence function, which allows us to capture the complete extreme dependence structure of a portfolio. We also present a simple nonparametric estimation procedure of this function. To show our new method at work we apply it to a financial data set of high-frequency stock data and estimate the extreme dependence in the data. Among the results in the investigation we show that the extreme dependence is the same for different time scales. This is consistent with the result on high-frequency FX data reported in Hauksson et al. (2001). Hence, the different asset classes seem to share the same time scaling for extreme dependence. This time scaling property of high-frequency data is also explained from a theoretical point of view.

Original languageEnglish
Title of host publicationStatistical Modelling and Regression Structures
Subtitle of host publicationFestschrift in Honour of Ludwig Fahrmeir
PublisherPhysica-Verlag HD
Pages267-300
Number of pages34
ISBN (Print)9783790824124
DOIs
StatePublished - 2010
Externally publishedYes

Keywords

  • Risk management
  • extreme risk assessment
  • high-frequency data
  • multivariate extreme value statistics
  • multivariate models
  • tail dependence function

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