This paper provides a statistical analysis of high-frequency recordings of the German share price index DAX. The data set extends from November 1988 to the end of the year 1995 and includes all minute-to-minute changes during trading hours at the Frankfurt Stock Exchange. The focus of this study is on the limiting behaviour characterising the tail regions of the empirical distribution. Application of the popular Hill estimator for the tail shape yields results very similar to those of other analyses of speculative returns. However, since the reliability of tail index estimation rests on the appropriateness of the tail regions, the question of optimally choosing the sample fraction emerges. Exploiting recent advances in extreme value theory I apply a couple of novel methods for determining the optimal cut-off value for the tail of the empirical distribution. As it turns out, most algorithms suggest that one has to go out quite far into the tails for estimation of the extremal index. The findings obtained at the highest frequency (minute-to-minute returns) are confirmed when considering data at various levels of time-aggregation. A test for stability of extreme value behaviour over time gives no clear indication of changes of the limiting distribution. It is also illustrated how the approximation of the tails can be used to estimate the likelihood of large returns.
[1]
B. M. Hill,et al.
A Simple General Approach to Inference About the Tail of a Distribution
,
1975
.
[2]
Peter Hall,et al.
Using the bootstrap to estimate mean squared error and select smoothing parameter in nonparametric problems
,
1990
.
[3]
J. Teugels,et al.
Tail Index Estimation, Pareto Quantile Plots, and Regression Diagnostics
,
1996
.
[4]
F. Longin.
The Asymptotic Distribution of Extreme Stock Market Returns
,
1996
.
[5]
Thomas Lux,et al.
The stable Paretian hypothesis and the frequency of large returns: an examination of major German stocks
,
1996
.
[6]
W. DuMouchel.
Estimating the Stable Index $\alpha$ in Order to Measure Tail Thickness: A Critique
,
1983
.
[7]
P. Phillips,et al.
Testing the covariance stationarity of heavy-tailed time series: An overview of the theory with applications to several financial datasets
,
1994
.
[8]
B. Mandelbrot.
The Variation of Certain Speculative Prices
,
1963
.