Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/56698
Authors: 
Herrera, Rodrigo
Schipp, Bernhard
Year of Publication: 
2011
Series/Report no.: 
SFB 649 discussion paper 2011-022
Abstract: 
The analysis of return series from financial markets is often based on the Peaks-over-threshold (POT) model. This model assumes independent and identically distributed observations and therefore a Poisson process is used to characterize the occurrence of extreme events. However, stylized facts such as clustered extremes and serial dependence typically violate the assumption of independence. In this paper we concentrate on an alternative approach to overcome these difficulties. We consider the stochastic intensity of the point process of exceedances over a threshold in the framework of irregularly spaced data. The main idea is to model the time between exceedances through an Autoregressive Conditional Duration (ACD) model, while the marks are still being modelled by generalized Pareto distributions. The main advantage of this approach is its capability to capture the short-term behaviour of extremes without involving an arbitrary stochastic volatility model or a prefiltration of the data, which certainly impacts the estimation. We make use of the proposed model to obtain an improved estimate for the Value at Risk. The model is then applied and illustrated to transactions data from Bayer AG, a blue chip stock from the German stock market index DAX.
Subjects: 
extreme value theory
autoregressive conditional duration
value at risk
self-exciting point process
conditional intensity
JEL: 
C22
C58
F30
Document Type: 
Working Paper

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