Modelling, Estimation and Visualization of Multivariate Dependence for High-frequency Data

Erik Brodin () and Claudia Klüppelberg ()
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Erik Brodin: Chalmers University of Technology, Department of Mathematical Sciences
Claudia Klüppelberg: Technische Universitöat München, Center for Mathematical Sciences

A chapter in Statistical Modelling and Regression Structures, 2010, pp 267-300 from Springer

Abstract: 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.

Keywords: Risk management; extreme risk assessment; high-frequency data; multivariate extreme value statistics; multivariate models; tail dependence function (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2413-1_15

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DOI: 10.1007/978-3-7908-2413-1_15

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