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**Harvard**

Brodin, E. och Klüppelberg, C. (2010) *Modelling, estimation and visualization of multivariate dependence for high-frequency data*.

** BibTeX **

@inbook{

Brodin2010,

author={Brodin, Erik and Klüppelberg, C.},

title={Modelling, estimation and visualization of multivariate dependence for high-frequency data},

booktitle={Statistical Modelling and Regression Structures: Festschrift in Honour of Ludwig Fahrmeir},

isbn={9783790824124},

pages={267-300},

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.},

year={2010},

keywords={extreme risk assessment , high-frequency data , multivariate extreme value statistics , multivariate models , Risk management , tail dependence function},

}

** RefWorks **

RT Book, Section

SR Electronic

ID 236158

A1 Brodin, Erik

A1 Klüppelberg, C.

T1 Modelling, estimation and visualization of multivariate dependence for high-frequency data

YR 2010

T2 Statistical Modelling and Regression Structures: Festschrift in Honour of Ludwig Fahrmeir

SN 9783790824124

SP 267

OP 300

AB 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.

LA eng

DO 10.1007/978-3-7908-2413-1_15

LK http://dx.doi.org/10.1007/978-3-7908-2413-1_15

OL 30